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Application of an automated chemistry workstation to problems in synthetic chemistry
8
Original Research Paper
95
Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 95-105 Elsevier Science Publishers B.V., Amsterdam
Application of an automated chemistry workstation to problems in synthetic chemistry L. Andrew Corkan, Jean-Christophe
Plouvier and Jonathan S. Lindsey
Department of Chemistry, Carnegie Mellon Uniuersity, 4400 Fifth Avenue, Pittsburgh, PA 15213 (USA) (Received
29 April
1992;
accepted
6 July
1992)
Abstract
Corkan, L.A., Plouvier, J.-C. and Lindsey, J.S., 1992. Application of an automated chemistry workstation to problems chemistry. Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17: 95-105.
in synthetic
An automated chemistry workstation is applied to problems in the synthetic chemistry of porphyrins. A factorial design study (sixteen experiments, 96 data points) was performed to examine the role of catalyst and reactant concentrations on porphyrin yield. Four experiments could be scheduled to run concurrently; all sixteen experiments were completed in less than 1 day of workstation time. The response surface from this experiment shows the conditions for achieving the highest yield. A simplex optimization was performed over the same reaction space, requiring fewer experiments to arrive at the optimal reaction parameters. A strategic search was performed to screen a list of reagents for catalytic activity. The effective concentration range of each catalyst was surveyed by systematic modification of an ongoing reaction. By terminating reactions when a yield threshold was surpassed or when the entire concentration range had been spanned, compounds with catalytic activity and their effective concentration ranges were identified with minimal experimentation. A new scientific finding was made concerning the catalytic activity of methanesulfonic acid in the porphyrin condensation. Automated chemistry workstations of this type should yield rapid accelerations in scientific research.
INTRODUCTION
Automation systems with the capability to work exhaustively, precisely, strategically, and autonomously will provide the scientist with a powerful tool for tackling difficult scientific problems. Toward this goal, we have sought to unify mi-
Correspondence to: Dr. J.S. Lindsey, Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213 (USA).
0925.5281/92/$05.00
0 1992 - El sevier Science
Publishers
croscale synthetic chemistry with analytical chemistry in a workstation equipped with a powerful experiment manager software package [1,2]. For automated experimentation to be successful, the reaction, analytical, and sample-handling capabilities of the workstation must be matched with the scientific problem [3]. This paper describes applications of the workstation in the synthetic chemistry of porphyrins, including a factorial design study of reaction conditions, a simplex optimization of reaction conditions, and a strategic search for effective condensation catalysts. These com-
B.V. All rights
reserved
L.A. Corkan et al. /Lab.
96
1. Porphyrinogen
Self-Assembly
Ar
H+ -
AI\
2. Porphyrinogen
.H
A/‘H
H
Oxidation
k
n
Fig. 1. Porphyrin reaction scheme. Benzaldehyde (1) and pyrrole (2) react under acid catalysis to form linear polypyrromethanes (not shown) and the porphyrinogen (3). Upon addition of an oxidant such as 2,3-dichloro-5,6dicyano-benzoquinone (DDQ, 41, the porphyrinogen is converted to the porphyrin (5) and the polypyrromethanes are converted to polypyrromethenes (not shown).
plementary experiments enable aggressive attacks on scientific problems.
MATCHING TION
THE
CHEMISTRY
AND
THE
WORKSTA-
Our domain of interest involves the fundamental chemistry of self-assembling systems [41. A self-assembly process occurs as the first step in a mild synthetic strategy for preparing tetraphenylporphyrins (Fig. 1) [5,6]. In this synthesis, the room temperature acid-catalyzed reaction of four aldehyde (1)and four pyrrole (2) molecules results in self-assembly of the porphyrinogen (3). Upon addition of an oxidant such as DDQ (4), the porphyrinogen is converted to the porphyrin (5). This convergent two-step strategy is exemplary of a trend in synthetic chemistry toward performing multiple processes in a single flask, thereby minimizing workup procedures in an overall synthetic plan. The complexity of multi-
In& Manage. I7 (1992) 95-105 /Original Research Paper
??
step reactions places a premium on precise control over numerous experimental parameters. Important research issues in the synthetic chemistry of porphyrins include reaction mechanisms, the efficacy of various catalysts and oxidants, and the search for improved reaction pathways in those instances where the reaction fails [5,6]. In this domain absorption spectroscopy and thin-layer chromatography (TLC) are ideal analytical tools, and the chemistry can be carried out at the microscale level [7-91. Using the first-generation workstation, 24 data points concerning benzaldehyde, byproducts, and porphyrin were collected from a single l-h microscale reaction [71. This is a relatively clean chemistry domain because precipitation does not occur, the solutions are nonviscous, and absorption spectroscopy can be used for analysis. High-quality investigations of self-assembling processes require precise manipulations, but because preparative purifications are not necessary, the reactions need be no larger in scale than is required for compatibility with the analytical instruments and the capabilities of the sample-handling system [3]. Accordingly, the reactions can be performed at the microscale level. Microscale chemistry has several advantages. First, a large number of reactors can be contained in a small space, facilitating robotic manipulations. Second, precious resources are used in a parsimonious manner, minimizing financial costs and environmental impact. Microscale chemistry, however, places high demands on the sample manipulation system, since reagents, catalysts, and reaction samples of l-50 ~1 must be manipulated precisely without sample carryover. Quality sample-handling depends on the accuracy of the syringe system, the reproducibility of the sampling and the integrity of the sample during transfers. The sample manipulator of the automated chemistry workstation [l] has a precision of *2.5% over the range of lo-500 ~1 and + 4% upon going to 1.0 ~1 in handling methylene chloride solutions. Minimizing sample carryover is of critical importance for quantitative measurements in porphyrin chemistry, since even 0.1 ~1 residual acid could foul the absorption spectral analysis of a porphyrin solution. There was no
??
L.A. Corkan et al. /Lab.
If:
Manage. 17 (1992) 95-105/Original
detectable sample carryover as determined by experiments with transfers of tetraphenylporphyrin solutions in methylene chloride. Evaporation can present serious problems with microscale quantities of volatile solvents, especially since the vessels and vials are not hermetically sealed. Evaporation of methylene chloride (b.p. 39°C) from the reaction vessels (45 pi/h at room temperature) is inconsequential for the typical lo-ml reaction volumes and short reaction times. Evaporation from the dilution vials (100 pi/h at room temperature) could cause problems in protracted experimental procedures, but invariably the analysis procedures are completed within 10 min. Careful attention to these and related sample-handling issues enables quantitative experimentation at the microscale level.
A FACTORIAL
DESIGN
Research Paper
97
the acid catalyst concentration. Thus a desirable experiment is to monitor the rate of porphyrin formation as a function of reactant concentrations and as a function of acid catalyst concentration. A 4 X 4 factorial design experiment was planned over the dimensions of reactant concentration and acid catalyst concentration, using a twenty-fold range in concentration (5, 13.6, 36.8, and 100 mM). The sixteen experiments were grouped into four sets of four, with the catalyst concentration varied within each set and the reagent concentration varied from set to set. Because the solvent and reagents do not react until the catalyst is added, the solvent and reagents can be added in a batch mode using the AutoPreparation Table (Fig. 2). Accessing the reagent database, the reagent’s name, density and formula weight are entered, and the reagent is assigned a vial number that identifies its location in the workstation. In the Auto-Preparation Table the total solvent volume for each reaction vessel (10 ml in this case) and the amount of pyrrole and benzaldehyde for each reaction vessel are specified (Fig. 2). The event programming menus are then used to compose a protocol for adding catalyst (trifluoroacetic acid, TFA) and monitoring the reaction
EXPERIMENT
In the porphyrin reaction the formation of the porphyrinogen involves competition between cyclization and polymerization. Consequently, the yield of porphyrin (derived from porphyrinogen) is sensitive to the pyrrole and benzaldehyde concentrations. The reaction rate is influenced by the concentration of reactants and, in addition, by
<<< Auto-Pm
= = Rsag~ntRolusnt I:= JIM nt Uol- fi I PYR&E .......... ................... f #ZlILDEtlYDE d 5
ration I&mu >>> ReactYon Uemel e -.
................. ................. ................. .................
