The dawn of unmanned analytical laboratories

Trends in Analytical Chemistry 88 (2017) 41e52

Contents lists available at ScienceDirect

Trends in Analytical Chemistry journal homepage: www.elsevier.com/locate/trac

The dawn of unmanned analytical laboratories Gurpur Rakesh D. Prabhu, Pawel L. Urban* Department of Applied Chemistry, National Chiao Tung University, 1001 University Rd., Hsinchu, 300, Taiwan

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 28 December 2016

The twentieth century has brought enormous developments in instrumentation for chemical analysis. However, most of the state-of-the-art analytical instruments still require a substantial investment of human labor. In the twenty first century, some laboratories need to analyze thousands of samples every day, at the same time maintaining high repeatability. To cope with the growing requirements of modern science and industry, it is necessary to automate most or the entire sample handling steps. Thus, a number of automated analytical approaches have been developed, including: continuous flow analysis, flow injection analysis, microfluidic systems, micro total analysis systems, microtiter plate-compatible systems, centrifugal analyzers, cartridge analyzers, autosamplers, multi-axis robots, and total laboratory automation facilities. Automation of chemical analyses speeds up tedious operations related to the handling of samples and reagents, thus creating new possibilities for science and industry. However, when automating analytical laboratories, socioeconomic and safety aspects need to be taken into account. © 2016 Elsevier B.V. All rights reserved.

Keywords: Automated assay Flow techniques High throughput Microfluidics Robotics

1. Introduction Development of early robot-like systems dates back to the 15th century [1,2]. A few centuries later, the Industrial Revolution triggered a chain of events that led to the replacement of humans in repetitive and dangerous jobs with machines [3]. Notably, the term
robot was coined by a Czech writer Karel Capek in his drama entitled Rossum's Universal Robots [4]. During the New York World's Fair organized in 1939, the Westinghouse company presented a prototype of its robot e Elektro e the Moto-Man [5]. The apparent humanoid characteristics of Elektro attracted attention of the broader public. The high expectations about the potential capabilities of robotic systems are reflected in the movies from the inter-war period, including the famous Modern Times (from 1936), starring Charlie Chaplin. Automation was quickly adopted by industry. The interest in automation was motivated by the economic factors, the anticipated reduction of labor costs, and scaling up the production. Since then automation has entered almost all areas of human life. Nowadays, it is impossible to imagine further progress of human civilization without highly mechanized production lines. However, in the post-war era, there was still a long way from the automation of food production to the automation of chemistry procedures

* Corresponding author. E-mail address: [email protected] (P.L. Urban). http://dx.doi.org/10.1016/j.trac.2016.12.011 0165-9936/© 2016 Elsevier B.V. All rights reserved.

(Fig. 1). Thus, the work of chemists remained manual during a large part of the 20th century. Among all branches of chemistry, analytical chemistry is the one that relies most on technology. Automated systems for chemical analysis were pioneered by many ingenious individuals, including Leonard Skeggs [6,7], Jaromír R u
zi
cka, Elo Hansen [8], Marek Trojanowicz [9,10], Masahide Sasaki [11,12], Rodney Markin [13,14], Janusz Pawliszyn [15,16], Miguel rcel and María Dolores Luque de Castro [17], to name just a Valca few. The early attempts of automation in chemistry typically meant mechanization of singular tasks such as pipetting, centrifugation, mixing, and on-line detection of chemical species. In fact, automation was featured in the chemical literature already in the middle of the 19th century (see the review by Olsen [18]). It took several decades to develop flexible multi-functional automation tools compatible with the chemical laboratory environment. In the 1980s, automation was already recognized as an important aspect of analytical chemistry [17,19]. Nowadays, less or more sophisticated automated systems find applications in combinatorial chemistry, high-throughput screening, and clinical analysis. They can handle hazardous chemicals, infectious samples, and perform dangerous reactions without putting human operators at risk. Such systems also decrease the incidence of errors e even during tedious repetitive operations e thus ensuring an increased reproducibility. In the automated chemistry platforms, reaction/analysis throughput is greatly increased providing the means for screening hundreds of samples and chemical reactions in a matter of minutes.

42

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

Fig. 1. Selected milestones with direct (top) and indirect (bottom) impact on the development of automated analytical systems. (Based on the references cited in the main text, the review by Olsen [18] and the references cited therein, as well as various on-line sources.)

Automation is relevant to all chemists because the 21st century's chemistry heavily relies on automation. While organic chemists are dreaming about a machine that could synthesize any compound [20], some chemistry areas could develop rapidly mainly because of the use of automation. The areas of analytical chemistry that require automation include: genome sequencing, microarray technology, difficult sample preparation, as well as chemical analysis of large numbers of microscale samples. Notably, automated sample preparation procedures are not only faster but also more accurate than manual procedures [21]. The aim of this review is to: (i) equip analytical chemists with background knowledge on the concept of automation; (ii) encourage them to implement automation in laboratory procedures; (iii) instruct them how they could implement elements of automation in their work without making considerable investments in the hardware. While the analytical chemistry literature is rich in excellent examples of automated techniques, we apologize those inventors whose brilliant work has not been mentioned in this short article due to the space constraints. The readers who are interested in specific aspects of chemistry automation are also encouraged to read the informative reviews published during the past two decades (e.g., [18,22e27]) and the older comprehensive monographs [17,19]. 2. Main approaches to automation Automated analytical equipment can be either standalone or integrated with other instruments (e.g., chromatographs, spectrometers) [21]. The former approach provides the benefit that the costly instruments are not occupied as the samples are prepared for analysis. The latter approach eliminates the intervention of analyst when the prepared samples are transferred to the detection system. The choice of proper sample preparation greatly affects the quality of the obtained data [21]. The key component of every automation equipment is the system for metering and handling liquids. Accurate volume and flow rate control are critical for sample dosing, dilution, addition of derivatising reagents and internal standards. Thus, modern analytical instruments are populated with various types of pumps and valves. There exist several automation strategies (Table 1), which include: flow-based techniques; microtiter plate systems; centrifugal and cartridge analyzers; robotic systems; and complex solutions to total laboratory automation. Their operational principles are diverse. While most automated instruments follow one of these strategies, the technology used in some of the newest prototypes and commercial products extends beyond them. One of the first popular automated systems for clinical analysis was developed by Leonard Skeggs in the 1950s, and termed continuous flow analysis (CFA; Fig. 2A) [6]. Although it took a few

years to grab the attention of industry, this brilliant idea remained in the mainstream for decades. In CFA, sample plugs are separated with bubbles of air, and move along polymer tubing. Reagents are introduced to the sample plugs, and the chemical reactions are conducted as the train of plugs advances from the tube inlet toward a detector. In the 1950s, CFA provided unprecedented repeatability. Variability of the analytical results no longer depended on sample processing by human analysts. Following the success of CFA, R u
zi
cka and Hansen [8] introduced and popularized flow-injection analysis (FIA). Unlike CFA, FIA does not use plugs of gas to separate sample segments. The samples are injected to a thin tubing (typically, ID < 1 mm), and driven toward a detector. Reagents are added to the stream of a carrier fluid in mixing tees, or simply spiked to the carrier fluid (cf., [9,10]) FIA greatly improved analytical robustness. It eliminated the drawbacks of injecting a compressible medium (gas) to the flow line. It provided enormous flexibility to the design of automated and sensitive assays. The high performance of CFA and FIA raised the interest of the engineers focused on miniaturization. Attempts were made to scale down the FIA manifolds, and incorporate the fluid channels into compact monolithic blocks. These activities e along with the emerging miniaturization of gas chromatographs [28] e eventually led to the development of microfluidic devices called lab-on-a-chip [29,30]. The microchips are fabricated by a variety of techniques, including photolithography, soft lithography (cf., [31,32]), and e most recently e 3D printing [33e35]. They enable precise manipulations on pico-, nano-, and micro-liter-range volumes of fluids (samples, reagents). Because the total amounts of samples required for analysis are so small, the lab-on-a-chip analytical devices provide superior mass sensitivities. Noteworthy applications of the microfluidic technology include (but are not limited to): biosensing [36], immunoassays [37], diagnosis [38], single-cell analysis [39e43], handling microscale vesicles [44e49], drug discovery procedures [50e53], and synthesis of nanoparticles [54e59]. Most importantly, microfluidic chips can readily be integrated into portable analytical equipment (Fig. 2B). For example, the heart of such a miniature analytical device can be a multi-layer polydimethylsiloxane (PDMS) chip with integrated elastomeric valves [60]. Such integrated instruments are characterized with very small footprints. They can be powered with miniature lithium batteries. Therefore, they can be used to conduct chemical analyses without direct access to proper laboratory facilities. By the late 1980s, the analytical determinative techniques used in automatic methods of analysis were mainly based on electrochemistry as well as atomic and molecular spectroscopies [17]. In the 21st century, two prominent detection platforms are mass spectrometry (MS) and nuclear magnetic resonance (NMR). Thus, microfluidic chips are sometimes coupled with sample inlets of

