Shape memory effects in self-healing polymers

Journal Pre-proof Shape Memory Effects in Self-Healing Polymers Chris C. Hornat, Marek W. Urban (Conceptualization) (Writing review and editing)

PII:

S0079-6700(20)30001-0

DOI:

https://doi.org/10.1016/j.progpolymsci.2020.101208

Reference:

JPPS 101208

To appear in:

Progress in Polymer Science

Accepted Date:

7 January 2020

Please cite this article as: Hornat CC, Urban MW, Shape Memory Effects in Self-Healing Polymers, Progress in Polymer Science (2020), doi: https://doi.org/10.1016/j.progpolymsci.2020.101208

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.

Shape Memory Effects in Self-Healing Polymers Chris C. Hornat, Marek W. Urban* [email protected]

Department of Materials Science and Engineering, Center for Optical Materials Science and Engineering Technologies (COMSET), Clemson University, Clemson, SC 29634, USA Author

ro

of

*Corresponding

Jo

ur na

lP

re

-p

Graphical Abstract

Abstract

Recent developments in self-healing polymers (SHPs) have been fueled by the increasing

need for sustainable materials with extended life-spans and functionality. This review focuses on the shape memory effect (SME) in polymers and its contribution to self-healing. Starting from structural requirements and thermodynamics, quantitative aspects of the SME are discussed in

the context of energy storage and release during the damage-repair cycle. Characterization of shape memory in polymers has largely concentrated on recovery and fixation ratios, which describe the efficiency of the geometrical changes. In this review, factors that govern strain, stress, and energy storage capacities are also explored. Of particular interest for self-healing are deformability and conformational entropic energy storage and release efficiency during reversible plasticity shape memory (RPSM) cycles. Physical and chemical mechanisms of

of

strength regain following shape recovery as well as other physical factors that influence the self-

ro

healing process are also discussed.

atomic force microscopy characteristic ratio dynamic mechanical analysis rubbery modulus elastic modulus shape memory fill factor interpenetrating network molecular weight between junctions (physical/chemical crosslinks and/or entanglements) Mn number average molecular weight PCL polycaprolactone PUR polyurethane R gas constant Rf fixation ratio Rr recovery ratio RPSM reversible plasticity shape memory ΔS change in conformational entropic energy upon deformation ΔSS VLT stored conformational entropic energy density during DMA experiment SHP self-healing polymer SMC shape memory cycle SME shape memory effect SMP shape memory polymer Tdef deformation temperature Tfix shape fixation temperature Tg glass transition temperature TLC liquid crystal transition temperature Tm crystalline melt temperature Trec shape recovery temperature TME temperature memory effect

Jo

ur na

lP

re

-p

AFM 𝐶∞ DMA ER E fsm IPN Mj

2

viscoelastic shape memory viscoelastic length transition during DMA experiment fixed strain during a SMC maximum strain during a SMC VLT maximum strain during DMA experiment residual strain following recovery during a SMC applied deforming stress during a SMC retractive stress due to conformational entropy VLT stress at εmax during a DMA experiment entanglement density junction/net-point density (physical/chemical crosslinks and/or entanglements) viscosity

of

VESM VLT εf εm εmax εr σdef σR σSF νe νj η

-p

ro

Key Words: shape memory, self-healing, stimuli-responsive polymers, reversible plasticity, viscoelasticity, deformability

re

1. Introduction

lP

Due to their unique dynamic properties, stimuli-responsive materials will play an integral role in future technological innovation. The ability to sense and react to external or internal stimuli, such as mechanical stress, temperature, pH, electric and magnetic fields, or biological

ur na

environment enable these materials to change color, size, shape, conductivity, permeability, or other properties [1, 2], which will help tackle forthcoming challenges in new and exciting ways that traditional materials simply cannot. Inspired by biological systems, advances in self-healing

Jo

polymers (SHPs) in particular have been pushed not only by their scientific curiosity, but also by the ever-growing need for sustainable materials with longer life spans [3]. To confront this issue, in addition to intricately designed and highly engineered materials capable of self-repair, commodity polymers that can heal without intervention under ordinary use conditions will be required.

3

Recent approaches to self-healing have largely focused on physical or chemical integration of distinct repairing components. These concepts can generally be grouped into the following schemes: (1) embedment of vessels encapsulating reactive liquids that rupture upon damage, releasing the enclosed healing agents to mend the wounded areas [4-12]; (2) chemical integration of dynamic reformable/reversible bonds [13-55]: (3) dispersion of superparamagnetic or other nanomaterials that remotely respond to electric/magnetic fields or electromagnetic

of

radiation to heat the material and enable flow/diffusion [56, 57]; and in contrast (4) self-repair

ro

without addition of any discrete physical/chemical healing constituents, driven by interchain van der Waals (vdW) interactions which form interlocking “key-and-lock” junctions in narrow

-p

composition ranges [58]. In addition to electric/magnetic fields, healing may be initiated by heat

re

[14, 15, 17, 18, 23, 28, 29, 32, 38, 39, 42, 44, 46, 47, 49, 51, 55], light [16, 22, 24, 27, 30, 34, 35, 40], pH changes [19, 25], redox [21, 26], mechanical compression [53], or moisture [36, 44, 48,

41, 48, 50, 58].

lP

49, 54], while select materials can self-repair autonomously under ambient conditions [4-12, 31,

ur na

In particular, significant strides have been made in developing dynamic reformable/reversible covalent and non-covalent chemistries for self-healing [59-64]. Types of dynamic covalent bonds which have been employed in SHPs can be generally classified as either (i) reversible cycloaddition reactions (Diels-Adler [14, 38, 65], anthracene derivatives [27],

Jo

courmarin [30]), (ii) exchange reactions (disulfide [21, 23, 35], diselenide [40], or siloxane [32] exchange, transesterification [51, 55], transamination [19, 43], transcarbamoylation [47] and dynamic reversible urea bonds [37, 46, 48]), (iii) stable free radical mediated reshuffling reactions (thiol/disulfide [22, 34], alkoxyamine [28, 42] or arylbenzofuranone [31] chemistries), and (iv) heterocyclic compound/carbohydrate facilitation of bond reformation [16, 20, 36, 45]. 4

Supramolecular chemistries including H-bonding [13, 15, 18, 29, 33, 50, 53, 54], π- π stacking [17, 18], host-guest interactions [26], and metal-ligand coordination [24, 25, 41, 50] have also been utilized to create SHPs. However, difficulties remain in creating mechanically robust materials with high strength and stiffness without requiring application of high temperatures for repair, particularly while also

of

attempting to achieve efficient and repeatable recovery of multiple forms and sizes of damage using scalable and affordable chemistries. One approach to combat several of these obstacles has

ro

been utilization of shape memory effects (SMEs) to facilitate repair [35, 36, 45, 51, 52, 65-72].

-p

Conformational entropic energy generated from damage acts to close the physical wound, allowing any ensuing chemical/physical processes essential for strength recovery to follow. This

re

enables healing of larger wounds than could otherwise be achieved without intervention. In fact, mechanisms similar to shape memory facilitated self-repair are found in nature. For example,

lP

healing of Delosperma cooperi leaves is driven by internal pre-stresses as well as hydraulic shrinking and swelling, which cause the plant bend to close and seal the wound [73]. Activation

ur na

of SME-type recovery in synthetic polymers, however, has typically necessitated application of an external stimulus, most commonly thermal energy. Taking advantage of the continuous nature and viscoelastic characteristics of the glass transition in polymers may potentially allow materials to be made with sufficient molecular mobility for gradual autonomous wound closure

Jo

and repair under ambient conditions while maintaining high strength and stiffness. Differing from classical shape memory, which involves sustained fixation of deformation until prompted by a stimulus [74], such spontaneous viscoelastic shape memory (VESM) behavior would be highly advantageous for self-healing. Yet despite of all the progress in self-healing chemistries,

5

the relationships between physical material properties and shape memory assisted repair have not been explored. The unique shape changing properties of shape memory polymers (SMPs) make them attractive for use in a wide range of applications beyond self-healing as well, such as for selftightening wound sutures [75], self-expanding stents [76], complex 4D printed structures and

of

devices [77, 78], pumps and valves for microfluidics [79], smart textiles with temperature dependent moisture permeability [80], reversible dry adhesives [81, 82], self-deployable

ro

structures for aerospace [83], and soft robots [84]. Even though for many of the above

-p

applications the magnitude of the shape changes and potential work/energy output are more important factors in material selection, characterization of SMPs has mostly centered on the

re

efficiency of the geometrical changes. Without practical tools to assess and/or predict shape memory storage capacities, selection of optimal/suitable materials for specific applications, each

lP

with unique sets of requirements, is often troublesome and time intensive. While scientific interests in SMPs continue, the effect was actually first noted in the 1940’s and referred to as

ur na

elastic memory [85], although the potential implications were not understood until much later with the advent of heat shrinking tubes and films [86]. 2. SMP Structural Requirements and Molecular Mechanisms

Jo

Shape memory is not unique to a specific polymer chemistry or microstructure; it is a consequence of molecular architecture, reversible mobility changes, conformational entropy, and programming [87]. For a polymer to be capable of shape memory, two structural elements are required (Fig. 1): (A) permanent net-points/junctions and (B) reversible molecular switching segments [74, 87, 88]. The permanent net-points/junctions create a three-dimensional network architecture that allows for storage/memory of the permanent shape by preventing chain 6

slippage/flow/creep upon deformation. Instead, deformation results in chain conformation changes and displacement of net-points/junctions. Chemical crosslinks, crystalline or other secondary phases, macromolecular entanglements [89], or interpenetrating networks can serve as junction-points in SMPs [90, 91]. Meanwhile, the reversible molecular switching segments act to fix or release the temporary shape when triggered by the stimulus through changes in molecular mobility upon formation/dissolution of reversible interactions [90, 92, 93]. This can be

of

accomplished utilizing the glass transition temperature (Tg), crystallization/melting transition

ro

(Tm), anisotropic/isotropic liquid crystal transition (TLC) [94, 95], reversible molecular

crosslinking [96, 97], or supramolecular association/disassociation [98, 99]. Note that for highly

-p

efficient shape fixing to be possible, it is necessary for the molecular switches to permeate

re

throughout the material. Since the majority of polymers can meet these requirements under certain conditions, there is the potential for many—if not most—polymeric materials to exhibit

lP

the SME [100]. Exceptions include low-molecular weight amorphous thermoplastics and

ur na

polymers with Tg or Tm above the decomposition temperature.