.................................. ................. :! ................. ................. :3 ................. ................. 15' ................. L i;i;;,i.ir;iT.;i.k :
Total lbm Uolum ... I lkthylm.Cl....... ................. f .................
<< T&agentData
>>
Dm8.: 8.978 Plw < ml>: 67.890 c : 1_45e+ew Rem ent Cont. I( : 6.me-883
+
<< Commands >>
~+&D ml1 tP31 w31 [PII IKI cP61 tF71 w81 [kc1
Select Ualue Hslp Reagent Cone. nmagent Uolum Solvent Uolum Define Reagent Define Solusnt load Pile saue Pile Exit
Fig. 2. Auto-Preparation Table for the factorial design experiment. The reaction vessels form the rows and the reagents and solvents form the columns of the spreadsheet. Reaction vessels l-5 are displayed; vessels lo-15 are displayed upon scrolling to the right. The reagent database (upper right corner) enables quantities of reagents to be specified by entering a concentration value; for a solvent volume the corresponding reagent volume is calculated. The total reaction volume is specified and the amount to be delivered is calculated assuming additivity of volumes.
L.A. Corkan et al. /Lab.
98
at geometrically staggered times (At = 15, 30, 60, 120, 240 min) relative to the finish time of the catalyst addition (see Event List 1). The event ‘UV-Vis of a reaction vessel (w/ dilution)’ performs a DDQ oxidation of a reaction sample prior to absorption spectrometric analysis. The event consists of: (1) addition of 600 ~1 of DDQ solution (0.01 M in toluene) to a dilution vial (equipped with a stir bar) by the product dilution station, (2) transfer of a 25-~1 sample from a reaction vessel into the DDQ solution (75 s); (3) stirring for 65 s while the sample manipulator is flushed; (4) transfer of a 25-~1 aliquot to the spectrometer cuvette containing 2.0 ml CH,Cl,-ethanol 3: 1 (50 s); and (5) collection of the absorption spectrum. For quantitative purposes the total dilution of the reaction sample (6.67 X 10W4) is displayed. This event list is then modified (employing reaction vessels 2, 3, and 4; TFA 10.5 ~1, 28.4 ~1, 77.0 ~1; altered start times) for reactions 2, 3, and 4, respectively. A new list is stored for each experiment. Next, the four event lists are scheduled for parallel implementation. The scheduling algorithm merges the four event lists so that the reactions are performed concurrently, maintaining the relative times of procedural events for a given reaction unchanged from the original event list (Fig. 3). The composite schedule of four event lists executes in 327.40 min, compared with 992.88 min if the four were performed serially. Including the Auto-Preparation times gives values for parallel versus serial execution of 351.26 and 1016.74 min, respectively, a 2.89-fold improvement in throughput. The total workstation time for all sixteen experiments was then only 1405.04 min (23.4 h).
EVENT
lnf Manage. 17 (1992) 95-105 /Original
0 5
.-E
c
Composite
??I
0
40
120
160 Time
II
I
200
240
II
260
320
360
Event
1. 2. 3. 4. 5. 6. 7.
Add 3.9 UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis
yl of reagent (3) analysis of reaction analysis of reaction analysis of reaction analysis of reaction analysis of reaction analysis of reaction
1, 1, 1, 1, 1, 1,
TFA 350 to 350 to 350 to 350 to 350 to 350 to
400
(min.)
Fig. 3. Scheduled timelines of four reactions in the factorial design experiment. Interleaving four experiments by varying the starting times does not alter the relative times of procedural events within a list.
Next the resources are tabulated for the Auto-Preparation Table and the four event lists (Table 1). Given that the schedule and resources are acceptable, the automated chemistry experiment is initiated by calling the command ‘Execute chemistry events’. This command prompts for the operator’s surname, which is stored with the date, time, and the computer-generated unique file name. This identification information is appended to the Log file, which serves as an electronic log book of workstation operation. The five hardware modules are initialized via the automatic start-up procedures. If no problems are encountered, a directory for data storage is created automatically, the Auto-Preparation Table is executed as a batch procedure, and then the scheduled event lists are executed. The six absorption spectra collected from each reaction provide a direct measure of the porphyrin yield over time. The three remaining sets of experiments employ the same four scheduled event lists with appropriate changes in reactant concentrations (5 mM, set 1;. . . 100 mM, set 4) in the Auto-Preparation Table. Plotting the highest yield at any time for each reaction yields a con-
LIST 1
Event No.
??
Schedule
-1111Dl
60
Research Paper
650 650 650 650 650 650
nm, nm, nm, nm, nm, nm,
to reaction 1 6.67 x 1O-4 6.67 x 10m4 6.67 x 1O-4 6.67 x 1O-4 6.67 x 1O-4 6.67 x 1O-4
Start
Finish
0.00 1.42 16.42 31.42 61.42 121.42 241.42
1.42 8.22 23.22 38.22 68.22 128.22 248.22
??
L.A. Corkan et al. /Lab.
In: Manage. 17 (1992) 95-105/Original
Research Paper
99
tour surface showing the dependence of yield on the concentrations of reactants and catalyst (Fig. 4). At the highest acid concentrations (100 mM) three of the experiments ([reactants] = 13.6, 36.8, and 100 mM) gave maximal yields at the earliest time point (0 mm). At these concentrations of catalyst the reaction is so fast that the growing in of the porphyrin cannot be resolved. With reactants at 5 mM the highest yield was obtained at the 15-min timepoint. At all lower concentrations the highest yields of porphyrin were obtained at the 4-h timepoint. These results, collected in less than 1 day of workstation time, illustrate the prowess of an automation system in rapid acquisition of scientific data.
A SIMPLEX
EXPERIMENT
The simplex algorithm provides a means for optimizing the response of several parameters TABLE
1
Resource tabulation design study
for
Reagent uolurnes (~1) 1. 13.8 2. 20.4 3. 119.8 4. 0.00 5. 0.00 6. 0.00 7. 0.00 8. 0.00 ??
??
Reaction solcents (ml) 1: 39.96 2: 0.00 3: 0.00
four
experiments
in the
factorial
9. 0.00 10. 0.00 11.0.00 12. 0.00 13. 0.00 14. 0.00 15. 0.00 16. 0.00 Hardware module solvents (ml) Dilution station: 14.40 UV-Vis: 115.0 TLC: 0.0 Carrier: 1.03
Supplies Sample vials: 0 Dilution vials: 24 TLC plates: 0 Workstation track record UV-Vis scans: 25 * Sample manipulator injections: 104 TLC scans: 0 ??
Timing (min) Auto-Preparation Event list: 327.40 Total execution:
Table: 23.86
351.26
* This value is for the reaction set at 5X 10e3 concentration. ** 24 UV-Vis scans and one reference scan.
M reagent
Fig. 4. Response surface of the factorial design experiment. The highest yields obtained at any time during each experiment are plotted to form the response surface. The contour graph on the top was inferred from the response surface by the minimum curvature method of the Surfer graphics package [lo].
[ll]. Though the factors influencing the course of a reaction can be investigated exhaustively through the use of factorial design experiments, the evolutionary nature of simplex experimentation yields a more focused attainment of the optimal combination of parameters. The experimental procedure used in the factorial design experiment can be modified for use as a template in a simplex search. The parameters that must be specified include the experimental template (event list), the method of computing the objective function (R), the CMS options, the initial simplex points, and the stop criteria. The event list and the Auto-Preparation Table used in the factorial design experiment are modified to form the experimental template for the simplex search. The Auto-Preparation Table holds the reagent and acid catalyst concentrations for the reactions that will be executed to begin the simplex search (the initial simplex points). As the search progresses the reagent volumes in the Auto-Preparation Table are modified corre-
100
L.A. Corkan et al. /Lab.
sponding to the new points in the search. Event list 2 is used by the simplex module as a template for analysis of the reactions. With each simplex cycle the reaction vessels and dilution station vials (used as part of the UV-Vis analysis) are incremented. Specific hooks that establish the dimensions and ranges of the simplex search must be provided to link the experimental template to the simplex module. The specification of a reagent concentration as a simplex variable is achieved by inputting the reagent number (as defined in the reagent database) in a menu for establishing simplex dimensions. In this case pyrrole is set equal to dimension 1 and trifluoroacetic acid is set equal to dimension 2. The porphyrin reaction involves condensation of four pyrrole and four benzaldehyde molecules. In this study we wish to keep the concentrations of pyrrole and benzaldehyde equal at all times, thus benzaldehyde is assigned to pyrrole as a dependent reagent with a 1: 1 ratio. This guarantees that equimolar benzaldehyde and pyrrole concentrations will function as one simplex dimension. Usually the range of each dimension must also be defined in a menu at the same time as the dimensions are assigned (in this case 5 x 10-3-10-’ MI, but in this case the ‘Fit to boundaries’ option is turned off, rendering the range setting irrelevant. The objective of this experiment is to maximize yield, thus the objective function (R) is set equal to porphyrin % yield. Automated computation of the objective function requires the reagent concentrations that are assigned to the simplex dimensions, the stoichiometric ratio of these reagents to the final product, and the product analytical parameters. Calculation of the percent yield is based on the theoretical yield, which in turn depends upon the limiting starting material.