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

43

Table 1 Main approaches to automation of analytical procedures, and typical advantages and disadvantages. Platform

Advantages

Disadvantages

Continuous flow analysis

Schematic

- analyses conducted in a series - simplicity - few mechanical elements

- technical issues with introducing/handling gas bubbles - certain sample treatment steps cannot be implemented

Flow injection analysis

- analyses conducted in a series - small volumes of solvents/reagents consumed - small volumes of chemical waste produced

- requirement to control dispersion of sample zones - sample/reagent mixing issues - certain sample treatment steps cannot be implemented

Microfluidic systems (lab-on-a-chip)/ micro total analysis systems (mTAS)

- portability - ultra-small volumes of solvents/reagents consumed - ultra-small volumes of chemical waste produced - multiple sample treatment stages

-

Microtiter plate-compatible systems

- analyses conducted in parallel - compatible with many standard assay procedures - compatible with many conventional detection systems

- large volumes of consumable materials required (plates) - large volumes of solvents/reagents required - large volume of solid and liquid waste produced - limited flexibility

Centrifugal analyzers

- analyses conducted in parallel - reduced handling of liquids

- bulky - only for specific assays (limited flexibility) - require the use of designated cartridges with solid and liquid materials - large volume of solid and liquid waste produced

Cartridge analyzers

- all-in-one solution for a few standard assays - portability - simplicity of use

- only for specific assays (limited flexibility) - require the use of designated cartridges with solid and liquid materials - large volume of solid and liquid waste produced

Autosamplers/robotic systems with restricted movement

- relatively robust - easy to program

- mechanical complexity - limited number of analytical procedures that can be executed - bulky - involve multiple mechanical operations

Multi-axis robots

- high degree of freedom - high flexibility

-

mechanical complexity high cost safety concerns complex maintenance large size involve multiple mechanical operations

Total laboratory automation

- high throughput - flexibility for a large number of programmed procedures - reduction of personnel costs

-

high mechanical complexity high cost complex maintenance very large size involve multiple mechanical operations

mass spectrometers, enabling highly automated processing and delivery of microscale samples for MS analysis [61e63]. For example, droplet microfluidics is a convenient way to handle microliter-volume aliquots of samples in front of the MS ion source [64]. In that case, the 3D printing technique could readily be used to prototype such digital microfluidic interfaces, which can be clipped on the inlet panel of a commercial mass spectrometer (Fig. 3). Since no clean room infrastructure is required to fabricate such interfaces, they can be constructed in average chemistry laboratories to automate microliter-scale reactions and MS analyses. The early prototypes of such systems already possess numerous automation features. They are operated by touch-screen panels and remote controls. Thus, operation of such systems requires no more attention than starting a washing machine. While the three platforms mentioned above implement the concept of fluidics, another very popular format uses sub-milliliter chamber arrays to perform assay reactions. Microtiter plates are plastic blocks incorporating a large number (typically, 96) of

appropriate expertise of operators required microfabrication required maintenance issues dispersion/mixing issues clogging microchannels

addressable wells. Each well is accessed from the top [65]. Pipetting samples and reagents into the microtiter plate wells is normally followed by detection by one of several standard detection methods, such as colorimetry, absorption spectroscopy, fluorimetry, or chemiluminescence detection [66]. However, microtiter plates can also be used to deliver samples to extraction [15,16] or separation systems [67]. The samples and reagents can be transferred onto microtiter plates manually (e.g., using multi-channel micropipette), or by a robot. The microtiter plate format enables performing highly parallel assays. Therefore, this format has become very popular in the high-throughput screening procedures performed in fundamental and applied research work, for example in the area of drug discovery [68e70]. The so-called centrifugal analyzers take advantage of centrifugal force to mix samples with reagents [71e75]. They subject the processed samples to analysis by on-line visible light absorption detection. In general, customized reagent cartridges are required to perform such analyses. On the other hand, portable cartridge

44

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

Fig. 2. From old to new. (A) Prototype of the AutoAnalyzer (May 1951). Reproduced from Ref. [7]. (B) Picture of the smartphone-controlled handheld microfluidic liquid handling system. Reproduced from Ref. [60] with permission of The Royal Society of Chemistry.

analyzers are designed to enable point-of-care analysis of clinical samples [76]. Although many of these systems are based on high technology, the range of their applications is limited. Hence, they are involved in a limited number of clinical analyses. Combining centrifuge with microfluidic chips brings the centrifugal analyzer technology to a higher level [75,77]. Importantly, the microfluidic centrifugal analyzers enable handling minute volumes of samples. At the same time, one benefits from the centrifugal force for driving fluids in the channels, and circumvents the engineering issues associated with the conventional pumping systems (e.g., syringe or hydrodynamic pumps). A patent filed by George Devol [78] and the foundation of the Unimation company by Joseph Engelberger [79] initiated the fast growth of robotic technology, which occurred in the second half of the 20th century. Robots were quickly adopted by industry, especially car factories [80]. The robotic systems for chemistry come in different flavors. Autosamplers with three or more axes of movement are very popular add-ons to various separation and detection devices, including gas and liquid chromatographs, mass spectrometers, and nuclear magnetic resonance spectrometers. In the early 1980s, robots with interchangeable hands were introduced allowing development of workstations to perform programmable multistep sample manipulations [23]. Although the main function of autosamplers is to obtain an aliquot of a sample and transfer it to the inlet of the associated instrument, modern autosamplers feature various other functions. Some are capable of extracting analytes using solid-phase microextraction fibers, or to perform incubations of samples with the supplied reagents. Automated and fast XY-positioning systems are indispensable for performing laser desorption/ionization MS analyses; in particular, when analyzing hundreds of samples or imaging distributions of analytes in heterogeneous 2D specimens (e.g., tissue slices).

The mechanical operations of multi-axis robots are much less restricted than those of autosamplers. Robotic arms are capable of moving samples in the three-dimensional space. In fact, flexible robotic systems were already developed by a few teams of chemists more than three decades ago [81]. Impressively, in one early work from 1982, Owens and Eckstein [82] developed a robotic sample preparation system with five rotational degrees of freedom capable of automated weighing of solids and liquids, pH determination, dilutions, and dissolutions. This system enabled automated pH titration analysis [82]. In other more recent work focused on protein expression and purification, a robotic system was used to monitor cell growth automatically and carry out further tasks including, cell lysis and purification of expressed b-galactosidase via affinity chromatography [83]. The assays including polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and chromatin immunoprecipitation (ChIP) were robotized to support studies on the regulation of gene expression and drug discovery [84]. In fact, the drug discovery field heavily relies on automated and flexible screening systems, which can test thousands of drug candidates [85]. Robots have been customized to prepare primary samples and to deliver secondary samples for analysis by high-end instrumentation, including nuclear magnetic resonance spectrometers [86] and mass spectrometers [87e90]. In one developmental work, specimens with heterogeneous distribution of analytes on the surface were mapped by MS with the aid of a single robotic arm (Fig. 4A) [88]. According to the proposed workflow, hemispherical test specimens were sampled by the robotic arm, and the samples were delivered to the direct analysis in real time (DART) ion source. Subsequently, the chemical distribution maps were constructed based on the ion signal intensities [88]. In other cases, robotic arms were used to carry the samples through multiple stages, including sample recognition, enzymatic reaction, incubation, dilution, and addition of internal standards (Fig. 4B) [90]. While the last two reports exemplify on-line coupling of robots with mass spectrometers, robots are also readily incorporated in off-line sample preparation e for example when conducting affinity imaging mass spectrometry (AIMS) [91]. Patch-clamp is a specialized technique used to study electrical properties of single cells. It can provide insights on channel activity or neural circuits. However, its reproducibility is not yet satisfactory. Robotic patch-clamp systems can make electrode alignment with the studied cells much more precise and convenient, improving the technique's reliability [92]. Beside the laboratory-grade robotic instrumentation mentioned above, instruments (e.g., mass spectrometers) can readily be fitted into small vehicles to detect atmospheric composition, and to match the recorded data with the latitude and longitude of the sampling site [93]. Similarly, portable chromatographs and mass spectrometers are installed on board of autonomous space vehicles used to analyze specimens collected on the surface of Mars [94,95]. Ocean scientists use autonomous underwater vehicles to probe deep waters [96]. One such underwater biological laboratory could identify DNA fragments without the need to dive to collect samples [96]. In addition, drone technology is recently developed to perform in-flight assays [97]. Single or dual robotic arm systems can be a good automation solution for small research laboratories. On the other hand, large clinical laboratories are likely to invest in industry-scale total laboratory automation solutions [23]. These are bulky heavily mechanized systems e designed for performing thousands of analyses according to pre-defined procedures, using standard sets of reagents, and supplying highly reproducible results. Such large-scale automation can also reduce labor costs. This aspect is especially relevant for clinical laboratories in the highly developed countries, which suffer from the shortages of qualified technicians [11,12,23].