While SMPs capable of responding to stimuli such as light [96, 98, 101] and chemical redox [97] have been developed, temperature is the most commonly used trigger to activate SMEs in polymers. Indirect methods of thermal activation have been explored as well, for

Jo

example through application of electric current (Joule/resistive heating) [102, 103], alternating magnetic fields (inductive heating) [104-106], high intensity ultrasound [107], infrared (IR) radiation (photothermal effect) [108, 109], and exposure to water [110-112] or other solvents [113] (plasticization). Thermal shape memory is caused by a transition of a polymer from a state dominated by internal energy and limited chain mobility (glassy or semi-crystalline state), to a

7

state dominated by conformational entropy (rubber state) as temperature is increased [87]. A conventional shape memory cycle (SMC) is illustrated in Fig. 2A, and the corresponding molecular level events are depicted by the schematics in Fig. 2B. For amorphous polymer chains

above the shape memory reversible transition temperature, random coil conformations are entropically favored. If a deforming force is applied to the material in this state, the coiled

of

segments stretch, causing orientation of the chains and displacement of the net-points/junctions,

ro

decreasing conformational entropy. Once the force is released, the system will seek to recover entropy by returning to more coiled chain conformations, and the original shape will be restored.

-p

However, shape recovery can be halted by decreasing the temperature below that of the transition (Tg, Tm, TLC), thus reducing free volume and chain mobility to fix the deformation. Once the

lP

the deformation is recovered [74, 87, 90].

re

temperature is subsequently increased above the reversible thermal transition at the desired time,

Under the certain conditions, some SMPs are able to recover seemingly permanent

ur na

deformation imparted below or at the reversible transition temperature as well, termed reversible plasticity shape memory (RPSM) [69, 92], which will be detailed in Section 6. RPSM is particularly useful for repeatable self-healing of scratches/cuts and indents [35, 45, 52, 65-71], allowing storage of conformational entropic energy during damage which can then facilitate

Jo

wound closure when triggered. Conventional shape memory can be applied to aid self-healing of cracks in addition to scratches/cuts [35, 72], but is less practical because pre-programming of the appropriate shape change prior to damage is required. 3. Thermodynamics and Statistical Mechanics

8

Shape recovery in SMPs is driven by recuperation of conformational entropy following deformation, which is also responsible for rubber elasticity, and can be fundamentally described in terms of thermodynamics and statistical mechanics. Amorphous polymer chains exist in a distribution of conformations, and the most probable state for a linear molecule is a strongly coiled random conformation, which is the state of maximum entropy [74, 115-117]. In the Boltzmann relationship (Equation 1), S is the conformational entropy of a macrostate, Ω

of

expresses the total number of possible microstates (possible conformations available) that the

𝑆 = 𝑘 ln 𝛺

ro

system can adapt, and k is the Boltzmann constant. (1)

-p

The entropy difference going from a macrostate S1 to S2 is

𝛺2 𝛺1

(2)

re

𝛥𝑆 = 𝑆2 − 𝑆1 = 𝑘 ln

The change in entropy is negative if macrostate 2 has fewer possible microstates (available

lP

conformations) than macrostate 1 [115-117]. When an amorphous polymer above Tg is deformed by an external force, assuming sufficient molecular weight/crosslinking/entanglements to prevent

ur na

flow/chain slippage, the chains will stretch into more elongated conformations. This reduces the number of available chain conformations (microstates), which is energetically unfavorable. The change in entropy per volume of a network of Gaussian strands is expressed by

Jo

𝛥𝑆 = −

𝜌𝑅 2 (𝛼 + 𝛼𝑦2 + 𝛼𝑧2 − 3) 2𝑀𝑗 𝑥

(3)

Where: ρ is density of the material, R is the gas constant, Mj is the molecular weight between junctions (physical/chemical crosslinks and/or entanglements), and αx, αy, and αz are the extension ratios caused by the deformation in each of the three dimensions given by Lx2/Lx1, Ly2/Ly1, and Ly2/Ly1 respectively (original sample size with sides Lx1, Ly1, and Lz1 and deformed 9

sample size with sides Lx2, Ly2, and Lz2) [115-117]. Once the force is removed, supposing no structural changes such as strain-induced crystallization occurred, the deformation is recovered based on the spatial arrangement of the network sites, driven by regain of conformational entropy. This is referred to as the entropic behavior of elastomers [74], or rubber elasticity [115, 116]. Assuming uniaxial extension along the x direction and no volume change upon

𝜈𝑗 𝑅 2 2 (𝛼 + − 3) 2 𝛼

(4)

ro

𝛥𝑆 = −

of

deformation, αx = α and αy = αz =1/√𝛼, resulting in an entropy change per volume expressed as

Where: νj is the junction density (νj = ρ/Mj). The magnitude of the corresponding retractive

-p

(engineering) stress σR generated by the decrease in entropy at temperature T is expressed as 1 ) 𝛼2

(5)

re

𝜎𝑅 = 𝜈𝑗 𝑅𝑇 (𝛼 −

However, in a conventional shape memory cycle (SMC), the applied force is maintained while

lP

temperature is decreased to below a thermal transition (Tg, Tm), and therefore the deformation is not recovered once the force is subsequently released [74]. This is because interactions formed in

ur na

the glass/crystalline phase are too strong for the entropic force to overcome, thus trapping conformational entropic energy and internal stress in the material. Once the temperature is increased back above the thermal transition, mobility of macromolecular chains will increase

Jo

again, allowing the deformation to be recovered upon release of the stored conformational entropic energy [74]. The modulus of the rubbery network is expressed as 𝜕𝜎𝑅 = 3𝜈𝑗 𝑅𝑇 𝛼→1 𝜕𝛼

𝐸𝑅 = lim

(6)

4. Quantification of Shape Memory

10

The most commonly used technique to quantify shape memory performance has been the thermomechanical shape memory cycle (SMC) [74]. This is accomplished by an instrument capable of precisely controlling and measuring stress, strain, and temperature. In this method, illustrated in Fig. 3, the material is first programmed by heating to above the shape memory thermal transition to a temperature Tdef, and then stretched by applying a force (σdef) to achieve strain εm. The material is then cooled to Tfix below the transition while the force is maintained.

of

Once equilibrated at a temperature Tfix, the force is removed resulting in programmed/fixed

ro

strain εf. The material is then heated at a constant rate (with no applied force) and allowed to freely recover to strain εr. Through this technique, the SME has been primarily defined and

-p

quantified by two values: the strain fixity ratio (Rf), which describes the programmability of the

re

material, and strain recovery ratio (Rr), which characterizes recoverability. Rr and Rf are defined as

𝜀𝑚 − 𝜀𝑟 𝜀𝑚

(7)

𝜀𝑓 𝜀𝑚

(8)

lP

𝑅𝑟 =

ur na

𝑅𝑓 =

The ability to minimize unrecoverable deformation and achieve high Rr is imperative for efficient and effective shape memory facilitation of self-healing in particular [71], as repair processes can only proceed if the damage surfaces are in intimate contact. SMCs can be repeated

Jo

multiple times in succession to evaluate consistency and variance, and the recovery rate (vr) can be expressed as

𝑣𝑟 =

𝑅𝑟 ∆𝑇𝑟

(9)

11

Where: ΔTr is the temperature interval over which shape recovery occurs [118], shown in Fig. 3. Recovery rate can be taken over time (t) instead of temperature as well [119]. In addition, the recovery force at a particular strain can be measured via isostrain experiments [87]. While traditional thermomechanical SMCs can provide useful information about the efficiency of the geometrical changes under specified conditions [74, 87], when serving as the

of

primary evaluation method for SMPs, several shortcomings are apparent. For example, a comparison of different materials is not meaningful unless similar experimental procedures are

ro

used (maximum strain, deformation temperature relative to the transition temperature, strain rate, deformation type etc.) [90, 92]. A lack of standardization of these experimental conditions thus

-p

makes SME evaluations troublesome. Additionally, Rr and Rf values in particular give no

re

information about the structural properties of a material that result in specific shape memory

strain, stress, and energy [120].

lP

capabilities, nor do they enable prediction of other performance characteristics such as stored

ur na

Shape memory fill factor (fsm), an extension to the traditional quantitative SMC [90], quantifies shape memory by the ratio of the experimental area inside the length-temperature curve to the ideal length-temperature area (fsm = Across-hatch/Aideal), as shown in Fig. 4. Thus, this technique evaluates not only the completeness of the healing and fixing, but also the rate of

Jo

healing. However, fsm suffers from similar concerns as the traditional thermomechanical SMC method. Also, higher fill factor does not necessarily imply a better solution for a given application, and it does not differentiate between slow and incomplete recovery. Although SMP performance in many applications, including self-healing, is largely a function of the potential magnitude of the shape changes and force/work the material can output, maximum storable strain, stress, and energy frequently go unreported. For conventional SMCs, 12

where programming of the temporary shape is performed well above the shape memory thermal transition temperature, storage capacities are determined by how much the SMP can be deformed before failure [121] in the rubbery plateau region of viscoelasticity. Thus, static stress-strain experiments, in combination with isostrain and hysteresis measurements, can be used to evaluate strain, stress and energy storage capacity. As will be discussed in Section 5, deformability, and thus maximum storable strain, stress, and energy, can often be enhanced by deforming at

of

temperatures lower than used in convention SMCs however. Therefore, direct measurement of

ro

true maximum storage capacities for SMPs in this way can be a time consuming and somewhat

-p

impractical process as experiments at multiple temperatures will be needed.