Inf Manage. I7 (1992) 95-105 /Original
Research Paper
During each simplex cycle, the theoretical yield is calculated by dividing the concentration of each reagent dimension by the reagent/product stoichiometric ratio; the lowest value among reagents is the limiting agent and this is used to calculate the theoretical yield. The stoichiometric ratio for pyrrole (and benzaldehyde) to porphyrin is 4 and this value is entered in the menu for assigning reagent concentrations to dimensions. In this experiment there is no limiting reagent since benzaldehyde and pyrrole, the sole reagents, are reacted in equimolar concentration. The reagent trifluoroacetic acid is designated a catalyst (nonreagent) by assigning the parameter 0 in the menu. The product analytical parameters are entered into the product database by the operator; in this case these include the porphyrin absorption spectral parameters A,,, (416 nm) and E (500000 M-’ cm-‘). These analysis parameters, in conjunction with the corrected absorption intensity (from the raw absorption data via a peakpicking routine), enable computation of the percent yield of porphyrin. The corrected intensity of the porphyrin absorption band enables a yield calculation identical to that done manually [5,6]. The simplex parameters that must be specified include the simplex start points, the stop criteria, and the CMS options [12]. The start points are determined by the user-specified reagent values in the Auto-Preparation Table. Two stop criteria involve the maximum number of simplex moves, or the tolerance for terminating the search, which have been set at 9 and 0.01, respectively. The CMS options ‘Expansion’, ‘Contraction’, ‘Weighted centroid’, ‘Expansion compared to reflection rather than best’, ‘Projected point check’, and ‘Next-to-worst rule’ were turned on in software. No range limit was placed on the simplex dimensions (the ‘Fit-to-boundary’ option was turned
EVENT LIST 2 Event No.
Event
1. 2. 3. 4.
UV-Vis UV-Vis UV-Vis UV-Vis
analysis analysis analysis analysis
of of of of
reaction reaction reaction reaction
1, 1, 1, 1,
350 to 350 to 350 to 350 to
??
650 nm, 650 nm, 650 nm, 650 nm,
6.67 x 6.67 x 6.67 x 6.67 x
10m4 1O-4 1O-4 1O-4
Start
Finish
0.00 15.00 30.00 60.00
6.80 21.80 36.80 66.80
??
L.A. Corkan et al. /Lab.
Inf Manage. 17 (1992) 9%105/Original
off). These settings are easily established through a series of windows. Finally the workstation is initialized (as in the factorial design experiment) and the simplex evolution is initiated one experimental cycle at a time. The simplex experiment gave a smooth evolution toward the maximum region of the response surface (Fig. 5). In each experimental cycle the highest yield value at t = 0, 15, 30, or 60 min is used as the basis for comparison (via a directory listing of the UV-Vis files). Three reflections and three expansions were performed during the course of the evolution, and three points were tested that gave lower values than any points in the then-current simplex. The points that were tested and then declined illustrate the power of the simplex algorithm to probe but then avoid poor regions of the response surface. This experiment proceeded until the upper limit of nine simplex moves had been performed. The simplex search led to a maximum yield of 35.9% at 18 mM benzaldehyde and pyrrole and 48.8 m A4 trifluoroacetic acid (Table 2). Simplex experimentation has rarely been performed in synthetic chemistry [13], in spite of the overwhelming importance of optimized yields for each reaction in a synthetic plan. Repetitive optimization procedures understandably hold little attraction to synthetic chemists who must work
TABLE Trace
Research Paper
101
100
36.6
[TFAI mM 136
55
I
I
I
13.6
36.8
100
[Reactants], mM
Fig. 5. Simplex experiment. The simplex evolution proceeds normally from the initial points to the optimal region of the response surface. A weighted centroid is employed to form a line of search containing the W (worst), E (expansion), and R (reflection) points on a linear scale; the data are plotted on a log scale. The contour, obtained from the 1 h data points of the factorial design experiment, is provided for comparison.
manually. In contrast, simplex optimization studies are performed easily with automation. Our results, in conjunction with those of Matsuda [14], show the utility of simplex experimentation with an automated chemistry workstation for identifying optimal reaction parameters.
2 of simplex
experiment
Reaction
BY*
[TFA] *
%yield
Type **
Vertex
Current
1 2 3 4 5 6 7 8 9 10 11 12
5.0 13.6 5.0 10.8 13.7 9.57 7.4 18.0 24.5 20.7 27.1 20.8
5.0 8.3 13.6 18.6 25.4 36.1 51.0 48.8 45.7 69.8 65.1 37.4
1.2 7.2 13.1 22.1 25.2 25.8 25.3 35.9 30.4 26.6 30.5 33.5
I I I R E R E R E R R R
1 2 3 4R 4E 5R 5E 6R 6E 7 8 9
1, 2, 3 No change 2, 3, 4E No change 3,4E, 5R No change 4E, 5R, 6R 5R, I, 6R 7, 8, 6R 8, 9, 6R
Concentrations in mM ** I, initial; E, expansion;
??
units. R, reflection.
simplex
L.A. Corkan et al. /Lab.
102 A STRATEGIC
SEARCHING
EXPERIMENT
A challenging task in synthetic chemistry is to find effective catalysts for particular reactions. The decision-tree programming approach is ideally suited for performing incisive searches for new catalysts. As an example, a list of potential acid catalysts can be tested for activity in the porphyrin reaction. The goal of this experiment is not to find optimal condensation conditions, but to identify those members of a list that show some catalytic activity. It is important that each catalyst be surveyed over a reasonable concentration range, since some catalysts may be active at quite low concentrations while others are active only at high concentrations. A flowchart for a catalyst searching protocol is shown in Fig. 6. The corresponding command
SURVFY OF CATALYSTS
_yese No
Yes
dyesN’o
6
End
Fig. 6. Flowchart
for surveying
a list of potential
catalysts
In5 Manage. I7 (1992) 95-105 /Original Research Paper
??
language is provided in the Appendix. A reaction is charged with solvent and reagents and an initial aliquot of catalyst is added. After 1.5 min the absorption is checked; if the yield is less than or equal to 6% then an additional aliquot of catalyst is added. This procedure will loop up to five times, reaching 10-l M catalyst. Upon achieving a yield that exceeds 6%, the reaction will be truncated and the next catalyst will be examined. If the yield is less than or equal to 6% with the highest level of catalyst, then the reaction is terminated and the next catalyst will be examined. Thus in a single reaction a catalyst can be examined for efficacy and its effective concentration range, if any, can be assessed. The provisions for terminating the reaction if the putative catalyst is ineffective enables an incisive search to identify effective catalysts for the porphyrin condensation with minimum effort. Before this protocol can be executed a menu is used to set globally applicable analysis parameters such as spectrometer scan range (350-600 nm), cuvette volume (2.0 ml), dilution vial volume (0.6 ml), and product parameters (A,, = 416 nm, E = 500 000 M- ’ cm- ‘). Though these parameters can be specified via individual statements, the use of globally applicable parameters simplifies the code (Table Al, Appendix). Timing is an important factor in precise experimentation. In this experiment the 15-min wait period is interspersed between the catalyst addition and the yield analysis steps. The yield analysis requires 5.25 min and the catalyst addition varies from 1.3 to 5.2 min (depending on the acid and the required volume), thus the overall analysis and addition procedures range from 6.55 to 10.45 min. In conjunction with the 15-min wait periods, the fourth data point is collected between 86.2 and 101.8 min. Though the timescales vary slightly for each acid, the precise time of execution of each procedure is known with f5 s precision, as is the case for both open-loop and closed-loop experimentation with the workstation. The search is performed over the four reagents, boron trifluoride etherate, trifluoroacetic acid, acetic acid, and methanesulfonic acid (Fig. 7). Boron trifluoride etherate gave a yield of 7.2% at
w
L.A. Corkan et al. /Lab.