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

45

Fig. 3. Digital microfluidic interface for mass spectrometry: (I) Experimental setup. (A) Schematic drawing of the interface comprising a digital microfluidic chip, V-EASI source, and a mass spectrometer. (B) Photograph of the 3D-printed chassis of the interface fitted with its key elements. (II) Actuation (merging) of two droplets (each ~ 10 mL) on the microchip. Red arrow: direction of droplet movement; red dashed line: chip outline; green dashed line: electrode zone; orange dashed line: incubation zone. Scale bar: 5 mm. Reproduced from Ref. [64] with permission of The Royal Society of Chemistry.

For example, the team of Sasaki created early automated laboratory systems incorporating conveyor belts, electronic boards and robots [12]. Unmanned remote laboratories were further developed for near-patient laboratory testing at moderate laboratory staffing [23,98]. While the total laboratory automation solutions are constantly being developed e due to the limited demand, high

specialization, and cost e the progress is not as apparent (at least in the scientific literature) as in the other areas discussed here. Alternative small-scale robotic automation systems can better meet the requirements of small- or medium-sized laboratories. For instance, Choi et al. [99] designed a BioRobot platform suitable for small and medium sized hospitals, which minimizes the

46

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

Fig. 4. Automation of chemical analysis with small robotic arms. (A) Robot in the home position (a), sampling position (b), and analysis position in which the needle is placed between the DART ion source and MS gas-ion-separator (GIST) interface (c). Selected ion intensity chronogram at m/z 443 ([M
Cl]þ for rhodamine 6G) observed during the analysis sequence corresponding to the robot positions described directly above (d). Sample mass spectrum observed during analysis of rhodamine 6G colored spots (e). End effector (grey), PEEK needle mount (tan), acupuncture needle (red) and 3D visualization camera (black) (f) for point cloud generation (g). Reproduced from Ref. [88] with permission of The Royal Society of Chemistry. (B) Snapshots from the operation of RAMSAY-2 during a typical analysis involving sample recognition and pickup, addition of reagents and a solvent promoting ionization, incubation, transfer to the ion source, and data acquisition. Reprinted from Sensors and Actuators B: Chemical, 239, C.-L. Chen, T.-R. Chen, S.-H. Chiu, P. L. Urban, Dual robotic arm “production line” mass spectrometry assay guided by multiple Arduino-type microcontrollers, 608e616, Copyright (2017), with permission from Elsevier [90].

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

consumption of reagents and easily integrates various types of equipment. The prototype could accommodate 70 different clinical tests, which are frequently conducted in hospitals [99]. In the production-oriented industries, robotized analytical systems form a part of “production lines” that also incorporate other stages related to synthesis and fabrication. For instance, robotic instruments are used to conduct high-throughput measurements of various properties of chemical coatings. In this case, automation can accelerate discovery of new industrial coating products with the desired properties [100].

47

rationally optimized and reduced. Because some automation schemes enable remote sensing and sample collection, carbon dioxide footprints related to the travel of analysts can also be minimized. Thus, automated analytical systems conform with the requirements of green analytical chemistry [112e114]. They follow some of the 12 principles of green chemistry proposed by Paul Anastas and John Warner [115], and later adapted for analytical chemistry [116].

4. Prototyping small-scale automated analytical systems 3. Benefits of automation The goal of chemical laboratory automation is “to reduce the manual effort in repetitive tasks as much as possible” [101]. However, before automated solutions are introduced, a number of questions need to be asked, for example: How can automation improve laboratory services? How can it decrease operating costs? What technology should be used [14]? Undoubtedly, cost effectiveness of analytical procedures is one of the key factors to be taken into account when choosing an automation solution. Manual work and automation will dominate where they are most effective [101]. Creative tasks will remain the domain of humans, while automated systems will primarily be dedicated to repetitive tasks [101]. Automated systems can be equipped with a large number of sensors, and the future automated labs will be able to conduct experiments and record the results by themselves. Recording many details of every experiment will improve reliability of the conducted research work, and will prevent missing important observations [102]. Overall, the benefits of analytical laboratory automation include: - reducing/eliminating manual labor; - limiting exposure of humans to hazardous (e.g., toxic, explosive) environment; - reducing the risk of sample contamination (e.g., ensuring sterile conditions); - speeding up analysis process (high-throughput operation); - improving repeatability/reproducibility; - reducing expenditure of costly chemicals; - reducing production of hazardous waste; - downscaling analytical technology and bringing it to the point of application (e.g., bed side). Notably, one of the driving forces in the rapid development of automated systems was the complicated sample preparation of matrix-rich real samples. Many ingenious sample preparation procedures have been introduced (e.g., [103e106]); however, most of them are still performed by humans. Automation of analytical laboratories eliminates the human work, at the same time warranting superior accuracy and reproducibility. In particular, miniaturized analysis tools simplify various operation units (e.g., sampling, dilution, treatment, transport). In the case of radiochemical analysis, automation can greatly improve the workplace safety [107]. Because extraction is one of the most common sample preparation steps, much effort has been made to automate extraction protocols (e.g., [15,16,108e111]). An important advantage in this case is a better control of chemical use and production of waste. The actions of automated systems are perfectly predictable. Computers can track how many milliliters of every chemical are consumed every hour, and how many liters of chemical waste are produced every day. This way, the environmental impact can be accurately assessed. Likewise, energy consumption during analytical processes can be

Chemists are currently equipped with tools to develop and customize automated systems for their needs. The door to automation in chemistry has been open wide thanks to the popularization of open-source electronic modules [117,118] and 3D printing [33e35]. Widely available electronic parts are used to develop data acquisition devices [119,120] as well as instrument control systems [64,90,108e110,121e127]. Because most of the open-source electronic components are commercial off-the-shelf products, which can be purchased in local electronic shops or on the internet, chemists can readily incorporate them in small-scale automated analysis devices without incurring excessive cost. The enthusiasm of the maker DIY community and proliferation of “fab labs” around the world [128] may further fuel the development of automated systems customized for analytical chemistry. The Maker Faire and similar events organized worldwide raise interest of non-engineers in the possibilities provided by the modern prototyping technologies. Because of the low-cost and accessibility of the open-source electronic equipment, chemistry students can currently learn the principles of automation and prototype simple analytical instruments [117,118,129,130]. However, when designing and constructing prototypes of robotic systems, one should take into account the safety aspects. In general, non-industry-grade electronic modules should not be used to control critical functions of mechanized systems, so as to prevent injury of humans in case of malfunction. Recently much effort has been made toward the development of inexpensive portable medical diagnostic technologies [27]. They must be able to operate with a limited electricity supply, and should not require refrigeration. Importantly, one must be able to implement such tools without extensive training. For instance, a commercially available smartphone can fulfill the functions of a controller, analyzer, and displayer in low-cost point-of-care monitoring [131]. A related report describes a portable smartphonebased spectrophotometer characterized with performances that are comparable with those of existing bench-top spectrophotometers [132]. The use of smartphone cameras as detectors democratizes high-throughput assay technology. With simple adaptors and dedicated apps, one can read the results of microtiter plates outside the laboratory [133]. While the first-generation robots implemented in analytical chemistry were “sensorless”, the second-generation robots were already equipped with multiple sensors to communicate with the outside world [17]. Nowadays, sensing technology is playing an important role in the development of automated systems. Physical sensors are used for identification of samples. For example, samples can be identified by reading barcodes, quick response (QR) codes, or radio frequency identification (RFID) tags affixed onto sample vials (e.g., [89,90]). On the other hand, chemical sensors and biosensors can provide feedback on the sample treatment process. This information can be used to modify the subsequent analytical steps, as required. The dilution factors can be adjusted taking into account the actual concentrations of the analytes in the samples. Optical sensors are easy to fabricate using off-the-shelf parts (e.g.,