Dynamic mechanical analysis (DMA) characterizes mechanical properties

re

and viscoelastic behavior of polymers as a function of temperature and/or oscillation frequency [122]. For evaluation of SMPs, DMA has been used to determine the shape memory thermal

lP

transition temperature, the magnitude of the modulus drop at the transition temperature, and the stability of the subsequent rubbery plateau modulus (E’R), which have been suggested to

ur na

qualitatively indicate if a material possesses promising shape memory capabilities [76, 90, 92, 123, 124]. DMA also provides information pertaining to both junction density (νj) and viscoelastic characteristics of polymers [92, 121, 122], which play vital roles in polymer shape memory [87, 92, 121, 125, 126]. With this in mind, recent studies demonstrated that polymers

Jo

exhibit unique shape memory transitions denoted as viscoelastic length transitions (VLTs) near Tg (Fig. 5 A) that can be measured in a single DMA experiment [114]. Directional elongations result due to the increased “viscous-like” behavior of polymer networks at the onset of Tg, and ensuing retractions are driven by release of stored conformational entropic energy due to the presence of chemical/physical crosslinks and/or chain entanglements. By quantifying VLTs in 13

terms of maximum stored strain (εmax), stress (σSF at εmax), and entropic energy density (ΔSS), predictions of relative shape memory capacities can be obtained. Visual comparisons of different Tg-based SMPs are facilitated by their placement on the polymer shape memory prediction plane (Fig. 5 B), generated from empirical fit equations of measured VLT εmax, σSF at εmax, and ΔSS values from thermoplastic and thermosetting polymers. The prediction plane shows shape memory as a spectrum, from high-strain low-stress polymer networks to low-strain high-stress

of

polymer networks, with higher VLT ΔSS equating to greater overall shape memory storage

ro

ability, as it encompasses both stress and strain aspects. Correlation of VLT εmax values from DMA with measured maximum failure strains in static stress-strain measurements confirmed the

-p

direct relationship between VLTs and shape memory capacity [127]. Also, limited data suggests

re

that higher VLT ΔSS values are correlated with efficacious self-repair [58]. 5. Deformability and Strain, Stress, and Energy Storage

lP

Enabling SMPs to perform larger shape changes, apply greater forces, or output more work increases their functionality for many potential applications. Accordingly, increasing the

ur na

amount of strain, stress, and energy a SMP can store is highly desirable. Fundamentally, assuming deformation results only in conformation changes, the amount of strain a SMP can store (and subsequently recover) under specified programming conditions, such as deformation

Jo

temperature, strain rate, or deformation type, is essentially determined by how much deformation can be applied prior to failure/rupture under those conditions [121]. This, in turn, has a large effect on how much energy and stress a SMP can store and release, as Equations 4 and 5 are functions of strain (α = ε + 1), as well as junction/net-point density (νj = ρ/Mj). For self-healing, the extent of deformation that can be sustained during damage and still be efficiently recovered is especially important. Thus, it is vital to understand ultimate strain of SMPs in particular, and 14

how it may depend on variables such as chemical structure, molecular architecture/network structure, temperature, and strain rate. This discussion will initially focus on chemically crosslinked Tg-based SMPs (amorphous single-phase) before expanding to other variations.

In a conventional shape memory cycle (SMC), the deformation step is performed in the rubbery plateau region well above the reversible switching temperature (Tg) of the SMP [74].

of

Based on theoretical considerations, assuming an ideal network and affine deformation, it is

ro

anticipated that failure of a polymer network in the rubbery state should occur once the strands become fully extended from their initial thermodynamically preferred random coil

-p

conformations; at which point backbone bonds would rupture to initiate fracture [129]. Thus, failure strain should depend on the extensibility of the chains, and consequently on the

re

junction/net-point density (νj). However, the strengths of rubbery polymers are significantly less

lP

than expected from covalent bond strengths, and failure commonly occurs around one-tenth of the theoretical maximum extensions [129]. Rupture must therefore begin at a stress concentrator

ur na

where the force is adequate for crack formation or growth/propagation of an existing crack. These stress concentrators may result either from microscopic cracks/flaws/defects that possibly unavoidably exist in all polymers, or formation of micro-cracks/defects may be inevitable at sufficiently large deformations, which then concentrate stress [129]. Nonetheless, in the rubbery

Jo

state, strain to failure is generally governed by ~1/νj (Fig. 6 A) [121, 126, 130, 131], albeit at lower strains than theoretically projected. While the deformation step in SMCs is most commonly performed well above the glass

transition temperature (Tg), polymers exhibit enhanced deformability in the Tg-region, as shown in Fig. 6B [121], approaching much closer to their theoretical maximum extensibility [129, 132]. This enables increased strain, stress, and entropic energy storage for Tg-based SMPs, compared 15

to during a conventional SMC, as a result of “viscoelastic toughening” [121, 125, 131, 133, 134] allowing for greater elongation of chain conformations. Consequently, shape memory may be more effective at assisting healing when damage occurs closer to Tg. In the rubbery state, polymers are highly elastic, but display more “viscous-like” character in the Tg-region, as indicated by the rise in tan δ (dampening) during DMA. The viscoelastic nature of this behavior is exemplified by the fact that ultimate property data follows time-temperature superposition,

of

with a change in strain rate causing a temperature shift of the deformability peak, while the

ro

maximum failure strain value itself is unchanged, thus giving rise to the “failure envelope”

-p

shown in Fig. 6C [129, 132].

This demonstrates that choice of deformation temperature and strain rate are crucial for

re

maximizing strain, stress, and energy storage in SMPs, and significant to shape memory aided self-healing likewise. However, why and how does viscoelasticity affect the rupture process? It

lP

seems to be linked to mechanical hysteresis, or the energy dissipated during deformation. The energy density at break as a function of temperature (from Tg to well above) for a given polymer

ur na

was found to be directly related to the amount of energy dissipated [135], thought to be largely converted to heat as a consequence of internal viscosity/molecular friction, which varies as a function of temperature around Tg. Energy dissipation appears to slow the rate of crack growth/propagation and/or decrease local stress concentrations, and thus delay rupture, allowing

Jo

further elongation of polymer chains prior to catastrophic failure [129, 132, 136]. In general terms, all the energy input during deformation is available for bond rupture when a material is completely elastic, whereas some of that energy is unavailable when a material is inelastic [137], even beyond what is lost.

16

Although this concept is qualitatively well known, only a few systems have been rigorously investigated [121, 128, 130, 134], and there are still many aspects of the deformability peak which are not well understood. Data on the effect of chemical makeup [129] has been insufficient to draw conclusions until just recently [127]. In particular, it was found that while crosslink/junction density (νj) is the primary determining factor for single phase amorphous thermosets, chemical composition can contribute in cases of specific additional interchain

of

attractive forces such as hydrogen bonding. However, the potential impacts of morphologies,

ro

microstructures, and architectures/network structures, remain unaddressed.

Among crosslinked non-crystallizing single-phase SMPs, which have been the focus of

-p

the discussion thus far, shape recovery ratios (Rr) tend to be high and uniform [131], because few

re

sources of potentially unrecoverable deformation exist for these materials. Linear, semicrystalline, multiphase, or filled polymers demonstrate the same general deformability behaviors

lP

and trends that have been discussed, but with additional complexities. For example, linear amorphous homopolymers rely on only macromolecular entanglements to act as SME junction

ur na

points, and thus are much more susceptible to unrecoverable deformations in the form of creep/chain slippage/flow, and thus lower Rr. The extent to which this occurs is dependent on multiple factors, including molecular weight and the rate/temperature of deformation. These materials still exhibit enhanced deformability around Tg [125], and thus enhancement of strain

Jo

storage ability as well. Meanwhile, secondary hard phases (including existing crystalline domains) and fillers can act as toughening agents by impeding the growth of cracks, for example, by causing the crack to follow a tortuous path and/or bifurcate [129, 132]. Melt temperature based (Tm-based) SMPs, such as polycaprolactone (PCL) SMPs as well as other crystallizing polymers, frequently display strain-induced crystallization. This can increase deformability of 17

some such SMPs below Tm compared to above [138], as crystallization can reduce the density of stored elastic energy when under stress [129, 132]. If plastic deformation of secondary or crystalline phase occurs, additional energy can be dissipated as well [132]. It is important to note though that some of these effects could decrease Rr in the process and may not always lead to greater recoverable strain and energy for SMPs ultimately. Only

of

toughening agents/mechanisms that allow increased chain elongation and conformational entropy storage in the switching segments prior to rupture will result in greater recoverable

ro

strain, stress, and energy for shape memory. The effectiveness and efficiency of conformational

-p

entropic energy

re

storage and release is fundamentally responsible for shape recovery efficiency, and the efficacy of self-healing facilitation as well; however, this is different from comparing total energy applied

lP

to energy released. While the Voigt model is too simplistic to explain how various toughening mechanisms can increase deformability to allow greater recoverable strain, stress, and energy for

ur na

shape memory, it can aid understanding of why some processes diminish recovery efficiency, and others do not. When chain scission or slippage occur, the potential stored conformational energy for those chains is irrevocably lost, and thus shape recovery efficiency is compromised. This is analogous to if the spring in a Voigt element fractures upon deformation, and

Jo

consequently there is no driving force for shape recovery (Fig. 7A) in those chains once the applied force is removed. While initially it may seem logical that energy dissipation upon deformation due to viscoelastic effects near Tg would also significantly reduce shape recoverability, this is actually not the case [131, 134]. In the Voigt model shown in Fig. 7B, although additional total applied energy is needed to reach a given deformation in a specific time 18

t as viscosity (η) of the dashpot increases, following removal of the applied force, the shape will recover back until the spring reaches equilibrium regardless of the value of η. The time for shape recovery, however, does increase as η increases. Strain induced crystallization in Tm-based SMPs similarly does not meaningfully affect shape recovery efficiency [138] in itself, so long as it occurs in switching segments. The same is not true of plastic deformation or strain induced

of

crystallization taking place in non-switching secondary phases or domains, however [71]. 6. Reversible Plasticity Shape Memory (RPSM)

ro

Reversible plasticity shape memory (RPSM) enables recovery of seemingly permanent

-p

deformation imparted below or at the reversible transition temperature [69, 92], and is the form of shape memory typically utilized for self-healing. Broadly, if a polymer is able to

re

accommodate an applied deformation (which appears plastic) via conformational changes, some recovery upon heating to above the appropriate transition temperature is likely (Tm if the

lP

deformation is fixed due to strain-induced crystallization; Tg if the material is fully amorphous). This can occur when polymers demonstrate yielding and ductile like behavior, as opposed to

ur na

brittle responses characterized by crack formation and crazing [117]. Whether brittle or ductile behavior transpires is determined by a complex amalgamation of several factors, including deformation type, temperature, and strain-rate (many polymers demonstrate a brittle-to-ductile

Jo

transition below Tg [139]). In general, increasing fracture toughness can increase crack resistance [68]. For glassy polymers close to Tg, viscoelastic toughening favors ductility, and thus potentially recoverable deformation [121, 134]. In terms of structure, it appears that higher chain flexibilities (decreased characteristic ratio (𝐶∞ )) and higher entanglement densities (νe) promote ductile responses [140].