Inj Manage. 17 (1992) 95-105/Original
r
Research Paper
103
nating when no activity is found. In this manner the search is directed toward attainment of the scientific goal with minimization of futile experimentation.
SUMMARY
0
Datapoints Total Acid (mM)
5’
Fig. 7. Data from ined at periodic yield is less than added. After four
the catalyst survey. Each catalyst is examintervals (15-min wait periods), and if the or equal to 6%, then additional catalyst is data points the experiment is terminated.
the first timepoint, causing the experiment to be truncated. Next trifluoroacetic acid was examined. Trifluoroacetic acid gave 0% at the first data point, but 8.4% after the second addition of acid, at which point the experiment was truncated. Methanesulfonic acid was investigated next and was found to give declining yields with each successive increase in acid. This steady decline in yield suggests that the porphyrinogen self-assembly process is extremely rapid with this acid concentration, so fast that the yield has peaked and the observed results show the decline in porphyrinogen, presumably via ring-opening and subsequent decay [5]. If so, lower concentrations of methanesulfonic acid may yield better results. The results with methanesulfonic acid represent a new scientific finding. Finally acetic acid was investigated. Acetic acid gave 0% at all data points and the entire range of acid concentration was explored before the experiment was terminated. This search enables strategic exploration, altering the course of the catalyst survey reaction based on the data collected, truncating reactions when the yield exceeds the threshold and termi-
Factorial design experiments, simplex experiments, and strategic searching experiments are easily planned for automated implementation. Factorial design experiments represent the brute-force approach of ‘doing all the experiments’ in order to ascertain the effects of reaction parameters. Simplex searches require fewer experiments to elucidate optimal reaction conditions. Strategic search protocols provide the capability to make decisions about experiments as the data are collected, enabling rapid pursuit of scientific goals. As one example, strategic search protocols are incisive in ferreting out the active and inactive condensation catalysts in a list of reagents. These results taken together show that automated experimentation can provide rapid acquisition of scientific data in domains of relatively clean chemistry.
ACKNOWLEDGMENT
The research in porphyrin chemistry ported by the NIH (GM36238).
is sup-
APPENDIX
The strategic searching program is shown in Table Al with comments demarcated / * * / and flowchart points shown in bold font.
L.A. Corkan et al. /Lab.
104 TABLE
In& Manage. 17 (1992) 95-105 /Original Research Paper
??
Al
Strategic / * define main 0
searching
program
main procedure
for effective
catalysts
*/
1 / * declare variables */ int abs, go, con step, reaction,
i, acid
mol, acid _vol, con;
/ * Start */ / * loop for 4 reactions, one for each acid */ / * Are there more catalysts to test? */ for (reaction = 1; reaction < 5; reaction = reaction + I){ / * initialize acid volume */ acid _vol = 0; acid_mol = gettmol (reaction); con step = 1; abs = 0; go= 1; / * Load new reaction */ load_rxn(reaction); while (go = = l){ / * compute volume by dividing reaction concentration by acid molarity con = next _ conctcon step); acid _vol = con/acid _mol; / * Add initial catalyst */ reag_to-rxn (reaction + 2, reaction, acid_vol); / * Wait 15 minutes */ wait(l5); */ /* collect absorption rxn to_spec (reaction, 40, 20); / * find corrected absorption at 416 nm */ abs = absb(i, 416); con -step = con -step + 1; i=i+ 1; / * Is yield > 6%? */ if Cabs > 9){ go = 0;) / * Add more catalyst? */ else if (con_ step > 4) { go = 0;) else{ go = 1;) }/ * end while */ I/ * end for */ I/ * end main */ )* End */ / * define function / * this user-defined to it */ next -conc(int cone
*/ function
returns
the concentration
*/
for the next reaction,
based upon the reaction
number
koncstep)
step)
1 if (concstep = = 1) (return SOO;} else if (concstep = = 2) (return 860;) else if (conccstep = = 3) (return else {return 6320;) I/ * end function definition */ / * define function */ / * this user-defined function passed to it */
2320;)
returns
/*5mM*/ / * 13.6 mM */ / * 36.8 mM */ / * 100 mM */
the molarity
of the acids in neat form, based upon the acid identification
number
passed
??
L.A. Corkan et al. /Lab.
TABLE
Inf Manage. I7 (1992) 95-105/Original
Research Paper
105
Al (continued)
get _ mol tint acid) l / * trifluoroacetic acid molarity x 10 */ if (acid = = l)( return 130;) / * methanesulfonic acid molarity x 10 */ else if (acid = = 2)(return 154;) / * acetic acid molarity x 10 */ else if (acid = = 3)(return 175;) / * boron trifluoride solution molarity X 10 */ else{ return 25;) l/ * end function definition */ / * define function */ / * this user-defined function loads methylene load _ rxn (int reaction number) 1 / * add 9.6 ml methylene chloride */ solv _ to rxn (1, reaction _ number, / * add 7 ~1 pyrrole */ reaggto-rxn (1, reaction-number, / * add 10 ~1 benzaldehyde */ reag_ to _ rxn (2, reaction -number, I/ * end function definition */
chloride,
pyrrole
and benzaldehyde
*/
9600); 7); 10);
REFERENCES L.A. Corkan and J.S. Lindsey, Experiment manager software for an automated chemistry workstation, including a scheduler for parallel experimentation, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (19921 47-74. J.-C. Plouvier, L.A. Corkan and J.S. Lindsey, Experiment planner for strategic experimentation with an automated chemistry workstation, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 75-94. J.S. Lindsey, A retrospective on the automation of laboratory synthetic chemistry, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 15-45. J.S. Lindsey, Self-assembly in synthetic routes to molecular devices. Biological principles and chemical perspectives, New Journal of Chemistry, 15 (1991) 153-180. J.S. Lindsey, I.C. Schreiman, H.C. Hsu, P.C. Kearney and A.M. Marguerettaz, Rothemund and Adler-Long0 reactions revisited: Synthesis of tetraphenylporphyrins under Journal of Organic Chemistry, 52 equilibrium conditions, (1987) 827-836. 6 J.S. Lindsey and R.W. Wagner, Investigation of the synthesis of ortho-substituted tetraphenylporphyrins, Journal of Organic Chemistry, 54 (1989) 828-836. 7 J.S. Lindsey, L.A. Corkan, D. Erb and G.J. Powers,
8
9
10 11
12 13
14
Robotic workstation for microscale synthetic chemistry: On-line absorption spectroscopy, quantitative automated thin layer chromatography, and multiple reactions in parallel, Reuiew of Scientific Instruments, 59 (1988) 940-950. A. Corkan and J.S. Lindsey, Design concepts for synthetic chemistry workstations, in J.R. Strimaitis and J.P. Helfrich Advances in Laboratory Automation Robotics, (Editors), Vol. 6, Zymark, Hopkinton, MA, 1990, pp. 477-497. L.A. Corkan, E. Haynes, S. Kline and J.S. Lindsey, Robotic thin layer chromatography instrument for synthetic chemistry, in Ali M. Emran (Editor), New Trends in Radiopharmaceutical Synthesis, Quality Assurance and Regulatory Control, Plenum Press, New York, 1991, pp. 355-370. Surfer, Version 4 (Golden Software, Inc. Golden, CO 80402, USA). C.K. Bayne and LB. Rubin, Practical Experimental Designs and Optimization Methods for Chemists, VCH Publishers, Deerfield Beach, FL, 1986. D. Betteridge, A.P. Wade and A.G. Howard, Reflections on the modified simplex-II, Talanta, 32 (1985) 723-734. S.N. Deming and S.L. Morgan, Teaching the fundamentals of experimental design, Analytica Chimica Acta, 150 (1983) 183-198. R. Matsuda, M. Ishibashi and Y. Takeda, Simplex optimization of reaction conditions with an automated system, Chemical and Pharmaceutical Bulletin, 36 (1988) 35123518.