48

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

[109,125,134e136]). Such sensors can readily be operated with inexpensive electronic modules, including Arduino [117]. 5. The role of computers in automation Automation of chemical procedures would not develop fast without computer-assisted control technology, what was recognized already in the early monographs [17]. Computers provide assistance at all stages of analytical workflows e from sampling to result presentation. For example, a robotic sample preparation program (RSPP) was developed to prepare worklists for a robotic system, thus saving time and human effort in sample preparation and producing high quality data with reduced errors [137]. Programming a Robot (PaR-PaR) is a simple robot programming language that facilitates setting up robotic routines by less experienced researchers [138]. Following little training, one can independently develop complex biochemical protocols utilizing robots. Automation in sample processing and detection also needs to be supplemented by developments in the software for automated evaluation of data [139]. Specialized software can make the research datasets easier to store and query, leading to a more reproducible science [102]. Smart data treatment algorithms can replace or supplement the work of expert scientists in highthroughput screening procedures. NMR spectra are typically evaluated by synthetic chemists who look for peaks signifying formation of the hypothetical reaction products. However, this step can also be conducted by automated spectral analysis algorithms [140]. Using such algorithms, spectra are deconvoluted, and the NMR resonances are classified. This way, large sample sets can be analyzed with little involvement of expert scientists. Informaticspowered pipelines are also used to evaluate liquid chromatography mass spectrometry datasets without supervision of analysts

[141]. They can identify suboptimal instrument performance, and spot the sources of variation throughout years of data collection. On-line analysis systems can provide data on-the progress of chemical reactions. These real-time data can be used for optimization of reaction conditions [142e144]. Various detection techniques could be used in such processes, including high performance liquid chromatography, FTIR spectroscopy, fluorescence spectroscopy, gas-liquid chromatography, and NMR. The self-optimizing reaction systems are particularly compatible with flow chemistry. Using this technology, it is possible to find the optimum conditions for chemical reactions eliminating tedious planning, iterative wet chemistry steps, and data analysis [142]. Chemical evolution could also be studied with a robotic system performed in oil droplets using feedback control [145]. The digital images of such droplets were evaluated by an image recognition algorithm, and the results were used to guide the subsequent iterations of the experiment. In other work, chemical reactions were carried out in 3D-printed reactionware, and a digital feedback mechanism was used for device optimization, providing an automated chemical discovery platform [146]. 6. The impact of the internet on automation Internet supports the automation efforts in various ways. Chemical procedures can be integrated with teleinformatic networks, adding a value to the existing chemistry infrastructure. Modern software solutions provide unlimited access to the instrument control and datasets over the internet [147]. For example, a cloud-based tool named Wet Lab Accelerator (WLA) allows biologists, who are not very familiar with programming knowledge, to generate bioanalytical protocols via robotic control [148]. On the other hand, the so-called cloud chemistry refers to the idea of performing chemical

Fig. 5. The principle of cloud-chemistry experiments. At the heart of the experiments is a self-optimizing catalytic reactor, located at Nottingham University, UK. This feeds data from the in-line analysis into an algorithm to calculate new reactor parameters (for example, flow rate, temperature and so on) that are likely to give an improved yield of the desired product; in this case, a methyl ether generated by the catalytic reaction of an alcohol with dimethyl carbonate. Reprinted by permission from Macmillan Publishers Ltd: Nature Chemistry [149], copyright (2015).

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

reactions/tests in distant laboratories by implementing internetbased interfaces (Fig. 5) [149]. In an early experiment designed to prove this concept, a remote operator took control over an experiment running in another country. The results were instantly transmitted to the remote operator [149]. In that early work, voice communication between the human operators was still necessary, and the teleinformatic network (Skype) facilitated that communication. One can further imagine direct connection of automated systems integrating robotic arms, FIA, and lab-on-a-chip in one location with a “tele-chemical” control center in another location. The role of the local operator could be reduced to refilling reagents and trouble-shooting, while the remote operator would provide scientific supervision over the entire system. Internet-aided control of instrument software is already implemented to provide distant diagnostics of the instruments (e.g., mass spectrometers) by the engineers located in the instrument vendor offices. However, the onsite operators still need to take certain actions (e.g., supply test samples), so that an instrument's performance can be evaluated. It is also appealing to incorporate chemical sensors into wireless networks to perform remote analyses in the absence of human operators on site [150]. Automation creates opportunities for business. For example, the Emerald Cloud Lab is designed as an on-line shop where customers can order experiments, and retrieve the results [102,151]. It is possible to select one of a few dozen protocols. The experiments are performed on automated workstations. The users can download their confidential data sets in several formats. The data can be visualized and processed in a number of ways using a professional software platform. We recently disclosed an automated liquid-liquid extraction system, and coupled it with a mass spectrometer to conduct long-

49

term monitoring of drug dissolution process (Fig. 6) [109]. This system is autonomous. Once set up, it could perform repetitive sample collection, extraction, and analysis e without direct supervision of the analyst. Importantly, it was integrated with teleinformatic networks: it posted updates about the analysis progress on the dedicated website accessible from any place in the world; it sent regular updates to the mobile phone of the analyst in charge (by short message service enabled by the GSM network); displayed updates on an LCD screen; and generated voice messages informing the people present in the laboratory room about every step executed by this analysis system [109]. In another impressive work, Fitzpatrick et al. developed a modular software system for the monitoring and control of chemical reactions via the Internet [152]. That automated system does not only enable chemical synthesis but also reaction monitoring using mass spectrometry and optimization. 7. Conclusions, future trends, challenges In the second decade of the 21st century, it is clear that analytical chemists cannot escape from automation. Because analytical assays are omnipresent (used in medicine, environmental science, food and drug quality control), it is desirable to work on customized automation approaches, which could fit every sector. Automation of analyses in these areas speeds up tedious operations related to the handling of samples and reagents, and create new possibilities. Performing thousands of analyses every hour enables gathering enormous quantities of data. Introduction of robotic systems to analytical laboratories facilitates highthroughput analysis, increases accuracy of tests, and decreases the labor costs [23]. The obtained big data can later be used to

Fig. 6. Automated liquid-liquid extraction system hyphenated with mass spectrometer. (A) System layout. EM: electromagnet; M1-M3: microfluidic peristaltic pumps; L: laboratory-grade peristaltic pump. (B) Schematic of the electronic control unit. Reprinted from Analytica Chimica Acta, 894, K.-T. Hsieh, P.-H. Liu, P. L. Urban, Automated on-line liquid-liquid extraction system for temporal mass spectrometric analysis of dynamic samples, 35e43, Copyright (2015), with permission from Elsevier [109].