19

Aside from eliminating heating and cooling steps for deformation and shape fixation [92, 141], RPSM has several other advantages and differences compared to conventional SMCs. As discussed in Section 5, deforming at non-conventional temperatures can enhance deformability in some instances [121, 129, 131, 132, 138], and thus increase strain storage in SMPs and consequently entropic energy and stress storage as well. In addition, lower deformation temperatures also result in lower recovery temperatures for a given SMP, a phenomenon referred

of

to as the temperature memory effect (TME) [76, 92, 142-147], which can allow tuning of shape

ro

recovery behavior (it is thought TME may result from greater internal energy storage as

deformation temperature is decreased, since cooperative conformation changes become more

-p

restricted and hindered as free volume decreases) [141, 147]. As mentioned, RVSM can also

re

enable repair of some scratches, cuts, and intendents upon heating above Ttrans [35, 45, 52, 65-71] (Section 7). However, RPSM does have a few potential disadvantages as well. For example,

lP

shape fixity ratios (Rf) are often decreased compared to conventional SMCs [92, 143] as a result of greater initial elastic “spring-back” [141], granted for many applications this is

ur na

inconsequential. Although less thermal energy is expended during RPSM cycles, greater applied work/force is needed to fix the same deformation (higher modulus) [141], thus mechanical energy efficiency is decreased. Note this does not mean that recovery ratios (Rr) are compromised during RPSM cycles, it just indicates greater energy input is needed for the same

Jo

stored strain and energy. Conformational entropic energy storage and release efficiency, which governs shape recoverability, is separate from the ratio of total applied energy to energy released.

7. Shape Memory Facilitation of Self-Healing

20

Self-healing polymers (SHPs) have the inherent ability to restore properties when damaged by mechanical, thermal, or other means [148], enabling enhanced lifespan and functionality of the materials [3]. Polymers capable of healing scratches and/or indents have been developed by employing the SME to facilitate repair [35, 36, 45, 51, 52, 65-71]. In particular, reversible plasticity shape memory (RPSM) enables repeatable recovery of some seemingly permanent deformation imparted below the reversible transition temperature [69, 92]. If the

of

material possess sufficient fracture toughness/crack resistance to avoid crack formation upon

ro

damage, deformation can be recovered upon activation of the SME using conformation entropic

-p

energy stored during

the damage event [68], bringing the damage surfaces into close proximity. Thereafter any

re

subsequent self-healing chemical and/or physical processes necessary for recovery of mechanical

lP

strength can proceed. This mechanism differs from surface tension driven macroscopic/interfacial flow, illustrated in Fig. 8 [60]. RPSM-based self-healing of

ur na

scratches/cuts and/or indents has been demonstrated in both Tg- [45, 66-68, 70] and Tm-based [35, 52, 69, 71] SMPs, single- [67] and multi-phase [71] thermoplastics, thermosets [35, 45, 52, 66, 67], semi-interpenetrating networks (semi-IPNs) [69], and polymer composites [68].

If the fracture resistance is too low such that cracks are generated upon damage instead of

Jo

conformational changes/deformation, it is anticipated that efficacious healing via the RPSM facilitated mechanism will not be possible because significant energy input from damage will be irrevocably lost [68]. Mitigation of unrecoverable deformation is vital for efficient storage and release of conformational entropic energy during the damage-repair cycle, which is necessary for effective shape recovery and facilitation of self-healing [71]. This is illustrated in Fig. 9, which 21

shows that for a neat epoxy SMP, upon scratching many large cracks are formed because local stresses exceed the fracture strength of the material, and consequently the wound does not fully shut following activation of the SME [68]. However, for a graphene-filled epoxy SMP composite, no cracks form following scratching under identical conditions as a result of increased fracture strength/toughness, and complete wound closure occurs after triggering of the SME. As mentioned in Section 6, whether ductile (yielding) or brittle (cracking) behavior occurs

of

is complex and dependent on many factors, including damage mode, temperature, and strain rate,

ro

as well as structural factors, with higher chain flexibilities (decreased characteristic ratio (𝐶∞ )) and higher entanglement densities (νe) seeming to favor ductile responses [140]. Generally,

-p

higher tan δ values signify greater crack resistance [68] due to better energy dissipation at crack

re

tips. Since the peaks in deformability and tan δ occur in the Tg-region due to increased “viscouslike” behavior [121, 128], recoverable forms of deformation may also be more likely during

lP

damage closer to Tg, as well as in the presence of any other toughening agents/mechanisms that do not result in a reduction of Rr.

ur na

Fig. 10 illustrates indent repair by reversible plasticity-type shape recovery in (A) acrylate/methacrylate-based polymers [67], (B) polyurethanes [149], and (C) epoxies [66]. Upon heating to the appropriate thermal transition temperature, such as Tg, indentations created at typical ambient conditions can be recovered. Interestingly, even non-crosslinked amorphous

Jo

polymers lacking sufficient entanglements to display a clear rubbery plateau during DMA, which conventionally would not be considered SMPs, can exhibit RPSM-repair of indentations under the right conditions (Fig. 10A) [67].

22

Healing of scratch/cut damage in materials utilizing RPSM to facilitate repair is typically a two-stage process: (1) activation of the SME to induce wound closure, and (2) subsequent healing/recovery of properties (mechanical or otherwise) by some physical/chemical processes once the surfaces are in sufficiency close proximity. The second stage may consist of a variety of different processes, but it is vital for wound closure and interfacial re-bonding mechanisms to be correctly tuned and sequenced [60]. In the semi-IPN system shown in Fig. 11A, linear

of

polycaprolactone (PCL) chains interpenetrate a crosslinked-PCL network [69]. Upon heating to

ro

80 ⁰C (above the PCL Tm), RPSM recovery is initiated in the crosslinked-PCL network, bringing the damage surfaces into close proximity, which allows the mobile linear-PCL chains to diffuse

-p

across the damage area to mend the wound. This general approach was extended to phase

re

separated morphologies, with electrospun linear-PCL fibers enclosed in an amorphous epoxy matrix. Heating to 80 ⁰C (above the epoxy Tg and PCL Tm) prompts shape recovery in the epoxy

interface [70].

lP

phase to shut the wound, as well as melting of the PCL which flows into and re-bonds the

ur na

Self-healing is enabled in crosslinked polydisulfide networks by dynamic reversible disulfide bonds [35], illustrated in Fig. 11B. Following damage, first the material is heated to 80 ⁰C (above Tm ≈ 61 ⁰C), stimulating shape recovery to close/seal the wound. Exposure to UV light (320-390 nm wavelength, 2000 mW/cm2 intensity, for 5 mins) then induces disulfide

Jo

exchange, causing network reshuffling and material flow which lead to healing at the damage interface (Fig. 11B1). The distinctness of the two stages in the self-healing process are made clear by the stress-strain results in Fig. 11B2, where it is seen that failure stress and strain remain compromised following shape recovery to close/seal the wound, and only after application of UV light to trigger healing are the original properties recovered. Pre-stretching/programming of the 23

SMP in the direction perpendicular to damage permits repair of larger wounds (~100 μm wide). UV light can also be used to reprogram the permanent shape of this material, which conventional crosslinked SMPs are incapable of achieving. The role of complex morphologies, interphases, and viscoelasticity-driven SME on selfhealing was investigated in semi-crystalline PCL-based thermoplastic polyurethane fibers [71],

of

in which polyurethane (PUR) hard-segment domains/crystals distributed the PCL-rich matrix serve as physical crosslinks/junctions to enable shape memory. Fibers pulled from solution

ro

during polymerization (Fig. 11C1  PURP) contain microscale phase-separated domains with mechanically stable gradient interphases, which aid in efficient storage and release of entropic

-p

energy during the damage-repair cycle. Following damage, heating to 65 ⁰C (above the PCL-Tm)

re

triggers the SME, resulting in wound closure through release of stored entropic energy, and regain of mechanical strength results from diffusion of PCL chain segments. However,

lP

chemically identical nanophase-separated fibers pulled from melt following polymerization (Fig. 11C2  PURM) exhibit significantly worse self-repair efficiency (~50% repair efficiency for

ur na

PURM vs. ~85% efficiency for PURP) as a result of energy losses due to the mechanical instability of the nanoscale phases. The ability to minimize unrecoverable deformation and maximize stored conformational entropic energy during deformation is therefore clearly seen as

Jo

a prerequisite for efficient and effective shape memory facilitation of self-healing. While RPSM facilitated repair can greatly enhance the size scale of healable damage,

typically application of a stimulus such as heat is required to initiate the process. It would be of even greater practical significance if shape recovery could occur autonomously under ambient conditions following damage. For example, exploiting the continuous viscoelastic qualities of the glass transition in polymers could potentially permit adequate molecular mobility for wound 24

closure and mending without intervention while also allowing high strength and stiffness. In contrast to conventional shape memory and RPSM, gradual unprompted recovery via viscoelastic shape memory (VESM) as described would not have true indefinite fixation of deformation, but still be driven by conformational entropy. Additionally, a better understanding of the SME could substantially aid in the development of SHPs that utilize shape memory to assist in the repair process. Though numerous self-healing chemistries have been designed, the

of

influences of material properties such as molecular architecture/network structure and

ro

viscoelasticity on the shape memory driven repair process are not as well understood. Self-repair in polymers commonly requires the interplay and coordination of both chemical and physical

8. Physical Aspects of Self-Healing

re

necessitates consideration of both types of processes.

-p

events, which may occur at different size scales [60], and proper design of such systems

lP

Early research of healing in thermoplastic polymers broke-down the repair process into five stages (Fig. 12): segmental surface rearrangements, surface approach, wetting, diffusion, and

ur na

randomization [148, 150]. In many recent studies, various chemical re-bonding techniques demonstrated in thermosetting and thermoplastic self-repairing polymers [59-64] have supplemented the diffusion phase; however the general model is still applicable [59]. During

Jo

surface rearrangement, factors such as topography and roughness of the surfaces, chain-end distribution, and molecular weight distribution come into play. Next, the surfaces come together, through shape memory or other means, to enable subsequent molecular level physical and/or chemical self-healing to occur [148]. Once the surfaces are brought in contact, they must form an interface and wet each other before diffusion can ensue. The necessary interdiffusion distance for reformation of chain entanglements and full recovery of mechanical strength is between 0.4 to 25

0.8 times the radius of gyration (Rg) [151-153], and the average monomer interdiffusion depth is expressed as a function of time X(t) by 𝑋(𝑡) = 𝑋∞ (𝑡⁄𝑇𝑟 )

1⁄4

(10)

where: Tr is the reptation time, which has been found to be proportional to chain molecular weight to the third power [148, 153, 154]. Analysis of the thermodynamics of self-healing has

of

revealed that more flexible and shorter chains are more mobile, thus are more able to facilitate recovery [59]. Following fracture, polymer surfaces are more amenable to healing [155].

ro

Segregation of chain ends to fracture surfaces [148] is favorable for repair, as tethered and free

-p

chains, which can be generated upon damage, have higher mobility than undamaged chains [59]. Surface molecules can also have enhanced mobility and reduced Tg due to greater degree of

re

freedom [148, 156-158], and recent studies found that Tg inside a fresh cut could be lower than

lP

undamaged surfaces [20], which could additionally facilitate diffusion.