Original Research Paper
95
Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 95-105 Elsevier Science Publishers B.V., Amsterdam
Application of an automated chemistry workstation to problems in synthetic chemistry L. Andrew Corkan, Jean-Christophe
Plouvier and Jonathan S. Lindsey
Department of Chemistry, Carnegie Mellon Uniuersity, 4400 Fifth Avenue, Pittsburgh, PA 15213 (USA) (Received
29 April
1992;
accepted
6 July
1992)
Abstract
Corkan, L.A., Plouvier, J.-C. and Lindsey, J.S., 1992. Application of an automated chemistry workstation to problems chemistry. Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17: 95-105.
in synthetic
An automated chemistry workstation is applied to problems in the synthetic chemistry of porphyrins. A factorial design study (sixteen experiments, 96 data points) was performed to examine the role of catalyst and reactant concentrations on porphyrin yield. Four experiments could be scheduled to run concurrently; all sixteen experiments were completed in less than 1 day of workstation time. The response surface from this experiment shows the conditions for achieving the highest yield. A simplex optimization was performed over the same reaction space, requiring fewer experiments to arrive at the optimal reaction parameters. A strategic search was performed to screen a list of reagents for catalytic activity. The effective concentration range of each catalyst was surveyed by systematic modification of an ongoing reaction. By terminating reactions when a yield threshold was surpassed or when the entire concentration range had been spanned, compounds with catalytic activity and their effective concentration ranges were identified with minimal experimentation. A new scientific finding was made concerning the catalytic activity of methanesulfonic acid in the porphyrin condensation. Automated chemistry workstations of this type should yield rapid accelerations in scientific research.
INTRODUCTION
Automation systems with the capability to work exhaustively, precisely, strategically, and autonomously will provide the scientist with a powerful tool for tackling difficult scientific problems. Toward this goal, we have sought to unify mi-
Correspondence to: Dr. J.S. Lindsey, Department of Chemistry, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213 (USA).
0925.5281/92/$05.00
0 1992 - El sevier Science
Publishers
croscale synthetic chemistry with analytical chemistry in a workstation equipped with a powerful experiment manager software package [1,2]. For automated experimentation to be successful, the reaction, analytical, and sample-handling capabilities of the workstation must be matched with the scientific problem [3]. This paper describes applications of the workstation in the synthetic chemistry of porphyrins, including a factorial design study of reaction conditions, a simplex optimization of reaction conditions, and a strategic search for effective condensation catalysts. These com-
B.V. All rights
reserved
L.A. Corkan et al. /Lab.
96
1. Porphyrinogen
Self-Assembly
Ar
H+ -
AI\
2. Porphyrinogen
.H
A/‘H
H
Oxidation
k
n
Fig. 1. Porphyrin reaction scheme. Benzaldehyde (1) and pyrrole (2) react under acid catalysis to form linear polypyrromethanes (not shown) and the porphyrinogen (3). Upon addition of an oxidant such as 2,3-dichloro-5,6dicyano-benzoquinone (DDQ, 41, the porphyrinogen is converted to the porphyrin (5) and the polypyrromethanes are converted to polypyrromethenes (not shown).
plementary experiments enable aggressive attacks on scientific problems.
MATCHING TION
THE
CHEMISTRY
AND
THE
WORKSTA-
Our domain of interest involves the fundamental chemistry of self-assembling systems [41. A self-assembly process occurs as the first step in a mild synthetic strategy for preparing tetraphenylporphyrins (Fig. 1) [5,6]. In this synthesis, the room temperature acid-catalyzed reaction of four aldehyde (1)and four pyrrole (2) molecules results in self-assembly of the porphyrinogen (3). Upon addition of an oxidant such as DDQ (4), the porphyrinogen is converted to the porphyrin (5). This convergent two-step strategy is exemplary of a trend in synthetic chemistry toward performing multiple processes in a single flask, thereby minimizing workup procedures in an overall synthetic plan. The complexity of multi-
In& Manage. I7 (1992) 95-105 /Original Research Paper
??
step reactions places a premium on precise control over numerous experimental parameters. Important research issues in the synthetic chemistry of porphyrins include reaction mechanisms, the efficacy of various catalysts and oxidants, and the search for improved reaction pathways in those instances where the reaction fails [5,6]. In this domain absorption spectroscopy and thin-layer chromatography (TLC) are ideal analytical tools, and the chemistry can be carried out at the microscale level [7-91. Using the first-generation workstation, 24 data points concerning benzaldehyde, byproducts, and porphyrin were collected from a single l-h microscale reaction [71. This is a relatively clean chemistry domain because precipitation does not occur, the solutions are nonviscous, and absorption spectroscopy can be used for analysis. High-quality investigations of self-assembling processes require precise manipulations, but because preparative purifications are not necessary, the reactions need be no larger in scale than is required for compatibility with the analytical instruments and the capabilities of the sample-handling system [3]. Accordingly, the reactions can be performed at the microscale level. Microscale chemistry has several advantages. First, a large number of reactors can be contained in a small space, facilitating robotic manipulations. Second, precious resources are used in a parsimonious manner, minimizing financial costs and environmental impact. Microscale chemistry, however, places high demands on the sample manipulation system, since reagents, catalysts, and reaction samples of l-50 ~1 must be manipulated precisely without sample carryover. Quality sample-handling depends on the accuracy of the syringe system, the reproducibility of the sampling and the integrity of the sample during transfers. The sample manipulator of the automated chemistry workstation [l] has a precision of *2.5% over the range of lo-500 ~1 and + 4% upon going to 1.0 ~1 in handling methylene chloride solutions. Minimizing sample carryover is of critical importance for quantitative measurements in porphyrin chemistry, since even 0.1 ~1 residual acid could foul the absorption spectral analysis of a porphyrin solution. There was no
??
L.A. Corkan et al. /Lab.
If:
Manage. 17 (1992) 95-105/Original
detectable sample carryover as determined by experiments with transfers of tetraphenylporphyrin solutions in methylene chloride. Evaporation can present serious problems with microscale quantities of volatile solvents, especially since the vessels and vials are not hermetically sealed. Evaporation of methylene chloride (b.p. 39°C) from the reaction vessels (45 pi/h at room temperature) is inconsequential for the typical lo-ml reaction volumes and short reaction times. Evaporation from the dilution vials (100 pi/h at room temperature) could cause problems in protracted experimental procedures, but invariably the analysis procedures are completed within 10 min. Careful attention to these and related sample-handling issues enables quantitative experimentation at the microscale level.
A FACTORIAL
DESIGN
Research Paper
97
the acid catalyst concentration. Thus a desirable experiment is to monitor the rate of porphyrin formation as a function of reactant concentrations and as a function of acid catalyst concentration. A 4 X 4 factorial design experiment was planned over the dimensions of reactant concentration and acid catalyst concentration, using a twenty-fold range in concentration (5, 13.6, 36.8, and 100 mM). The sixteen experiments were grouped into four sets of four, with the catalyst concentration varied within each set and the reagent concentration varied from set to set. Because the solvent and reagents do not react until the catalyst is added, the solvent and reagents can be added in a batch mode using the AutoPreparation Table (Fig. 2). Accessing the reagent database, the reagent’s name, density and formula weight are entered, and the reagent is assigned a vial number that identifies its location in the workstation. In the Auto-Preparation Table the total solvent volume for each reaction vessel (10 ml in this case) and the amount of pyrrole and benzaldehyde for each reaction vessel are specified (Fig. 2). The event programming menus are then used to compose a protocol for adding catalyst (trifluoroacetic acid, TFA) and monitoring the reaction
EXPERIMENT
In the porphyrin reaction the formation of the porphyrinogen involves competition between cyclization and polymerization. Consequently, the yield of porphyrin (derived from porphyrinogen) is sensitive to the pyrrole and benzaldehyde concentrations. The reaction rate is influenced by the concentration of reactants and, in addition, by
<<< Auto-Pm
= = Rsag~ntRolusnt I:= JIM nt Uol- fi I PYR&E .......... ................... f #ZlILDEtlYDE d 5
ration I&mu >>> ReactYon Uemel e -.