50

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

search for significant trends, and make discoveries. This way, the important information can be fished out, and relevant details will not be overlooked. Humankind has endeavored to build miniaturized machines of ever smaller size [153]. This notion has affected all areas of modern science, including analytical chemistry. The 2016 Nobel Prize in Chemistry was awarded for the development of molecular machines [153]. If this technology is developed further, one can envisage that the robotic sample processing systems will be downscaled to nanometer-range size. Although that statement sounds like science fiction today, it may be achievable following further advancements in supramolecular chemistry, nanotechnology, and electronics. The ultra-small size of automated analytical devices will create new possibilities for investigating complex but intrinsically small biological systems such as cell organelles in their native states. Automation is rapidly spreading into different spheres of human lives [154]. Although one of the goals of laboratory automation is to have a “perfect laboratory”, the dark sides of the automation trend cannot be unnoticed. In fact, the concerns about security and integrity of automated systems were already expressed in the mid1980s [101] but they have not been addressed to a great extent till now. For example, when using the internet-based instrument control and data processing, one needs to ensure adequate protection of the datasets. Especially when handling clinical data, it should never be possible for a hacker to match the analytical results with the personal information of a patient. On a lighter note, the science fiction movie The Matrix shows how machines could take over humans in a dystopian future, if they outgrew human capabilities. Though the anticipated consequence of automation is exaggerated in that movie, such threats cannot be totally negated. Indeed, one of the shortcomings of automation, highlighted in the early literature, is that “the more automated the process is, the less is the contact of the chemist or worker with it” [17]. Although some quality control functions can be automated, analytical chemists must always be in control of the outcomes of chemical analysis, and never blame the machines for the failure. On the other hand, the lack of qualified people who could design and implement automated laboratory systems, feared three decades ago [101], does not seem to be an obstacle anymore. The open-source electronic modules can be handled even by chemists who do not normally have extensive background in electronics. However, automation of chemical analysis may pose socioeconomic challenges. According to a report by the Oxford Martin School, automation will threaten the job market to a higher extent in the most populated countries (China, India) than the less populated ones [155,156]. Therefore, one also needs to consider the social and economic impacts of automation within the sector of chemical analysis. In our opinion, the low- and medium-skill jobs may be affected to the greatest extent, while PhD analysts will still be required to oversee the operations of highly automated laboratories. Acknowledgements We acknowledge the Ministry of Science and Technology, Taiwan (MOST 104-2628-M-009-003-MY4) for the financial support. References [1] M.E. Rosheim, Leonardo's Lost Robots, Springer, Heidelberg, 2006. [2] M.E. Moran, The da Vinci robot, J. Endourol. 20 (2006) 986e990. [3] F.C. Mills, Introduction to “mechanization in industry”, in: H. Jerome (Editor), Mechanization in Industry, NBER, Cambridge, 1934.
[4] K. Capek, Rossum's Universal Robots, Aventinum, Praha, 1920.

[5] The Atlantic Website, The 1939 New York World’s Fair, http://www. theatlantic.com/photo/2013/11/the-1939-new-york-worlds-fair/100620/. (Accessed 1 October 2016). [6] L.T. Skeggs Jr., An automatic method for colorimetric analysis, Am. J. Clin. Pathol. 28 (1957) 311e322. [7] R. Stanley, A multidimensional approach to analytical science from invention and prototype to business on an international scale (1939e1976), J. Autom. Chem. 6 (1984) 175e185. [8] J. R u
zi
cka, E. Hansen, Flow Injection Analysis, second ed., Wiley, New York, 1988. [9] M. Trojanowicz (Editor), Advances in Flow Analysis, Wiley, Weinheim, 2008.  ska, Recent advances in flow injection analysis, [10] M. Trojanowicz, K. Kołacin Analyst 141 (2016) 2085e2139. [11] M. Sasaki, T. Kageoka, K. Ogura, H. Kataoka, T. Ueta, S. Sugihara, Total laboratory automation in Japan: past, present and the future, Clin. Chim. Acta 278 (1998) 217e227. [12] R.A. Felder, The clinical chemist: Masahide Sasaki, MD, PhD (August 27, 1933-September 23, 2005), Clin. Chem. 52 (2006) 791e792. [13] R.S. Markin, M. Sasaki, A laboratory automation platform: the next robotic step, Med. Lab. Obs. 24 (1992) 24e29. [14] R.S. Markin, S.A. Whalen, Laboratory automation: trajectory, technology, and tactics, Clin. Chem. 46 (2000) 764e771. [15] J.P. Hutchinson, L. Setkova, J. Pawliszyn, Automation of solid-phase microextraction on a 96-well plate format, J. Chromatogr. A 1149 (2007) 127e137. [16] D. Vuckovic, E. Cudjoe, F.M. Musteata, J. Pawliszyn, Automated solid-phase microextraction and thin-film microextraction for high-throughput analysis of biological fluids and ligand-receptor binding studies, Nat. Protoc. 5 (2010) 140e161. [17] M. Valc arcel, M.D. Luque de Castro, Automatic Methods of Analysis, vol. 9, 1988. Series: Techniques and Instrumentation in Analytical Chemistry, New York. [18] K. Olsen, The first 110 years of laboratory automation: technologies, applications, and the creative scientist, J. Lab. Autom. 17 (2012) 469e480. , G. Ramis, An Introduction to Laboratory Automation, Wiley, New [19] V. Cerda York, 1990. [20] M. Peplow, Organic synthesis: the robo-chemist, Nature 512 (2014) 20e22. [21] M. May, Automated sample preparation, Jan. 15, 2016. Science website, http://www.sciencemag.org/custom-publishing/technology-features/ automated-sample-preparation (Accessed 11 October 2016). [22] C.D. Hawker, M.R. Schlank, Development of standards for laboratory automation, Clin. Chem. 46 (2000) 746e750. [23] J. Boyd, Robotic laboratory automation, Science 295 (2002) 517e518. [24] N. Blow, Lab automation: tales along the road to automation, Nat. Methods 5 (2008) 109e112. [25] G.S. Streitberg, P.T. Bwititi, L. Angel, K.A. Sikaris, Automation and expert systems in a core clinical chemistry laboratory, J. Lab. Autom. 14 (2009) 94e105. [26] D.A. Armbruster, D.R. Overcash, J. Reyes, Clinical chemistry laboratory automation in the 21st century e amat victoria curam (victory loves careful preparation), Clin. Biochem. Rev. 35 (2014) 143e153. [27] P.B. Lillehoj, New developments in global health technologies, J. Lab. Autom. 19 (2014) 223e224. [28] S.C. Terry, J.H. Jerman, J.B. Angell, A gas chromatographic air analyzer fabricated on a silicon wafer, IEEE Trans. Electron Dev. 26 (1979) 1880e1886. [29] D. Janasek, J. Franzke, A. Manz, Scaling and the design of miniaturized chemical-analysis systems, Nature 442 (2006) 374e380. [30] D. Mark, S. Haeberle, G. Roth, F. von Stetten, R. Zengerle, Microfluidic lab-ona-chip platforms: requirements, characteristics and applications, Chem. Soc. Rev. 39 (2010) 1153e1182. [31] Y. Xia, G.M. Whitesides, Soft lithography, Angew. Chem. Int. Ed. 37 (1998) 550e575. [32] Y. Temiz, R.D. Lovchik, G.V. Kaigala, E. Delamarche, Lab-on-a-chip devices How to close and plug the lab? 132 (2015) 156e175. [33] B.C. Gross, J.L. Erkal, S.Y. Lockwood, C. Chen, D.M. Spence, Evaluation of 3D printing and its potential impact on biotechnology and the chemical sciences, Anal. Chem. 86 (2014) 3240e3253. [34] H. Kodama, Automatic method for fabricating a three-dimensional plastic model with photo-hardening polymer, Rev. Sci. Instrum. 52 (1981) 1770e1773. [35] B. Gross, S.Y. Lockwood, D.M. Spence, Recent advances in analytical chemistry by 3D printing, Anal. Chem. (2016), http://dx.doi.org/10.1021/ acs.analchem.6b04344. €nsson, S. Senkbeil, J.P. Kutter, Recent advances in lab-on-a[36] J.P. Lafleur, A. Jo chip for biosensing applications, Biosens. Bioelectron. 76 (2016) 213e233. [37] K.N. Han, C.A. Li, G.H. Seong, Microfluidic chips for immunoassays, Annu. Rev. Anal. Chem. 6 (2013) 119e141. [38] L. Ying, Q. Wang, Microfluidic chip-based technologies: emerging platforms for cancer diagnosis, BMC Biotechnol. 13 (2013) 76. [39] C.T. Culbertson, Single cell analysis on microfluidic devices, Methods Mol. Biol. 339 (2006) 203e216. [40] A. Amantonico, P.L. Urban, R. Zenobi, Analytical techniques for single-cell metabolomics: state of the art and trends, Anal. Bioanal. Chem. 398 (2010) 2493e2504. [41] S. Hosic, S.K. Murthy, A.N. Koppes, Microfluidic sample preparation for single cell analysis, Anal. Chem. 88 (2016) 354e380.