Free volume is a necessary prerequisite for chain mobility, and thus generally beneficial

ur na

for self-healing [60]. Although Tg is not a strict criterion for self-healing, several soft, low-Tg polymers capable of self-repair have been developed. However, this does not imply that all such polymers are able to self-heal [36]. For the most part, they exhibit random flow instead of localized self-healing. The development of the high Tg, high mechanical strain and stiffness

Jo

materials capable of self-healing without requiring application of high temperatures or other stimulus for repair, particularly while trying to achieve high degrees of recovery as well as repeatable reparability is challenging. One way to obtain desirable mechanical properties in SHPs without compromising the ability to repair under ambient conditions is through structural heterogeneities, such as phase separated domains, imbedded hard and soft chain segments, 26

addition of fillers to form composites, or enhance supramolecular interactions (H-bonding, van der Waals interactions) [60, 64]. In such designs, soft phases provide free volume and mobility, while more rigid phases can contribute robust mechanical properties [33]. Another strategy is to take advantage of the continuous viscoelastic characteristics of the glass transition, which may enable enhanced strength and stiffness while allowing sufficient molecular mobility for wound closure and mending processes to proceed without intervention in materials near Tg at ambient

of

conditions.

ro

Remote self-repair has been demonstrated by dispersing superparamagnetic nanoparticles that respond to magnetic fields in a thermoplastic polymer matrix [56]. An oscillating magnetic

-p

field is applied to excite the magnetic moment of the nanoparticles, which convert the energy

re

input from the oscillating field into thermal energy. This softens the material to allow local amorphous flow/diffusion at the interfacial regions to repair the polymer matrix. In the same

electromagnetic radiation [57].

lP

vein, conductive nanomaterials can also enable remote repair via electric fields or

ur na

In addition to the chemical and morphological make-up of the material, the specific conditions and mode of damage also influences the viability/applicability of particular physical repair mechanisms [60]. It is important to note that mechanisms designed for one type of damage

Jo

may not be suitable for others. For example, it was found that the self-repair of ballistic puncture in poly(ethylene-co-methacrylic acid) copolymers and ionomers is driven by heat produced from friction during damage, resulting in a local melt state with adequate elasticity to spring back and mend the wound [160-163]. Meanwhile, composite materials designed to autonomously repair crack damage and prevent propagation have often utilized encapsulation methods [4-8]. Selfhealable fibers developed thus far primarily have employed the encapsulation concept as well, by 27

co-electrospinning core-shell fibers with a liquid healing agent core inside a polymer shell [912]. However, such designs may have adverse effects on mechanical and other properties and have proven ill-suited for practical applications. Alternatively, materials intended to repair from complete fracture into two pieces require manual intervention [14, 15, 19, 22, 26, 31-33, 37, 3942, 44].

of

9. Conclusions and Insights The dynamic stimuli-responsive capabilities that shape memory and self-healing

ro

polymers embody have attracted significant scientific and technological interest, and their unique

-p

abilities will allow for novel approaches to critical real-world problem solving in the years to come. One of the greatest challenges looming today stems from the environmental impacts of

re

excess disposal/waste of commodity materials, driving the need for increased sustainability. To

lP

that end, prodigious efforts have been made to prolong materials’ lifespans through the advancement of self-healing polymers (SHPs). While numerous innovative self-healing chemistries using dynamic reformable/reversible covalent and non-covalent bonding methods

ur na

have been developed, many challenges persist. Creation of materials exhibiting high strength and stiffness that can efficiently and repeatedly self-repair without intervention under normal use conditions has remained elusive. In addition, most synthetic self-repairing materials to-date have

Jo

required incorporation of special physical/chemical healing components. For the potential environmental benefits from SHPs to truly have an impact, further developments are needed towards creating materials based on commonly used monomers/oligomers without necessitating complex synthesis or elaborate fabrication processes. Research motivated to address these difficulties will require careful molecular and structural design, and to help better understand the role of shape memory in the repair process, quantitative assessments of shape memory will be 28

needed. The contributions of different morphologies, microstructures, architectures/network structures, chemical composition, and viscoelastic behaviors on shape memory and self-healing need further understanding. The importance of proper material design to the coordination of chemical and physical processes necessary at different length scales for self-healing will be critical. It is vital for wound

of

closure and interfacial re-bonding mechanisms to be tuned, sequenced, and amplified by efficient storage and release of conformational entropic energy upon damage to effectively facilitate self-

ro

healing. Knowledge of the stress and strain intensities and distributions caused by damage,

-p

which may depend on factors such as the viscoelastic state of the material, can provide insights into optimal material design for recovery of different types or sizes of damage. Diversified

re

molecular architectures may help create materials with varied stress-strain recovery profiles that best suit each application. At the same time, greater familiarity with molecular/chemical

lP

interactions and associations that can drive strength recovery mechanisms could open new avenues for self-healing. In particular, to achieve optimal mechanical properties while still

ur na

allowing for autonomous repair under use conditions would appear to require a tuned balance between interaction strengths and mobility.

Jo

Author Contributuion

Chris C. Hornat: Data Collection, Writing, Marek W. Urban: Conceptualization, Writing, Reviewing and Editing.

Declaration of interests 29

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests

Acknowledgments

of

This work was supported by the National Science Foundation under Award DMR 1744306 and

Jo

ur na

lP

re

-p

ro

partially by the J.E. Sirrine Foundation Endowment at Clemson University.

30

References

[8] [9] [10]

[11] [12]

[13]

[14] [15] [16]

Jo

[17]

of

[7]

ro

[6]

-p

[5]

re

[3] [4]

lP

[2]

Liu F, Urban MW. Recent advances and challenges in designing stimuli-responsive polymers. Prog Polym Sci 2010;35:3-23. Urban MW. Stimuli-responsive Materials: From Molecules to Nature Mimicking Materials Design. Cambridge: Royal Society of Chemistry, 2016 . 475 pp. Sumerlin BS. Next-generation self-healing materials. Science 2018;362:150-1. White SR, Sottos NR, Geubelle PH, Moore JS. Autonomic healing of polymer composites. Nature 2001;409:794-797. Toohey KS, Sottos NR, Lewis JA, Moore JS, White SR. Self-healing materials with microvascular networks. Nat Mater 2007;6:581-585. Bleay SM, Loader CB, Hawyes VJ, Humberstone L, Curtis PT. A smart repair system for polymer matrix composites. Composites Part A 2001;32:1767-76. Pang JWC, Bond IP. ‘Bleeding composites’—damage detection and self-repair using a biomimetic approach. Composites Part A 2005;36:183-8.t Hansen CJ, Wu W, Toohey KS, Sottos NR, White SR, Lewis JA. Self‐Healing Materials with Interpenetrating Microvascular Networks. Adv Mater 2009;21:4143-7. Park JH, Braun PV. Coaxial Electrospinning of Self‐Healing Coatings. Adv Mater 2010;22:496-9. Sinha-Ray S, Pelot DD, Zhou ZP, Rahman A, Wu XF, Yarin AL. Encapsulation of selfhealing materials by coelectrospinning, emulsion electrospinning, solution blowing and intercalation. J Mater Chem 2012;22:9138-46. Lee MW, An S, Lee C, Liou M, Yarin AL, Yoon SS. Self-healing transparent core–shell nanofiber coatings for anti-corrosive protection. J Mater Chem A 2014;2:7045-53. An S, Liou M, Song KY, Jo HS, Lee MW, Al-Deyab SS, et al. Highly flexible transparent self-healing composite based on electrospun core–shell nanofibers produced by coaxial electrospinning for anti-corrosion and electrical insulation. Nanoscale 2015;7:17778-85. Sijbesma RP, Beijer FH, Brunsveld L, Folmer BJB, Hirschberg JHKK, Lange RFM, et al. Reversible polymers formed from self-complementary monomers using quadruple hydrogen bonding. Science 1997;278:1601-4. Chen X, Dam MA, Ono K, Mal A, Shen H, Nutt SR, et al. A thermally re-mendable crosslinked polymeric material. Science 2002;295:1698-702. Cordier P, Tournilhac F, Soulié-Ziakovic C, Leibler L. Self-healing and thermoreversible rubber from supramolecular assembly. Nature 2008;451:977-980. Ghosh B, Urban MW. Self-repairing oxetane-substituted chitosan polyurethane networks. Science 2009;323:1458-60. Burattini S, Colquhoun HM, Greenland BW, Hayes W. A novel self-healing supramolecular polymer system. Faraday Discuss 2009;143:251-64. Burattini S, Greenland BW, Merino DH, Weng W, Seppala J, Colquhoun HM, et al. A healable supramolecular polymer blend based on aromatic π−π stacking and hydrogenbonding interactions. J Am Chem Soc 2010;132:12051-8. Deng G, Tang C, Li F, Jiang H, Chen Y. Covalent cross-linked polymer gels with reversible sol− gel transition and self-healing properties. Macromolecules 2010;43:11914. Ghosh B, Chellappan KV, Urban MW. Self-healing inside a scratch of oxetane-substituted chitosan-polyurethane (OXE-CHI-PUR) networks. J Mater Chem 2011;21:14473-86.

ur na

[1]

[18]

[19]

[20]