................. ................. ................. .................
.................................. ................. :! ................. ................. :3 ................. ................. 15' ................. L i;i;;,i.ir;iT.;i.k :
Total lbm Uolum ... I lkthylm.Cl....... ................. f .................
<< T&agentData
>>
Dm8.
+
<< Commands >>
~+&D ml1 tP31 w31 [PII IKI cP61 tF71 w81 [kc1
Select Ualue Hslp Reagent Cone. nmagent Uolum Solvent Uolum Define Reagent Define Solusnt load Pile saue Pile Exit
Fig. 2. Auto-Preparation Table for the factorial design experiment. The reaction vessels form the rows and the reagents and solvents form the columns of the spreadsheet. Reaction vessels l-5 are displayed; vessels lo-15 are displayed upon scrolling to the right. The reagent database (upper right corner) enables quantities of reagents to be specified by entering a concentration value; for a solvent volume the corresponding reagent volume is calculated. The total reaction volume is specified and the amount to be delivered is calculated assuming additivity of volumes.
L.A. Corkan et al. /Lab.
98
at geometrically staggered times (At = 15, 30, 60, 120, 240 min) relative to the finish time of the catalyst addition (see Event List 1). The event ‘UV-Vis of a reaction vessel (w/ dilution)’ performs a DDQ oxidation of a reaction sample prior to absorption spectrometric analysis. The event consists of: (1) addition of 600 ~1 of DDQ solution (0.01 M in toluene) to a dilution vial (equipped with a stir bar) by the product dilution station, (2) transfer of a 25-~1 sample from a reaction vessel into the DDQ solution (75 s); (3) stirring for 65 s while the sample manipulator is flushed; (4) transfer of a 25-~1 aliquot to the spectrometer cuvette containing 2.0 ml CH,Cl,-ethanol 3: 1 (50 s); and (5) collection of the absorption spectrum. For quantitative purposes the total dilution of the reaction sample (6.67 X 10W4) is displayed. This event list is then modified (employing reaction vessels 2, 3, and 4; TFA 10.5 ~1, 28.4 ~1, 77.0 ~1; altered start times) for reactions 2, 3, and 4, respectively. A new list is stored for each experiment. Next, the four event lists are scheduled for parallel implementation. The scheduling algorithm merges the four event lists so that the reactions are performed concurrently, maintaining the relative times of procedural events for a given reaction unchanged from the original event list (Fig. 3). The composite schedule of four event lists executes in 327.40 min, compared with 992.88 min if the four were performed serially. Including the Auto-Preparation times gives values for parallel versus serial execution of 351.26 and 1016.74 min, respectively, a 2.89-fold improvement in throughput. The total workstation time for all sixteen experiments was then only 1405.04 min (23.4 h).
EVENT
lnf Manage. 17 (1992) 95-105 /Original
0 5
.-E
c
Composite
??I
0
40
120
160 Time
II
I
200
240
II
260
320
360
Event
1. 2. 3. 4. 5. 6. 7.
Add 3.9 UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis UV-Vis
yl of reagent (3) analysis of reaction analysis of reaction analysis of reaction analysis of reaction analysis of reaction analysis of reaction
1, 1, 1, 1, 1, 1,
TFA 350 to 350 to 350 to 350 to 350 to 350 to
400
(min.)
Fig. 3. Scheduled timelines of four reactions in the factorial design experiment. Interleaving four experiments by varying the starting times does not alter the relative times of procedural events within a list.
Next the resources are tabulated for the Auto-Preparation Table and the four event lists (Table 1). Given that the schedule and resources are acceptable, the automated chemistry experiment is initiated by calling the command ‘Execute chemistry events’. This command prompts for the operator’s surname, which is stored with the date, time, and the computer-generated unique file name. This identification information is appended to the Log file, which serves as an electronic log book of workstation operation. The five hardware modules are initialized via the automatic start-up procedures. If no problems are encountered, a directory for data storage is created automatically, the Auto-Preparation Table is executed as a batch procedure, and then the scheduled event lists are executed. The six absorption spectra collected from each reaction provide a direct measure of the porphyrin yield over time. The three remaining sets of experiments employ the same four scheduled event lists with appropriate changes in reactant concentrations (5 mM, set 1;. . . 100 mM, set 4) in the Auto-Preparation Table. Plotting the highest yield at any time for each reaction yields a con-
LIST 1
Event No.
??
Schedule
-1111Dl
60
Research Paper
650 650 650 650 650 650
nm, nm, nm, nm, nm, nm,
to reaction 1 6.67 x 1O-4 6.67 x 10m4 6.67 x 1O-4 6.67 x 1O-4 6.67 x 1O-4 6.67 x 1O-4
Start
Finish
0.00 1.42 16.42 31.42 61.42 121.42 241.42
1.42 8.22 23.22 38.22 68.22 128.22 248.22
??
L.A. Corkan et al. /Lab.
In: Manage. 17 (1992) 95-105/Original
Research Paper
99
tour surface showing the dependence of yield on the concentrations of reactants and catalyst (Fig. 4). At the highest acid concentrations (100 mM) three of the experiments ([reactants] = 13.6, 36.8, and 100 mM) gave maximal yields at the earliest time point (0 mm). At these concentrations of catalyst the reaction is so fast that the growing in of the porphyrin cannot be resolved. With reactants at 5 mM the highest yield was obtained at the 15-min timepoint. At all lower concentrations the highest yields of porphyrin were obtained at the 4-h timepoint. These results, collected in less than 1 day of workstation time, illustrate the prowess of an automation system in rapid acquisition of scientific data.
A SIMPLEX
EXPERIMENT
The simplex algorithm provides a means for optimizing the response of several parameters TABLE
1
Resource tabulation design study
for
Reagent uolurnes (~1) 1. 13.8 2. 20.4 3. 119.8 4. 0.00 5. 0.00 6. 0.00 7. 0.00 8. 0.00 ??
??
Reaction solcents (ml) 1: 39.96 2: 0.00 3: 0.00
four
experiments
in the
factorial
9. 0.00 10. 0.00 11.0.00 12. 0.00 13. 0.00 14. 0.00 15. 0.00 16. 0.00 Hardware module solvents (ml) Dilution station: 14.40 UV-Vis: 115.0 TLC: 0.0 Carrier: 1.03
Supplies Sample vials: 0 Dilution vials: 24 TLC plates: 0 Workstation track record UV-Vis scans: 25 * Sample manipulator injections: 104 TLC scans: 0 ??
Timing (min) Auto-Preparation Event list: 327.40 Total execution:
Table: 23.86
351.26
* This value is for the reaction set at 5X 10e3 concentration. ** 24 UV-Vis scans and one reference scan.
M reagent
Fig. 4. Response surface of the factorial design experiment. The highest yields obtained at any time during each experiment are plotted to form the response surface. The contour graph on the top was inferred from the response surface by the minimum curvature method of the Surfer graphics package [lo].