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52 [42] A. Reece, B. Xia, Z. Jiang, B. Noren, R. McBride, J. Oakey, Microfluidic techniques for high throughput single cell analysis, Curr. Opin. Biotechnol. 40 (2016) 90e96. [43] D. Hümmer, F. Kurth, N.N. Rainer, P.S. Dittrich, Single cells in confined volumes: microchambers and microdroplets, Lab. Chip 16 (2016) 447e458. [44] G. Birnbaumer, S. Küpcü, C. Jungreuthmayer, L. Richter, K. Vorauer-Uhl, A. Wagner, C. Valenta, U. Sleytr, P. Ertl, Rapid liposome quality assessment using a lab-on-a-chip, Lab. Chip 11 (2011) 2753e2762. [45] H. Nuss, C. Chevallard, P. Guenoun, F. Malloggi, Microfluidic trap-and-release system for lab-on-a-chip-based studies on giant vesicles, Lab. Chip 12 (2012) 5257e5261. [46] D. van Swaay, A. deMello, Microfluidic methods for forming liposomes, Lab. Chip 13 (2013) 752e767. [47] T. Robinson, P.E. Verboket, K. Eyer, P.S. Dittrich, Controllable electrofusion of lipid vesicles: initiation and analysis of reactions within biomimetic containers, Lab. Chip 14 (2014) 2852e2859. [48] L. Lu, J.W. Schertzer, P.R. Chiarot, Continuous microfluidic fabrication of synthetic asymmetric vesicles, Lab. Chip 15 (2015) 3591e3599. [49] D. Carugo, E. Bottaro, J. Owen, E. Stride, C. Nastruzzi, Liposome production by microfluidics: potential and limiting factors, Sci. Rep. 6 (2016) 25876. [50] P.S. Dittrich, A. Manz, Lab-on-a-chip: microfluidics in drug discovery, Nat. Rev. Drug Discov. 5 (2006) 210e218. [51] P. Neu
zil, S. Giselbrecht, K. L€ ange, T.J. Huang, A. Manz, Revisiting lab-on-achip technology for drug discovery, Nat. Rev. Drug Discov. 11 (2012) 620e632. [52] C.Y. Chan, P.-H. Huang, F. Guo, X. Ding, V. Kapur, J.D. Mai, P.K. Yuen, T.J. Huang, Accelerating drug discovery via organs-on-chips, Lab. Chip 13 (2013) 4697e4710. [53] N. Shembekar, C. Chaipan, R. Utharala, C.A. Merten, Droplet-based microfluidics in drug discovery, transcriptomics and high-throughput molecular genetics, Lab. Chip 16 (2016) 1314e1331. [54] I. Shestopalov, J.D. Tice, R.F. Ismagilov, Multi-step synthesis of nanoparticles performed on millisecond time scale in a microfluidic droplet-based system, Lab. Chip 4 (2004) 316e321. [55] S. Krishnadasan, R.J.C. Brown, A.J. deMello, J.C. deMello, Intelligent routes to the controlled synthesis of nanoparticles, Lab. Chip 7 (2007) 1434e1441. [56] L. Frenz, A.E. Harrak, M. Pauly, S.B. Colin, A.D. Griffiths, J.-C. Baret, Dropletbased microreactors for the synthesis of magnetic iron oxide nanoparticles, Angew. Chem. Int. Ed. Engl. 47 (2008) 6817e6820. [57] L.L. Lazarus, A.S.-J. Yang, S. Chu, R.L. Brutchey, N. Malmstadt, Flow-focused synthesis of monodisperse gold nanoparticles using ionic liquids on a microfluidic platform, Lab. Chip 10 (2010) 3377e3379. [58] J.B. Wacker, I. Lignos, V.K. Parashar, M.A.M. Gijs, Controlled synthesis of fluorescent silica nanoparticles inside microfluidic droplets, Lab. Chip 12 (2012) 3111e3116. [59] L. Capretto, D. Carugo, S. Mazzitelli, C. Nastruzzi, X. Zhang, Microfluidic and lab-on-a-chip preparation routes for organic nanoparticles and vesicular systems for nanomedicine applications, Adv. Drug Deliv. Rev. 65 (2013) 1496e1532. [60] B. Li, L. Li, A. Guan, Q. Dong, K. Ruan, R. Hu, Z. Li, A smartphone controlled handheld microfluidic liquid handling system, Lab. Chip 14 (2014) 4085e4092. [61] D. Gao, H. Liu, Y. Jiang, J.-M. Lin, Recent advances in microfluidics combined with mass spectrometry: technologies and applications, Lab. Chip 13 (2013) 3309e3322. [62] A. Oedit, P. Vulto, R. Ramautar, P.W. Lindenburg, T. Hankemeier, Lab-on-aChip hyphenation with mass spectrometry: strategies for bioanalytical applications, Curr. Opin. Biotechnol. 31 (2015) 79e85. [63] P.L. Urban, Y.-C. Chen, Y.-S. Wang, Time-resolved Mass Spectrometry: from Concept to Applications, Wiley, Chichester, 2016. [64] J.-B. Hu, T.-R. Chen, C.-H. Chang, J.-Y. Cheng, Y.-C. Chen, P.L. Urban, A compact 3D-printed interface for coupling open digital microchips with Venturi easy ambient sonic-spray ionization mass spectrometry, Analyst 140 (2015) 1495e1501. tsy, The use of spiral loops in serological and virological micro[65] G. Taka methods, Acta Microbiol. Immunol. Hung 3 (1955) 191e202. [66] K. L. Cox, V. Devanarayan, A. Kriauciunas, J. Manetta, C. Montrose, S. Sittampalam, Assay Guidance Manual, https://www.ncbi.nlm.nih.gov/books/ NBK92434/. (Accessed 16 October 2016). [67] X. Gong, E.S. Yeung, An absorption detection approach for multiplexed capillary electrophoresis using a linear photodiode array, Anal. Chem. 71 (1999) 4989e4996. [68] C.L. Crespi, V.P. Miller, B.W. Penman, Microtiter plate assays for inhibition of human, drug-metabolizing cytochromes P450, Anal. Biochem. 248 (1997) 188e190. [69] Y. Naritomi, Y. Teramura, S. Terashita, A. Kagayama, Utility of microtiter plate assays for human cytochrome P450 inhibition studies in drug discovery: application of simple method for detecting quasi-irreversible and irreversible inhibitors, Drug. Metab. Pharmacokinet. 19 (2004) 55e61.  ski, M. Markowicz, E. Mikiciuk-Olasik, Adaptation of high[70] P. Szyman throughput screening in drug discovery e toxicological screening tests, Int. J. Mol. Sci. 13 (2012) 427e452. [71] N.G. Anderson, Computer interfaced fast analyzers, Science 166 (1969) 317e324.