31

Jo

ur na

lP

re

-p

ro

of

[21] Yoon JA, Kamada J, Koynov K, Mohin J, Nicolaÿ R, Zhang Y, et al. Self-healing polymer films based on thiol–disulfide exchange reactions and self-healing kinetics measured using atomic force microscopy. Macromolecules 2011;45:142-9. [22] Amamoto Y, Kamada J, Otsuka H, Takahara A, Matyjaszewski K. Repeatable Photoinduced Self‐Healing of Covalently Cross‐Linked Polymers through Reshuffling of Trithiocarbonate Units. Angew Chem Int Ed 2011;50:1660-3. [23] Canadell J, Goossens H, Klumperman B. Self-healing materials based on disulfide links. Macromolecules 2011;44:2536-41. [24] Burnworth M, Tang L, Kumpfer JR, Duncan AJ, Beyer FL, Fiore GL, et al. Optically healable supramolecular polymers. Nature 2011;472:334-337. [25] Holten-Andersen N, Harrington MJ, Birkedal H, Lee BP, Messersmith PB, Lee KYC, et al. pH-induced metal-ligand cross-links inspired by mussel yield self-healing polymer networks with near-covalent elastic moduli. Proc Natl Acad Sci USA 2011;108:2651-5. [26] Nakahata M, Takashima Y, Yamaguchi H, Harada A. Redox-responsive self-healing materials formed from host–guest polymers. Nat Commun 2011;2:511/1-6. [27] Froimowicz P, Frey H, Landfester K. Towards the Generation of Self-Healing Materials by Means of a Reversible Photo-induced Approach. Macromol Rapid Commun 2011;32:468-73. [28] Yuan Ce, Rong MZ, Zhang MQ, Zhang ZP, Yuan YC. Self-healing of polymers via synchronous covalent bond fission/radical recombination. Chem Mater 2011;23:5076-81. [29] Cui J, del Campo A. Multivalent H-bonds for self-healing hydrogels. Chem Commun 2012;48:9302-4. [30] Ling J, Rong MZ, Zhang MQ. Photo-stimulated self-healing polyurethane containing dihydroxyl coumarin derivatives. Polymer 2012;53:2691-8. [31] Imato K, Nishihara M, Kanehara T, Amamoto Y, Takahara A, Otsuka H. Self‐Healing of Chemical Gels Cross‐Linked by Diarylbibenzofuranone‐Based Trigger‐Free Dynamic Covalent Bonds at Room Temperature. Angew Chem Int Ed 2012;51:1138-42. [32] Zheng P, McCarthy TJ. A surprise from 1954: siloxane equilibration is a simple, robust, and obvious polymer self-healing mechanism. J Am Chem Soc 2012;134:2024-7. [33] Chen Y, Kushner AM, Williams GA, Guan Z. Multiphase design of autonomic selfhealing thermoplastic elastomers. Nat Chem 2012;4:467-72. [34] Amamoto Y, Otsuka H, Takahara A, Matyjaszewski K. Self‐Healing of Covalently Cross‐Linked Polymers by Reshuffling Thiuram Disulfide Moieties in Air under Visible Light. Adv Mater 2012;24:3975-80. [35] Michal BT, Jaye CA, Spencer EJ, Rowan SJ. Inherently photohealable and thermal shapememory polydisulfide networks. ACS Macro Lett 2013;2:694-9. [36] Yang Y, Urban MW. Self‐Repairable Polyurethane Networks by Atmospheric Carbon Dioxide and Water. Angew Chem Int Ed 2014;53:12142-7. [37] Ying H, Zhang Y, Cheng J. Dynamic urea bond for the design of reversible and selfhealing polymers. Nat Commun 2014;5:3218/1-9. [38] Oehlenschlaeger KK, Mueller JO, Brandt J, Hilf S, Lederer A, Wilhelm M, et al. Adaptable hetero Diels–Alder networks for fast self‐healing under mild conditions. Adv Mater 2014;26:3561-6. [39] Neal JA, Mozhdehi D, Guan Z. Enhancing mechanical performance of a covalent selfhealing material by sacrificial noncovalent bonds. J Am Chem Soc 2015;137:4846-50. 32

Jo

ur na

lP

re

-p

ro

of

[40] Ji S, Cao W, Yu Y, Xu H. Visible‐Light‐Induced Self‐Healing Diselenide‐ Containing Polyurethane Elastomer. Adv Mater 2015;27:7740-5. [41] Li CH, Wang C, Keplinger C, Zuo JL, Jin L, Sun Y, et al. A highly stretchable autonomous self-healing elastomer. Nat Chem 2016;8:618-24. [42] Jin K, Li L, Torkelson JM. Recyclable Crosslinked Polymer Networks via One‐Step Controlled Radical Polymerization. Adv Mater 2016;28:6746-50. [43] Chao A, Negulescu I, Zhang D. Dynamic Covalent Polymer Networks Based on Degenerative Imine Bond Exchange: Tuning the Malleability and Self-Healing Properties by Solvent. Macromolecules 2016;49:6277-84. [44] Lai JC, Mei JF, Jia XY, Li CH, You XZ, Bao Z. A Stiff and Healable Polymer Based on Dynamic‐Covalent Boroxine Bonds. Adv Mater 2016;28:8277-82. [45] Yang Y, Urban MW. Self-healing of glucose-modified polyurethane networks facilitated by damage-induced primary amines. Polym Chem 2017;8:303-9. [46] Zechel S, Geitner R, Abend M, Siegmann M, Enke M, Kuhl N, et al. Intrinsic self-healing polymers with a high E-modulus based on dynamic reversible urea bonds. NPG Asia Mater 2017;9:e420/1-8. [47] Fang Z, Zheng N, Zhao Q, Xie T. Healable, reconfigurable, reprocessable thermoset shape memory polymer with highly tunable topological rearrangement kinetics. ACS Appl Mater Interfaces 2017;9:22077-82. [48] Park JI, Choe A, Kim MP, Ko H, Lee TH, Noh SM, et al. Water-adaptive and repeatable self-healing polymers bearing bulky urea bonds. Polym Chem 2018;9:11-9. [49] Delpierre S, Willocq B, Manini G, Lemaur V, Goole J, Gerbaux P, et al. A Simple Approach for Self-Healable and Stiff Polymer Network from Iminoboronate-Based Boroxine Chemistry. Chem Mater 2019;31:3736-3744. [50] Zhang L, Liu Z, Wu X, Guan Q, Chen S, Sun L, et al. A Highly Efficient Self-Healing Elastomer with Unprecedented Mechanical Properties. Adv Mater 2019;31:1901402/1-8. [51] Lu L, Fan J, Li G. Intrinsic healable and recyclable thermoset epoxy based on shape memory effect and transesterification reaction. Polymer 2016;105:10-8. [52] Van Herck N, Du Prez FE. Fast healing of polyurethane thermosets using reversible triazolinedione chemistry and shape-memory. Macromolecules 2018;51:3405-14. [53] Yanagisawa Y, Nan Y, Okuro K, Aida T. Mechanically robust, readily repairable polymers via tailored noncovalent cross-linking. Science 2018;359:72-6. [54] An N, Wang X, Li Y, Zhang L, Lu Z, Sun J. Healable and Mechanically Super-Strong Polymeric Composites Derived from Hydrogen‐Bonded Polymeric Complexes. Adv Mater 2019;31:1904882/1-7. [55] Brutman JP, Delgado PA, Hillmyer MA. Polylactide vitrimers. ACS Macro Lett 2014;3:607-10. [56] Corten CC, Urban MW. Repairing polymers using oscillating magnetic field. Adv Mater 2009;21:5011-5. [57] Huang L, Yi N, Wu Y, Zhang Y, Zhang Q, Huang Y, et al. Multichannel and Repeatable Self‐Healing of Mechanical Enhanced Graphene‐Thermoplastic Polyurethane Composites. Adv Mater 2013;25:2224-8. [58] Urban MW, Davydovich D, Yang Y, Demir T, Zhang Y, Casabianca L. Key-and-lock commodity self-healing copolymers. Science 2018;362:220-5. [59] Yang Y, Urban MW. Self-healing polymeric materials. Chem Soc Rev 2013;42:7446-67. 33

Jo

ur na

lP

re

-p

ro

of

[60] Yang Y, Ding X, Urban MW. Chemical and physical aspects of self-healing materials. Prog Polym Sci 2015;49:34-59. [61] Du P, Wang X. Reversible cross-linking polymer-based self-healing materials. In: Li G, Meng H, editors. Recent Advances in Smart Self-healing Polymers and Composites. Cambridge: Woodhead Publishing, 2015. p. 159-79. [62] Deng W, You Y, Zhang A. Supramolecular network-based self-healing polymer materials. In: Li G, Meng H, editors. Recent Advances in Smart Self-healing Polymers and Composites. Cambridge: Woodhead Publishing, 2015. p. 181-210. [63] Zou W, Dong J, Luo Y, Zhao Q, Xie T. Dynamic covalent polymer networks: From old chemistry to modern day innovations. Adv Mater 2017;29:1606100/1-16. [64] Yang Y, Urban MW. Self‐Healing of Polymers via Supramolecular Chemistry. Adv Mater Interfaces 2018;5:1800384/1-19. [65] Rivero G, Nguyen LTT, Hillewaere XKD, Du Prez FE. One-pot thermo-remendable shape memory polyurethanes. Macromolecules 2014;47:2010-8. [66] Nelson BA, King WP, Gall K. Shape recovery of nanoscale imprints in a thermoset “shape memory” polymer. Appl Phys Lett 2005;86:103108/1-3. [67] Wornyo E, Gall K, Yang F, King W. Nanoindentation of shape memory polymer networks. Polymer 2007;48:3213-25. [68] Xiao X, Xie T, Cheng YT. Self-healable graphene polymer composites. J Mater Chem 2010;20:3508-14. [69] Rodriguez ED, Luo X, Mather PT. Linear/network poly(ε-caprolactone) blends exhibiting shape memory assisted self-healing (SMASH). ACS Appl Mater Interfaces 2011;3:15261. [70] Luo X, Mather PT. Shape memory assisted self-healing coating. ACS Macro Lett 2013;2:152-6. [71] Yang Y, Davydovich D, Hornat CC, Liu X, Urban MW. Leaf-inspired self-healing polymers. Chem 2018;4:1928-36. [72] Li G, Uppu N. Shape memory polymer based self-healing syntactic foam: 3-D confined thermomechanical characterization. Compos Sci Technol 2010;70:1419-27. [73] Speck O, Schlechtendahl M, Borm F, Kampowski T, Speck T. Humidity-dependent wound sealing in succulent leaves of Delosperma cooperi–An adaptation to seasonal drought stress. Beilstein J Nanotechnol 2018;9:175-86. [74] Lendlein A, Kelch S. Shape‐memory polymers. Angew Chem Int Ed 2002;41:2034-57. [75] Lendlein A, Langer R. Biodegradable, elastic shape-memory polymers for potential biomedical applications. Science 2002;296:1673-6. [76] Gall K, Yakacki CM, Liu Y, Shandas R, Willett N, Anseth KS. Thermomechanics of the shape memory effect in polymers for biomedical applications. J Biomed Mater Res Part A 2005;73:339-48. [77] Ge Q, Sakhaei AH, Lee H, Dunn CK, Fang NX, Dunn ML. Multimaterial 4D Printing with Tailorable Shape Memory Polymers. Sci Rep 2016;6:31110/1-11. [78] Huang L, Jiang R, Wu J, Song J, Bai H, Li B, et al. Ultrafast Digital Printing toward 4D Shape Changing Materials. Adv Mater 2017;29:1605390/1-6. [79] Ebara M, Uto K, Idota N, Hoffman JM, Aoyagi T. Rewritable and shape-memory soft matter with dynamically tunable microchannel geometry in a biological temperature range. Soft Matter 2013;9:3074-80. 34