[ll]. Though the factors influencing the course of a reaction can be investigated exhaustively through the use of factorial design experiments, the evolutionary nature of simplex experimentation yields a more focused attainment of the optimal combination of parameters. The experimental procedure used in the factorial design experiment can be modified for use as a template in a simplex search. The parameters that must be specified include the experimental template (event list), the method of computing the objective function (R), the CMS options, the initial simplex points, and the stop criteria. The event list and the Auto-Preparation Table used in the factorial design experiment are modified to form the experimental template for the simplex search. The Auto-Preparation Table holds the reagent and acid catalyst concentrations for the reactions that will be executed to begin the simplex search (the initial simplex points). As the search progresses the reagent volumes in the Auto-Preparation Table are modified corre-
100
L.A. Corkan et al. /Lab.
sponding to the new points in the search. Event list 2 is used by the simplex module as a template for analysis of the reactions. With each simplex cycle the reaction vessels and dilution station vials (used as part of the UV-Vis analysis) are incremented. Specific hooks that establish the dimensions and ranges of the simplex search must be provided to link the experimental template to the simplex module. The specification of a reagent concentration as a simplex variable is achieved by inputting the reagent number (as defined in the reagent database) in a menu for establishing simplex dimensions. In this case pyrrole is set equal to dimension 1 and trifluoroacetic acid is set equal to dimension 2. The porphyrin reaction involves condensation of four pyrrole and four benzaldehyde molecules. In this study we wish to keep the concentrations of pyrrole and benzaldehyde equal at all times, thus benzaldehyde is assigned to pyrrole as a dependent reagent with a 1: 1 ratio. This guarantees that equimolar benzaldehyde and pyrrole concentrations will function as one simplex dimension. Usually the range of each dimension must also be defined in a menu at the same time as the dimensions are assigned (in this case 5 x 10-3-10-’ MI, but in this case the ‘Fit to boundaries’ option is turned off, rendering the range setting irrelevant. The objective of this experiment is to maximize yield, thus the objective function (R) is set equal to porphyrin % yield. Automated computation of the objective function requires the reagent concentrations that are assigned to the simplex dimensions, the stoichiometric ratio of these reagents to the final product, and the product analytical parameters. Calculation of the percent yield is based on the theoretical yield, which in turn depends upon the limiting starting material.
Inf Manage. I7 (1992) 95-105 /Original
Research Paper
During each simplex cycle, the theoretical yield is calculated by dividing the concentration of each reagent dimension by the reagent/product stoichiometric ratio; the lowest value among reagents is the limiting agent and this is used to calculate the theoretical yield. The stoichiometric ratio for pyrrole (and benzaldehyde) to porphyrin is 4 and this value is entered in the menu for assigning reagent concentrations to dimensions. In this experiment there is no limiting reagent since benzaldehyde and pyrrole, the sole reagents, are reacted in equimolar concentration. The reagent trifluoroacetic acid is designated a catalyst (nonreagent) by assigning the parameter 0 in the menu. The product analytical parameters are entered into the product database by the operator; in this case these include the porphyrin absorption spectral parameters A,,, (416 nm) and E (500000 M-’ cm-‘). These analysis parameters, in conjunction with the corrected absorption intensity (from the raw absorption data via a peakpicking routine), enable computation of the percent yield of porphyrin. The corrected intensity of the porphyrin absorption band enables a yield calculation identical to that done manually [5,6]. The simplex parameters that must be specified include the simplex start points, the stop criteria, and the CMS options [12]. The start points are determined by the user-specified reagent values in the Auto-Preparation Table. Two stop criteria involve the maximum number of simplex moves, or the tolerance for terminating the search, which have been set at 9 and 0.01, respectively. The CMS options ‘Expansion’, ‘Contraction’, ‘Weighted centroid’, ‘Expansion compared to reflection rather than best’, ‘Projected point check’, and ‘Next-to-worst rule’ were turned on in software. No range limit was placed on the simplex dimensions (the ‘Fit-to-boundary’ option was turned
EVENT LIST 2 Event No.
Event
1. 2. 3. 4.
UV-Vis UV-Vis UV-Vis UV-Vis
analysis analysis analysis analysis
of of of of
reaction reaction reaction reaction
1, 1, 1, 1,
350 to 350 to 350 to 350 to
??
650 nm, 650 nm, 650 nm, 650 nm,
6.67 x 6.67 x 6.67 x 6.67 x
10m4 1O-4 1O-4 1O-4
Start
Finish
0.00 15.00 30.00 60.00
6.80 21.80 36.80 66.80
??
L.A. Corkan et al. /Lab.
Inf Manage. 17 (1992) 9%105/Original
off). These settings are easily established through a series of windows. Finally the workstation is initialized (as in the factorial design experiment) and the simplex evolution is initiated one experimental cycle at a time. The simplex experiment gave a smooth evolution toward the maximum region of the response surface (Fig. 5). In each experimental cycle the highest yield value at t = 0, 15, 30, or 60 min is used as the basis for comparison (via a directory listing of the UV-Vis files). Three reflections and three expansions were performed during the course of the evolution, and three points were tested that gave lower values than any points in the then-current simplex. The points that were tested and then declined illustrate the power of the simplex algorithm to probe but then avoid poor regions of the response surface. This experiment proceeded until the upper limit of nine simplex moves had been performed. The simplex search led to a maximum yield of 35.9% at 18 mM benzaldehyde and pyrrole and 48.8 m A4 trifluoroacetic acid (Table 2). Simplex experimentation has rarely been performed in synthetic chemistry [13], in spite of the overwhelming importance of optimized yields for each reaction in a synthetic plan. Repetitive optimization procedures understandably hold little attraction to synthetic chemists who must work
TABLE Trace
Research Paper
101
100
36.6
[TFAI mM 136
55
I
I
I
13.6
36.8
100
[Reactants], mM
Fig. 5. Simplex experiment. The simplex evolution proceeds normally from the initial points to the optimal region of the response surface. A weighted centroid is employed to form a line of search containing the W (worst), E (expansion), and R (reflection) points on a linear scale; the data are plotted on a log scale. The contour, obtained from the 1 h data points of the factorial design experiment, is provided for comparison.
manually. In contrast, simplex optimization studies are performed easily with automation. Our results, in conjunction with those of Matsuda [14], show the utility of simplex experimentation with an automated chemistry workstation for identifying optimal reaction parameters.
2 of simplex
experiment
Reaction
BY*
[TFA] *
%yield
Type **
Vertex
Current
1 2 3 4 5 6 7 8 9 10 11 12
5.0 13.6 5.0 10.8 13.7 9.57 7.4 18.0 24.5 20.7 27.1 20.8
5.0 8.3 13.6 18.6 25.4 36.1 51.0 48.8 45.7 69.8 65.1 37.4
1.2 7.2 13.1 22.1 25.2 25.8 25.3 35.9 30.4 26.6 30.5 33.5
I I I R E R E R E R R R
1 2 3 4R 4E 5R 5E 6R 6E 7 8 9
1, 2, 3 No change 2, 3, 4E No change 3,4E, 5R No change 4E, 5R, 6R 5R, I, 6R 7, 8, 6R 8, 9, 6R
Concentrations in mM ** I, initial; E, expansion;
??
units. R, reflection.
simplex
L.A. Corkan et al. /Lab.
102 A STRATEGIC
SEARCHING
EXPERIMENT
A challenging task in synthetic chemistry is to find effective catalysts for particular reactions. The decision-tree programming approach is ideally suited for performing incisive searches for new catalysts. As an example, a list of potential acid catalysts can be tested for activity in the porphyrin reaction. The goal of this experiment is not to find optimal condensation conditions, but to identify those members of a list that show some catalytic activity. It is important that each catalyst be surveyed over a reasonable concentration range, since some catalysts may be active at quite low concentrations while others are active only at high concentrations. A flowchart for a catalyst searching protocol is shown in Fig. 6. The corresponding command
SURVFY OF CATALYSTS
_yese No
Yes
dyesN’o
6
End
Fig. 6. Flowchart
for surveying
a list of potential
catalysts
In5 Manage. I7 (1992) 95-105 /Original Research Paper
??
language is provided in the Appendix. A reaction is charged with solvent and reagents and an initial aliquot of catalyst is added. After 1.5 min the absorption is checked; if the yield is less than or equal to 6% then an additional aliquot of catalyst is added. This procedure will loop up to five times, reaching 10-l M catalyst. Upon achieving a yield that exceeds 6%, the reaction will be truncated and the next catalyst will be examined. If the yield is less than or equal to 6% with the highest level of catalyst, then the reaction is terminated and the next catalyst will be examined. Thus in a single reaction a catalyst can be examined for efficacy and its effective concentration range, if any, can be assessed. The provisions for terminating the reaction if the putative catalyst is ineffective enables an incisive search to identify effective catalysts for the porphyrin condensation with minimum effort. Before this protocol can be executed a menu is used to set globally applicable analysis parameters such as spectrometer scan range (350-600 nm), cuvette volume (2.0 ml), dilution vial volume (0.6 ml), and product parameters (A,, = 416 nm, E = 500 000 M- ’ cm- ‘). Though these parameters can be specified via individual statements, the use of globally applicable parameters simplifies the code (Table Al, Appendix). Timing is an important factor in precise experimentation. In this experiment the 15-min wait period is interspersed between the catalyst addition and the yield analysis steps. The yield analysis requires 5.25 min and the catalyst addition varies from 1.3 to 5.2 min (depending on the acid and the required volume), thus the overall analysis and addition procedures range from 6.55 to 10.45 min. In conjunction with the 15-min wait periods, the fourth data point is collected between 86.2 and 101.8 min. Though the timescales vary slightly for each acid, the precise time of execution of each procedure is known with f5 s precision, as is the case for both open-loop and closed-loop experimentation with the workstation. The search is performed over the four reagents, boron trifluoride etherate, trifluoroacetic acid, acetic acid, and methanesulfonic acid (Fig. 7). Boron trifluoride etherate gave a yield of 7.2% at
w
L.A. Corkan et al. /Lab.