51

[72] N.G. Anderson, Analytical techniques for cell fractions: XII. A multiple-cuvet rotor for a new microanalytical system, Anal. Biochem. 28 (1969) 545e562. [73] C.A. Burtis, J.C. Mailen, W.F. Johnson, C.D. Scott, T.O. Tiffany, N.G. Anderson, Development of a miniature fast analyzer, Clin. Chem. 18 (1972) 753e761. [74] R. Gorkin, J. Park, J. Siegrist, M. Amasia, B.S. Lee, J.-M. Park, J. Kim, H. Kim, M. Madou, Y.-K. Cho, Centrifugal microfluidics for biomedical applications, Lab. Chip 10 (2010) 1758e1773. [75] M. Tang, G. Wang, S.-K. Kong, H.-P. Ho, A review of biomedical centrifugal microfluidic platforms, Micromachines 7 (2016) 26. [76] C.D. Chin, V. Linder, S.K. Sia, Commercialization of microfluidic point-of-care diagnostic devices, Lab. Chip 12 (2012) 2118e2134. [77] O. Strohmeier, M. Keller, F. Schwemmer, S. Zehnle, D. Mark, F. von Stetten, R. Zengerle, N. Paust, Centrifugal microfluidic platforms: advanced unit operations and applications, Chem. Soc. Rev. 44 (2015) 6187e6229. [78] G. C. Devol, 1961. Programmed article transfer. Patent No. US2988237. [79] J. Pearce, George C. Devol, Inventor of Robot Arm, Dies at 99, The New York Times, 2011. http://www.nytimes.com/2011/08/16/business/george-devoldeveloper-of-robot-arm-dies-at-99.html (Accessed 12 October 2016). [80] R. Brooks, Robots at work: toward a smarter factory, Futur 47 (2013) 24. [81] T.L. Isenhour, Robotics in the laboratory, J. Chem. Inf. Comput. Sci. 25 (1985) 292e295. [82] G.D. Owens, R.J. Eckstein, Robotic sample preparation station, Anal. Chem. 54 (1982) 2347e2351. [83] M.C. Wollerton, R. Wales, J.A. Bullock, I.R. Hudson, M. Beggs, Automation and optimization of protein expression and purification on a novel robotic platform, J. Lab. Autom. 11 (2006) 291e303. [84] W. Gaisford, Robotic liquid handling and automation in epigenetics, J. Lab. Autom. 17 (2012) 327e329. [85] K.W. Moore, R. Newman, G.K.Y. Chan, C. Leech, K. Allison, J. Coulson, P.B. Simpson, Implementation of a high specification dual-arm robotic platform to meet flexible screening needs, J. Lab. Autom. 12 (2007) 115e123. [86] M.A. Maccio, D.H. Bell, D. Davolos, Modular automation platforms: a case study of a flexible NMR sample preparation robot, J. Lab. Autom. 11 (2006) 387e398. [87] K. Kronkvist, M. Gustavsson, A.-K. Wendel, H. Jaegfeldt, Automated sample preparation for the determination of budesonide in plasma samples by liquid chromatography and tandem mass spectrometry, J. Chromatogr. A 823 (1998) 401e409. [88] R.V. Bennett, E.M. Morzan, J.O. Huckaby, M.E. Monge, H.I. Christensen, ndez, Robotic plasma probe ionization mass spectrometry (RoPPIF.M. Ferna MS) of non-planar surfaces, Analyst 139 (2014) 2658e2662. [89] S.-H. Chiu, P.L. Urban, Robotics-assisted mass spectrometry assay platform enabled by open-source electronics, Biosens. Bioelectron. 64 (2015) 260e268. [90] C.-L. Chen, T.-R. Chen, S.-H. Chiu, P.L. Urban, Dual robotic arm “production line” mass spectrometry assay guided by multiple Arduino-type microcontrollers, Sens. Actuat. B-Chem. 239 (2017) 608e616. [91] T. Yoshimi, S. Kawabata, S. Taira, A. Okuno, R. Mikawa, S. Murayama, K. Tanaka, O. Takikawa, Affinity imaging mass spectrometry (AIMS): highthroughput screening for specific small molecule interactions with frozen tissue sections, Analyst 140 (2015) 7202e7208. [92] H. Shen, Robots record brain activity inside neurons, Nature 532 (2016) 135e136. [93] P.M. Mach, E.M. McBride, Z.J. Sasiene, K.R. Brigance, S.K. Kennard, K.C. Wright, G.F. Verbeck, Vehicle-mounted portable mass spectrometry system for the covert detection via spatial analysis of clandestine methamphetamine laboratories, Anal. Chem. 87 (2015) 11501e11508. [94] M.S. Matthews, H.H. Kieffer, B.M. Jakosky, C. Snyder, Mars, University of Arizona Press, Tucson, 1992. [95] National Aeronautics and Space Administration (NASA), Curiosity Rover, http://mars.nasa.gov/msl/mission/instruments/spectrometers/sam/. (Accessed 12 October 2016). [96] R.A. Lovett, Underwater robot automates ocean testing, Nature 24 (February 2010). http://www.nature.com/news/2010/100224/full/news.2010.89.html (Accesed 12 October 2016). [97] A. Priye, S. Wong, Y. Bi, M. Carpio, J. Chang, M. Coen, D. Cope, J. Harris, J. Johnson, A. Keller, R. Lim, S. Lu, A. Millard, A. Pangelinan, N. Patel, L. Smith, K. Chan, V.M. Ugaz, Lab-on-a-Drone: toward pinpoint deployment of smartphone-enabled nucleic acid-based diagnostics for mobile health care, Anal. Chem. 88 (2016) 4651e4660. [98] R.A. Felder, J. Savory, K.S. Margrey, J.W. Holman, J.C. Boyd, Development of a robotic near patient testing laboratory, Arch. Pathol. Lab. Med. 119 (1995) 948e951. [99] B.J. Choi, S.M. Jin, S.H. Shin, J.C. Koo, S.M. Ryew, J. Kim, W.H. Son, K.T. Ahn, W. Chung, H.R. Choi, Development of flexible BioRobot platform for integrated clinical test, J. Lab. Autom. 13 (2008) 90e96. [100] T.-C. Kuo, N.A. Malvadkar, R. Drumright, R. Cesaretti, M.T. Bishop, Highthroughput industrial coatings research at the dow chemical company, ACS Comb. Sci. 18 (2016) 507e526. [101] J.G. Liscouski, Laboratory automation, J. Chem. Inf. Comput. Sci. 25 (1985) 288e292. [102] E.C. Hayden, The automated lab, Nature 516 (2014) 131e132. [103] J. Pawliszyn, H.L. Lord (Editors), Handbook of Sample Preparation, Wiley, New Jersey, 2010.

52

G.R.D. Prabhu, P.L. Urban / Trends in Analytical Chemistry 88 (2017) 41e52

[104] J. Pawliszyn (Editor), Comprehensive Sampling and Sample Preparation: Analytical Techniques for Scientists, first ed, Elsevier, Amsterdam, 2012. [105] D.M. Pavlovi c, S. Babi c, A.J.M. Horvat, M. Ka
stelan-Macan, Sample preparation in analysis of pharmaceuticals, Trends Anal. Chem. 26 (2007) 1062e1075. [106] K.D. Clark, C. Zhang, J.L. Anderson, Sample preparation for bioanalytical and pharmaceutical analysis, Anal. Chem. 88 (2016) 11262e11270. mez, M. Casas, V. Cerda, Automation of [107] Y. Fajardo, J. Avivar, L. Ferrer, E. Go radiochemical analysis by applying flow techniques to environmental samples, Trends Anal. Chem. 29 (2010) 1399e1408. [108] J.-B. Hu, S.-Y. Chen, J.-T. Wu, Y.-C. Chen, P.L. Urban, Automated system for extraction and instantaneous analysis of millimeter-sized samples, RSC Adv. 4 (2014) 10693e10701. [109] K.-T. Hsieh, P.-H. Liu, P.L. Urban, Automated on-line liquid-liquid extraction system for temporal mass spectrometric analysis of dynamic samples, Anal. Chim. Acta 894 (2015) 35e43. [110] C.-H. Chang, P.L. Urban, Fizzy extraction of volatile and semivolatile compounds into the gas phase, Anal. Chem. 88 (2016) 8735e8740.
a mkova , P. Solich, J. Sabo, Automation of [111] M. Alexovi
c, B. Horstkotte, I. Sr dispersive liquid-liquid micro extraction and related techniques. Approaches based on flow, batch, flow-batch and in syringe modes, Trends Anal. Chem. 86 (2017) 39e55. [112] S. Armenta, S. Garrigues, M. de la Guardia, Green analytical chemistry, Trends Anal. Chem. 27 (2008) 497e511.  ska, B. Zygmunt, J. Namiesnik, Green analytical [113] M. Tobiszewski, A. Mechlin chemistry in sample preparation for determination of trace organic pollutants, Trends Anal. Chem. 28 (2009) 943e951. [114] M. de la Guardia, Green analytical chemistry (Editorial), Trends Anal. Chem. 29 (2010) 577. [115] P.T. Anastas, J.C. Warner, Green Chemistry: Theory and Practice, Oxford University Press, New York, 1998. [116] A. Gałuszka, Z. Migaszewski, J. Namiesnik, The 12 principles of green analytical chemistry and the SIGNIFICANCE mnemonic of green analytical practices, Trends Anal. Chem. 50 (2013) 78e84. [117] P.L. Urban, Universal electronics for miniature and automated chemical assays, Analyst 140 (2015) 963e975. [118] P.L. Urban, Self-built labware stimulates creativity, Nature 532 (2016) 313. [119] J.P. Grinias, J.T. Whitfield, E.D. Guetschow, R.T. Kennedy, An inexpensive, open-source USB Arduino data acquisition device for chemical instrumentation, J. Chem. Educ. 93 (2016) 1316e1319. [120] G.N. Meloni, Building a microcontroller based potentiostat: an inexpensive and versatile platform for teaching electrochemistry and instrumentation, J. Chem. Educ. 93 (2016) 1320e1322. [121] H.H. See, P.C. Hauser, Automated electric-field-driven membrane extraction system coupled to liquid chromatography e mass spectrometry, Anal. Chem. 86 (2014) 8665e8670. [122] H. Ting, J.-B. Hu, K.-T. Hsieh, P.L. Urban, A pinch-valve interface for automated sampling and monitoring of dynamic processes by gas chromatography-mass spectrometry, Anal. Methods 6 (2014) 4652e4660. [123] J.-B. Hu, T.-R. Chen, Y.-C. Chen, P.L. Urban, Microcontroller-assisted compensation of adenosine triphosphate levels: instrument and method development, Sci. Rep. 5 (2015) 8135. [124] S.-Y. Chen, P.L. Urban, On-line monitoring of Soxhlet extraction by chromatography and mass spectrometry to reveal temporal extract profiles, Anal. Chim. Acta 881 (2015) 74e81. [125] K. Violaki, T. Fang, N. Mihalopoulos, R. Weber, A. Nenes, Real-time, online automated system for measurement of water-soluble reactive phosphate ions in atmospheric particles, Anal. Chem. 88 (2016) 7163e7170. [126] C.-L. Chen, K.-T. Hsieh, C.-F. Hsu, P.L. Urban, Facile multi-dimensional profiling of chemical gradients at the millimetre scale, Analyst 141 (2016) 150e156. [127] Y.-R. Tang, H.-Y. Huang, J.-B. Hu, R. Rattinam, C.-H. Li, Y.-C. Chen, P.L. Urban, Capillary hydrodynamic chromatography reveals temporal profiles of cell aggregates, Anal. Chim. Acta 910 (2016) 75e83. [128] Fab labs, https://www.fablabs.io/. (Accessed 12 October 2016). [129] P.L. Urban, Open-source electronics as a technological aid in chemical education, J. Chem. Educ. 91 (2014) 751e752. [130] L. Points, J. Grizou, L. Cronin, Robotic chemistry sets for the classroom, http:// www.rsc.org/eic/2016/07/robotic-chemistry-digital-3d-printing. (Accessed 12 December 2016). [131] D. Zhang, Q. Liu, Biosensors and bioelectronics on smartphone for portable biochemical detection, Biosens. Bioelectron. 75 (2016) 273e284.