Jo

ur na

lP

re

-p

ro

of

[80] Mondal S, Hu JL. Temperature stimulating shape memory polyurethane for smart clothing. Indian J Fibre Text Res 2006;31:66-71. [81] Reddy S, Arzt E, del Campo A. Bioinspired surfaces with switchable adhesion. Adv Mater 2007;19:3833-7. [82] Xie T, Xiao X. Self-peeling reversible dry adhesive system. Chem Mater 2008;20:2866-8. [83] Sokolowski WM, Tan SC. Advanced self-deployable structures for space applications. J Spacecr Rockets 2007;44:750-4. [84] Jin B, Song H, Jiang R, Song J, Zhao Q, Xie T. Programming a crystalline shape memory polymer network with thermo-and photo-reversible bonds toward a single-component soft robot. Sci Adv 2018;4:eaao3865/1-6. [85] Vernon LB, Vernon HM. Process of manufacturing articles of thermoplastic synthetic resins. US 2,234,993, 1941 . [86] Rainer WC, Redding EM, Hitov JJ, Sloan AW, Stewart WD. Polyethylene product and process. US 3,144,398A, 1958 . [87] Leng J, Du S. Shape-memory polymers and multifunctional composites. Boca Raton: CRC Press, 2010. 363 pp. [88] Behl M, Lendlein A. Shape-memory polymers. Mater Today 2007;10:20-8. [89] Gu X, Mather PT. Entanglement-based shape memory polyurethanes: Synthesis and characterization. Polymer 2012;53:5924-34. [90] Liu C, Qin H, Mather PT. Review of progress in shape-memory polymers. J Mater Chem 2007;17:1543-58. [91] Meng H, Li G. A review of stimuli-responsive shape memory polymer composites. Polymer 2013;54:2199-221. [92] Xie T. Recent advances in polymer shape memory. Polymer 2011;52:4985-5000. [93] Zhao Q, Qi HJ, Xie T. Recent progress in shape memory polymer: New behavior, enabling materials, and mechanistic understanding. Prog Polym Sci 2015;49:79-120. [94] Rousseau IA, Mather PT. Shape memory effect exhibited by smectic-C liquid crystalline elastomers. J Am Chem Soc 2003;125:15300-1. [95] Qin H, Mather PT. Combined one-way and two-way shape memory in a glass-forming nematic network. Macromolecules 2008;42:273-80. [96] Lendlein A, Jiang H, Jünger O, Langer R. Light-induced shape-memory polymers. Nature 2005;434:879-82. [97] Aoki D, Teramoto Y, Nishio Y. SH-containing cellulose acetate derivatives: preparation and characterization as a shape memory-recovery material. Biomacromolecules 2007;8:3749-57. [98] Kumpfer JR, Rowan SJ. Thermo-, photo-, and chemo-responsive shape-memory properties from photo-cross-linked metallo-supramolecular polymers. J Am Chem Soc 2011;133:12866-74. [99] Li J, Viveros JA, Wrue MH, Anthamatten M. Shape‐Memory Effects in Polymer Networks Containing Reversibly Associating Side‐Groups. Adv Mater 2007;19:2851-5. [100] Mather PT, Luo X, Rousseau IA. Shape memory polymer research. Annu Rev Mater Res 2009;39:445-71. [101] Lee KM, Koerner H, Vaia RA, Bunning TJ, White TJ. Light-activated shape memory of glassy, azobenzene liquid crystalline polymer networks. Soft Matter 2011;7:4318-24. [102] Cho JW, Kim JW, Jung YC, Goo NS. Electroactive shape‐memory polyurethane composites incorporating carbon nanotubes. Macromol Rapid Commun 2005;26:412-6. 35

Jo

ur na

lP

re

-p

ro

of

[103] Koerner H, Price G, Pearce NA, Alexander M, Vaia RA. Remotely actuated polymer nanocomposites—stress-recovery of carbon-nanotube-filled thermoplastic elastomers. Nat Mater 2004;3:115-120. [104] Mohr R, Kratz K, Weigel T, Lucka-Gabor M, Moneke M, Lendlein A. Initiation of shapememory effect by inductive heating of magnetic nanoparticles in thermoplastic polymers. Proc Natl Acad Sci USA 2006;103:3540-5. [105] Schmidt AM. Electromagnetic activation of shape memory polymer networks containing magnetic nanoparticles. Macromol Rapid Commun 2006;27:1168-72. [106] Yakacki CM, Satarkar NS, Gall K, Likos R, Hilt JZ. Shape‐memory polymer networks with Fe3O4 nanoparticles for remote activation. J Appl Polym Sci 2009;112:3166-76. [107] Li G, Fei G, Xia H, Han J, Zhao Y. Spatial and temporal control of shape memory polymers and simultaneous drug release using high intensity focused ultrasound. J Mater Chem 2012;22:7692-6. [108] Maitland DJ, Metzger MF, Schumann D, Lee A, Wilson TS. Photothermal properties of shape memory polymer micro-actuators for treating stroke. Lasers Surg Med 2002;30:111. [109] Liu Y, Boyles JK, Genzer J, Dickey MD. Self-folding of polymer sheets using local light absorption. Soft Matter 2012;8:1764-9. [110] Yang B, Huang WM, Li C, Lee CM, Li L. On the effects of moisture in a polyurethane shape memory polymer. Smart Mater Struct 2003;13:191-195. [111] Huang WM, Yang B, An L, Li C, Chan YS. Water-driven programmable polyurethane shape memory polymer: Demonstration and mechanism. Appl Phys Lett 2005;86:114105/1-3. [112] Qi X, Yao X, Deng S, Zhou T, Fu Q. Water-induced shape memory effect of graphene oxide reinforced polyvinyl alcohol nanocomposites. J Mater Chem A 2014;2:2240-9. [113] Lv H, Leng J, Liu Y, Du S. Shape‐memory polymer in response to solution. Adv Eng Mater 2008;10:592-5. [114] Hornat CC, Yang Y, Urban MW. Quantitative Predictions of Shape‐Memory Effects in Polymers. Adv Mater 2017;29: 1603334/1-8. [115] James HM, Guth E. Theory of the elastic properties of rubber. J Chem Phys 1943;11:45581. [116] Treloar LRG. The elasticity and related properties of rubbers. Rep Prog Phys 1973;36:755-826. [117] Hiemenz PC, Lodge TP. Polymer chemistry Boca Raton: CRC Press, 2007. 587 pp. [118] Lendlein A, Kelch S, Schulte J, Kratz K. Shape-Memory Polymers. In: Buschow KHJ, Cahn RW, Flemings MC, Kramer EJ, Mahajan S, and Veyssiere P, editors. Encyclopedia of Mateials Science and Technology – Updates. New York: Elsevier Science Ltd, 2005. p. 1-9. [119] Bonner M, de Oca HM, Brown M, Ward I. A novel approach to predict the recovery time of shape memory polymers. Polymer 2010;51:1432-6. [120] Berg GJ, McBride MK, Wang C, Bowman CN. New directions in the chemistry of shape memory polymers. Polymer 2014;55:5849-72. [121] Yakacki CM, Willis S, Luders C, Gall K. Deformation Limits in Shape‐Memory Polymers. Adv Eng Mater 2008;10:112-9. [122] Menard KP. Dynamic mechanical analysis: a practical introduction, Boca Raton: CRC Press, 2008. 218 pp. 36

Jo

ur na

lP

re

-p

ro

of

[123] Kim BK, Lee SY, Xu M. Polyurethanes having shape memory effects. Polymer 1996;37:5781-93. [124] Ratna D, Karger-Kocsis J. Recent advances in shape memory polymers and composites: a review. J Mater Sci 2008;43:254-69. [125] Smith KE, Temenoff JS, Gall K. On the toughness of photopolymerizable (meth) acrylate networks for biomedical applications. J Appl Polym Sci 2009;114:2711-22. [126] Ortega AM, Kasprzak SE, Yakacki CM, Diani J, Greenberg AR, Gall K. Structure– property relationships in photopolymerizable polymer networks: Effect of composition on the crosslinked structure and resulting thermomechanical properties of a (meth) acrylate‐ based system. J Appl Polym Sci 2008;110:1559-72. [127] Hornat CC, Nijemeisland M, Senardi M, Yang Y, , Pattyn C, van der Zwaag S, Urban MW. Quantitative predictions of maximum strain storage in shape memory polymers. Polymer 2019;122006/1-5` [128] Smith TL. Ultimate tensile properties of elastomers. I. Characterization by a time and temperature independent failure envelope. J Polym Sci Part A Polym Phys 1963;1:3597615. [129] Smith TL. Strength and extensibility of elastomers. In: Eirich FR, editor. Rheology. Vol 5. New York: Academic Press, 1969. p. 127-221. [130] Smith TL, Chu WH. Ultimate tensile properties of elastomers. VII. Effect of crosslink density on time–temperature dependence. J Polym Sci Part A2 Polym Phys 1972;10:13350. [131] Feldkamp DM, Rousseau IA. Effect of Chemical Composition on the Deformability of Shape‐Memory Epoxies. Macromol Mater Eng 2011;296:1128-41. [132] Smith TL. Strength of elastorners—a perspective. Polym Eng Sci 1977;17:129-43. [133] Safranski DL, Gall K. Effect of chemical structure and crosslinking density on the thermomechanical properties and toughness of (meth) acrylate shape memory polymer networks. Polymer 2008;49:4446-55. [134] Feldkamp DM, Rousseau IA. Effect of the Deformation Temperature on the Shape‐ Memory Behavior of Epoxy Networks. Macromol Mater Eng 2010;295:726-34. [135] Harwood JAC, Payne AR. Hysteresis and strength of rubbers. J Appl Polym Sci 1968;12:889-901. [136] Andrews EH. Rupture propagation in hysteresial materials: stress at a notch. J Mech Phys Solids 1963;11:231-42. [137] Andrews EH. A generalized theory of fracture mechanics. J Mater Sci 1974;9:887-94. [138] Zotzmann J, Behl M, Feng Y, Lendlein A. Copolymer networks based on poly(ω‐ pentadecalactone) and poly(ϵ‐caprolactone) segments as a versatile triple‐shape polymer system. Adv Func Mater 2010;20:3583-94. [139] Young RJ, Lovell PA. Introduction to polymers. Boca Raton: CRC Press, 2011. 652 pp. [140] Wu S. Chain structure, phase morphology, and toughness relationships in polymers and blends. Polym Eng Sci 1990;30:753-61. [141] Li G, Wang A. Cold, warm, and hot programming of shape memory polymers. J Polym Sci Part B Polym Phys 2016;54:1319-39. [142] Miaudet P, Derré A, Maugey M, Zakri C, Piccione PM, Inoubli R, et al. Shape and temperature memory of nanocomposites with broadened glass transition. Science 2007;318:1294-6. 37

Jo

ur na

lP

re

-p

ro

of

[143] Xie T. Tunable polymer multi-shape memory effect. Nature 2010;464:267-70. [144] Cui J, Kratz K, Lendlein A. Adjusting shape-memory properties of amorphous polyether urethanes and radio-opaque composites thereof by variation of physical parameters during programming. Smart Mater Struct 2010;19:065019/1-10. [145] Xie T, Page KA, Eastman SA. Strain‐Based Temperature Memory Effect for Nafion and Its Molecular Origins. Adv Func Mater 2011;21:2057-66. [146] Kratz K, Madbouly SA, Wagermaier W, Lendlein A. Temperature‐Memory Polymer Networks with Crystallizable Controlling Units. Adv Mater 2011;23:4058-62. [147] Yakacki CM, Ortega AM, Frick CP, Lakhera N, Xiao R, Nguyen TD. Unique Recovery Behavior in Amorphous Shape‐Memory Polymer Networks. Macromol Mater Eng 2012;297:1160-6. [148] Wool RP. Self-healing materials: a review. Soft Matter 2008;4:400-18. [149] Huang WM, Ding Z, Wang CC, Wei J, Zhao Y, Purnawali H. Shape memory materials. Mater today 2010;13:54-61. [150] Wool RP, O’connor KM. A theory crack healing in polymers. J Appl Phys 1981;52:595363. [151] Hammouda B, Kim KD, Sperling LH, Klein A. Reptation time, temperature, and cosurfactant effects on the molecular interdiffusion rate during polystyrene latex film formation. Macromolecules 1994;27:6841-50. [152] Wool RP. Polymer interfaces: structure and strength. Cincinnati: Hanser Pub Inc, 1995. 494 pp. [153] Sperling LH. Introduction to physical polymer science. Hoboken: John Wiley & Sons Inc, 2006. 845 pp. [154] Welp KA, Wool RP, Agrawal G, Satija SK, Pispas S, Mays J. Direct observation of polymer dynamics: mobility comparison between central and end section chain segments. Macromolecules 1999;32:5127-38. [155] Maes F, Montarnal D, Cantournet S, Tournilhac F, Corté L, Leibler L. Activation and deactivation of self-healing in supramolecular rubbers. Soft Matter 2012;8:1681-7. [156] Ellison CJ, Torkelson JM. The distribution of glass-transition temperatures in nanoscopically confined glass formers. Nat Mater 2003;2:695-700. [157] Bodiguel H, Fretigny C. Reduced Viscosity in Thin Polymer Films. Phys Rev Lett 2006;97:266105/1-4. [158] Fakhraai Z, Forrest JA. Measuring the surface dynamics of glassy polymers. Science 2008;319:600-4. [159] Wu DY, Meure S, Solomon D. Self-healing polymeric materials: A review of recent developments. Prog Polym Sci 2008;33:479-522. [160] Fall R. Puncture Reversal of Ethylene Ionomers- Mechanistic Studies. MS Thesis. Blacksburg: Virginia Polytechnic Institute and State University, 2001. 65 pp. [161] Kalista Jr SJ, Ward TC, Oyetunji Z. Self-healing of poly(ethylene-co-methacrylic acid) copolymers following projectile puncture. Mech Adv Mater Struct 2007;14:391-7. [162] Kalista SJ, Ward TC. Thermal characteristics of the self-healing response in poly(ethylene-co-methacrylic acid) copolymers. J R Soc Interface 2007;4:405-11. [163] Kalista SJ, Pflug JR, Varley RJ. Effect of ionic content on ballistic self-healing in EMAA copolymers and ionomers. Polym Chem 2013;4:4910-26.

38

of ro -p

Jo

ur na

lP

re

Fig. 1. Structural requirements for SMPs: (A) The net-points/junctions can consist of chemical crosslinks, crystalline or glassy secondary phases, macromolecular entanglements, or interpenetrating networks. (B) The reversible molecular switch can be achieved using reversible crosslinking, supramolecular association/disassociation, or the glass, melting, or liquid crystalline (LC) transition. [91], Copyright 2013. Adopted with permission from Elsevier Science Ltd.

39

of ro -p re lP

Jo

ur na

Fig. 2. (A) Conventional Tg-based shape memory cycle (SMC): initial shape at room temperature (a), shape at T > Tg (b), fixed shape at room temperature (c-d), and shape recovery process at recovery temperature TR (e-f). [114], Copyright 2016. Adopted with permission form John Wiley & Sons Inc. (B) Pictorial illustration of corresponding molecular level events. [74], Copyright 2002. Adopted with permission form John Wiley & Sons Inc.

40

of ro

Jo

ur na

lP

re

-p

Fig. 3. Graphical illustration of a quantitative thermomechanical shape memory cycle (SMC). Temperature (T) and stress (σ) are inputs, strain (ε) is the material response. (Deformation temperature (Tdef), applied deforming force (σdef), maximum deformed strain (εm), fixation temperature (Tfix), fixed strain (εf), recovery temperature (Trec), residual strain following recovery (εr), shape recovery temperature interval (ΔTr). [92], Copyright 2011. Adopted with permission from Elsevier Science Ltd.

Fig. 4. Schematic illustrations of SMP performance evaluated by fill factor (fsm = Across-hatch/Aideal). (A) Idealized SMP behavior. (B) Realistic SMP behavior demonstrating efficient shape fixing and

41

re

-p

ro

of

recovery. (C) Behavior of SMP with poor shape fixing but efficient recovery. (D) Behavior of SMP with efficient shape fixing but poor recovery. [90], Copyright 2007. Reproduced with permission from the Royal Society of Chemistry.

Jo

ur na

lP

Fig. 5. (A) Dynamic mechanical analysis (DMA) storage modulus (a), loss modulus (b), tan δ (c), viscoelastic length transition (VLT) strain (d), and VLT stored conformational entropic energy density (e) plotted as functions of temperature during a strain-controlled DMA experiment for a polyurethane. (B) Shape memory polymer prediction plane, illustrating VLT stored entropic energy density (ΔSS) plotted as a function of VLT maximum strain (εmax) and stress at εmax (σSF at εmax) during a DMA experiment. [114], Copyright 2016. Adopted with permission form John Wiley & Sons Inc.

42

of ro -p re lP

Jo

ur na

Fig. 6. (A) Tensile failure strain near the onset of the rubbery plateau region as a function of temperature divided by rubbery modulus (ER) for tert-butyl acrylate (tBA) polymer networks crosslinked with either di(ethylene glycol) dimethacrylate (DEGDMA), poly(ethylene glycol) dimethacrylate (PEGDMA) Mn = 550, or PEGDMA Mn = 750. [126], Copyright 2008. Adopted with permission form John Wiley & Sons Inc. (B) Overlay of tensile failure strain from stress-strain experiment and storage modulus from dynamic mechanical analysis (DMA) as functions of temperature for tBA polymer networks crosslinked with PEGDMA (20 wt%). [121], Copyright 2008. Adopted with permission form John Wiley & Sons Inc. (C) Schematic depiction of tensile stress-strain behavior of amorphous crosslinked polymers as a function of temperature or strain rate, illustrating the failure-envelope. [128], Copyright 1963. Adopted with permission form John Wiley & Sons Inc.

43

of ro -p re

Jo

ur na

lP

Fig. 7. Voigt model of viscoelasticity (spring of modulus E in parallel with dashpot with viscosity η), illustrating (A) conformational entropic energy losses from chain scission/slippage (spring fracture) compromises shape recoverability, whereas (B) energy dissipation due to viscoelastic effects when deforming near Tg does not. Ratio of E/η determines the shape recovery time.

44

of ro -p re

Jo

ur na

lP

Fig. 8. Graphic depiction of physical repair mechanisms driven by (A) interfacial flow and (B) shape memory. [60], Copyright 2015. Reproduced with permssion from Elsevier Science Ltd. (C) Optical microscope images of shape memory driven wound clsosure of a polyurethane film. [45], Copyright 2017. Adapted with permission from the Royal Society of Chemistry.

45

lP

re

-p

ro

of

Fig. 9. Optical images of neat epoxy and epoxy-graphene composite shape memory materials, following scratch damage and subsequent recovery after heating to 90 ⁰C (above Tg). [68], Copyright 2010. Adopted with permission from the Royal Society of Chemistry.

Jo

ur na

Fig. 10. Self-healing of indentations by SME. (A) Atomic force microscopy (AFM) tapping mode images of an indentation in a poly(tert-butyl acrylate) film, illustrating the shape recovery process upon gradual heating from temperature to Tg at a heating rate of 1 oC/min (scan rate = 2 Hz or 1 min/frame). [67], Copyright 2007. Adapted with permission from Elsevier Science Ltd. (B) Three-dimensional (I) and two-dimensional (II) surface scanning images of indentation recovery in a polyurethane SMP film (film thickness = 170 nm; Berkovich diamond indenter). [149], Copyright 2010. Reproduced with permission from Elsevier Science Ltd. (C) AFM images through recovery of nanoscale indents in an epoxy during heating from room temperature to Tg (shape recovery shown for indents corresponding to loading forces of 7.2 µN) [66], Copyright 2005. Adopted with permission from the American Institute of Physics.

46

of ro -p re lP ur na Jo

Fig. 11. Shape memory facilitated repair. (A) Semi-interpenetrating network (SIPN) of linear poly(εcaprolactone crosslinked) (PCL) inside a crosslinked PCL network, illustrating edge crack closure and re-bonding upon heating (above Tm) following notch damage and stretching. [69], Copyright 2011. Adopted with permission from the American Chemical Society. (B-1) Optical images of polydisulfide networks, which shape recover upon heating to 80 ⁰C (above Tm) to close/seal the wound following deep scratch damage and heal upon exposure to UV light. (B-2) Stress-strain curves for undamaged, pre-strained, scratched, sealed, and healed polydisulfide films. [35], Copyright 2013. Adopted with permission from the American Chemical Society. (C) Optical images and recovery of mechanical properties following scratch damage as a function of healing time at 65 ⁰C (above Tm) for PCL-based heterogeneous thermoplastic polyurethane fibers, pulled from solution during polymerization (C1 

47

lP

re

-p

ro

of

PURP) and pulled from melt following polymerization (C1  PURM). [71], Copyright 2018. Adopted with permission from Elsevier Sciences Ltd.

Jo

ur na

Fig. 12. Pictorial representation of the proposed five stages of healing, showing the interface and molecular views: (1) rearrangement, (2) surface approach, (3) wetting, (4) diffusion, (5) diffusion and randomization. [159], Copyright 2008. Adopted with permission from Elsevier Science Ltd.

48