[132] A.J. Das, A. Wahi, I. Kothari, R. Raskar, Ultra-portable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness, Sci. Rep. 6 (2016) 32504. [133] A. Berg, B. Cortazar, D. Tseng, H. Ozkan, S. Feng, Q. Wei, R.Y.-L. Chan, J. Burbano, Q. Farooqui, M. Lewinski, D.D. Carlo, O.B. Garner, A. Ozcan, Cellphone-based hand-held microplate reader for point-of-care testing of enzyme-linked immunosorbent assays, ACS Nano 9 (2015) 7857e7866. , V. Cerda , M. Pokrzywnicka, Ł. Tymecki, [134] D.J. Cocovi-Solberg, M. Miro R. Koncki, Towards the development of a miniaturized fiberless optofluidic biosensor for glucose, Talanta 96 (2012) 113e120. [135] M. Pokrzywnicka, Ł. Tymecki, R. Koncki, Low-cost optical detectors and flow systems for protein determination, Talanta 96 (2012) 121e126. [136] Ł. Tymecki, M. Rejnis, M. Pokrzywnicka, K. Strzelak, R. Koncki, Fluorimetric detector and sensor for flow analysis made of light emitting diodes, Anal. Chim. Acta 721 (2012) 92e96. [137] H. Jiang, Z. Ouyang, J. Zeng, L. Yuan, N. Zheng, M. Jemal, M.E. Arnold, A userfriendly robotic sample preparation program for fully automated biological sample pipetting and dilution to benefit the regulated bioanalysis, J. Lab. Autom. 17 (2011) 211e221. [138] G. Linshiz, N. Stawski, S. Poust, C. Bi, J.D. Keasling, N.J. Hillson, PaR-PaR laboratory automation platform, ACS Synth. Biol. 2 (2013) 216e222. [139] S.K. Suram, P.F. Newhouse, J.M. Gregoire, High throughput light absorber discovery, part 1: an algorithm for automated tauc analysis, ACS Comb. Sci. 18 (2016) 673e681. [140] M.A. Bernstein, S. Sýkora, C. Peng, A. Barba, C. Cobas, Optimization and automation of quantitative NMR data extraction, Anal. Chem. 85 (2013) 5778e5786. [141] M.S. Bereman, J. Beri, V. Sharma, C. Nathe, J. Eckels, B. MacLean, M.J. MacCoss, An automated pipeline to monitor system performance in liquid chromatography e tandem mass spectrometry proteomic experiments, J. Proteome Res. 15 (2016) 4763e4769. [142] V. Sans, L. Porwol, V. Dragone, L. Cronin, A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy, Chem. Sci. 6 (2015) 1258e1264. [143] D.C. Fabry, E. Sugiono, M. Rueping, Online monitoring and analysis for autonomous continuous flow self-optimizing reactor systems, React. Chem. Eng. 1 (2016) 129e133. [144] C. Houben, A.A. Lapkin, Automatic discovery and optimization of chemical processes, Curr. Opin. Chem. Eng. 9 (2015) 1e7. [145] J.M.P. Gutierrez, T. Hinkley, J.W. Taylor, K. Yanev, L. Cronin, Evolution of oil droplets in a chemorobotic platform, Nat. Commun. 5 (2014) 5571. [146] M.D. Symes, P.J. Kitson, J. Yan, C.J. Richmond, G.J.T. Cooper, R.W. Bowman, T. Vilbrandt, L. Cronin, Integrated 3D-printed reactionware for chemical synthesis and analysis, Nat. Chem. 4 (2012) 349e354. [147] Indigo BioAutomation, http://www.indigobio.com/ascent/. (Accessed 13 October 2016). [148] M. Bates, A.J. Berliner, J. Lachoff, P.R. Jaschke, E.S. Groban, Wet Lab Accelerator: a web-based application democratizing laboratory automation for synthetic biology, ACS Synth. Biol. (2016), http://dx.doi.org/10.1021/acssynbio.6b00108. [149] R.A. Skilton, R.A. Bourne, Z. Amara, R. Horvath, J. Jin, M.J. Scully, E. Streng, rez, N. Asfaw, G.L.P. Aydos, J. Dupont, S.L.Y. Tang, P.A. Summers, J. Wang, E. Pe G. Comak, M.W. George, M. Poliakoff, Remote-controlled experiments with cloud chemistry, Nat. Chem. 7 (2015) 1e5. [150] A. Bonastre, J.V. Capella, R. Ors, M. Peris, In-line monitoring of chemical analysis processes using Wireless Sensor Networks, Trends Anal. Chem. 34 (2012) 111e125. [151] Emerald Cloud Lab, http://emeraldcloudlab.com/. (Accessed 14 December 2016). [152] D.E. Fitzpatrick, C. Battilocchio, S.V. Ley, A novel internet-based reaction monitoring, control and autonomous self-optimization platform for chemical synthesis, Org. Process Res. Dev. 20 (2016) 386e394. [153] Nobel Prize in Chemistry 2016, Press Release, http://www.nobelprize.org/ nobel_prizes/chemistry/laureates/2016/press.html. (Accessed 13 October 2016). [154] B. Panko, Five surprising ways AI could be a part of our lives by 2030, Sep. 12, 2016. http://www.sciencemag.org/news/2016/09/five-surprising-ways-aicould-be-part-our-lives-by-2030 (Accessed 13 October 2016). [155] Technology at work v2.0, Oxford Martin School, University of Oxford, http:// www.oxfordmartin.ox.ac.uk/downloads/reports/Citi_GPS_Technology_ Work_2.pdf. (Accessed 13 October 2016). [156] C. Matacic, How to know if a robot is about to steal your job, Feb. 14, 2016. Science website, http://www.sciencemag.org/news/2016/02/how-know-ifrobot-about-steal-your-job (Accessed 13 October 2016).