Inj Manage. 17 (1992) 95-105/Original
r
Research Paper
103
nating when no activity is found. In this manner the search is directed toward attainment of the scientific goal with minimization of futile experimentation.
SUMMARY
0
Datapoints Total Acid (mM)
5’
Fig. 7. Data from ined at periodic yield is less than added. After four
the catalyst survey. Each catalyst is examintervals (15-min wait periods), and if the or equal to 6%, then additional catalyst is data points the experiment is terminated.
the first timepoint, causing the experiment to be truncated. Next trifluoroacetic acid was examined. Trifluoroacetic acid gave 0% at the first data point, but 8.4% after the second addition of acid, at which point the experiment was truncated. Methanesulfonic acid was investigated next and was found to give declining yields with each successive increase in acid. This steady decline in yield suggests that the porphyrinogen self-assembly process is extremely rapid with this acid concentration, so fast that the yield has peaked and the observed results show the decline in porphyrinogen, presumably via ring-opening and subsequent decay [5]. If so, lower concentrations of methanesulfonic acid may yield better results. The results with methanesulfonic acid represent a new scientific finding. Finally acetic acid was investigated. Acetic acid gave 0% at all data points and the entire range of acid concentration was explored before the experiment was terminated. This search enables strategic exploration, altering the course of the catalyst survey reaction based on the data collected, truncating reactions when the yield exceeds the threshold and termi-
Factorial design experiments, simplex experiments, and strategic searching experiments are easily planned for automated implementation. Factorial design experiments represent the brute-force approach of ‘doing all the experiments’ in order to ascertain the effects of reaction parameters. Simplex searches require fewer experiments to elucidate optimal reaction conditions. Strategic search protocols provide the capability to make decisions about experiments as the data are collected, enabling rapid pursuit of scientific goals. As one example, strategic search protocols are incisive in ferreting out the active and inactive condensation catalysts in a list of reagents. These results taken together show that automated experimentation can provide rapid acquisition of scientific data in domains of relatively clean chemistry.
ACKNOWLEDGMENT
The research in porphyrin chemistry ported by the NIH (GM36238).
is sup-
APPENDIX
The strategic searching program is shown in Table Al with comments demarcated / * * / and flowchart points shown in bold font.
L.A. Corkan et al. /Lab.
104 TABLE
In& Manage. 17 (1992) 95-105 /Original Research Paper
??
Al
Strategic / * define main 0
searching
program
main procedure
for effective
catalysts
*/
1 / * declare variables */ int abs, go, con step, reaction,
i, acid
mol, acid _vol, con;
/ * Start */ / * loop for 4 reactions, one for each acid */ / * Are there more catalysts to test? */ for (reaction = 1; reaction < 5; reaction = reaction + I){ / * initialize acid volume */ acid _vol = 0; acid_mol = gettmol (reaction); con step = 1; abs = 0; go= 1; / * Load new reaction */ load_rxn(reaction); while (go = = l){ / * compute volume by dividing reaction concentration by acid molarity con = next _ conctcon step); acid _vol = con/acid _mol; / * Add initial catalyst */ reag_to-rxn (reaction + 2, reaction, acid_vol); / * Wait 15 minutes */ wait(l5); */ /* collect absorption rxn to_spec (reaction, 40, 20); / * find corrected absorption at 416 nm */ abs = absb(i, 416); con -step = con -step + 1; i=i+ 1; / * Is yield > 6%? */ if Cabs > 9){ go = 0;) / * Add more catalyst? */ else if (con_ step > 4) { go = 0;) else{ go = 1;) }/ * end while */ I/ * end for */ I/ * end main */ )* End */ / * define function / * this user-defined to it */ next -conc(int cone
*/ function
returns
the concentration
*/
for the next reaction,
based upon the reaction
number
koncstep)
step)
1 if (concstep = = 1) (return SOO;} else if (concstep = = 2) (return 860;) else if (conccstep = = 3) (return else {return 6320;) I/ * end function definition */ / * define function */ / * this user-defined function passed to it */
2320;)
returns
/*5mM*/ / * 13.6 mM */ / * 36.8 mM */ / * 100 mM */
the molarity
of the acids in neat form, based upon the acid identification
number
passed
??
L.A. Corkan et al. /Lab.
TABLE
Inf Manage. I7 (1992) 95-105/Original
Research Paper
105
Al (continued)
get _ mol tint acid) l / * trifluoroacetic acid molarity x 10 */ if (acid = = l)( return 130;) / * methanesulfonic acid molarity x 10 */ else if (acid = = 2)(return 154;) / * acetic acid molarity x 10 */ else if (acid = = 3)(return 175;) / * boron trifluoride solution molarity X 10 */ else{ return 25;) l/ * end function definition */ / * define function */ / * this user-defined function loads methylene load _ rxn (int reaction number) 1 / * add 9.6 ml methylene chloride */ solv _ to rxn (1, reaction _ number, / * add 7 ~1 pyrrole */ reaggto-rxn (1, reaction-number, / * add 10 ~1 benzaldehyde */ reag_ to _ rxn (2, reaction -number, I/ * end function definition */
chloride,
pyrrole
and benzaldehyde
*/
9600); 7); 10);
REFERENCES L.A. Corkan and J.S. Lindsey, Experiment manager software for an automated chemistry workstation, including a scheduler for parallel experimentation, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (19921 47-74. J.-C. Plouvier, L.A. Corkan and J.S. Lindsey, Experiment planner for strategic experimentation with an automated chemistry workstation, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 75-94. J.S. Lindsey, A retrospective on the automation of laboratory synthetic chemistry, Chemometrics and Intelligent Laboratory Systems: Laboratory Information Management, 17 (1992) 15-45. J.S. Lindsey, Self-assembly in synthetic routes to molecular devices. Biological principles and chemical perspectives, New Journal of Chemistry, 15 (1991) 153-180. J.S. Lindsey, I.C. Schreiman, H.C. Hsu, P.C. Kearney and A.M. Marguerettaz, Rothemund and Adler-Long0 reactions revisited: Synthesis of tetraphenylporphyrins under Journal of Organic Chemistry, 52 equilibrium conditions, (1987) 827-836. 6 J.S. Lindsey and R.W. Wagner, Investigation of the synthesis of ortho-substituted tetraphenylporphyrins, Journal of Organic Chemistry, 54 (1989) 828-836. 7 J.S. Lindsey, L.A. Corkan, D. Erb and G.J. Powers,
8
9
10 11
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14
Robotic workstation for microscale synthetic chemistry: On-line absorption spectroscopy, quantitative automated thin layer chromatography, and multiple reactions in parallel, Reuiew of Scientific Instruments, 59 (1988) 940-950. A. Corkan and J.S. Lindsey, Design concepts for synthetic chemistry workstations, in J.R. Strimaitis and J.P. Helfrich Advances in Laboratory Automation Robotics, (Editors), Vol. 6, Zymark, Hopkinton, MA, 1990, pp. 477-497. L.A. Corkan, E. Haynes, S. Kline and J.S. Lindsey, Robotic thin layer chromatography instrument for synthetic chemistry, in Ali M. Emran (Editor), New Trends in Radiopharmaceutical Synthesis, Quality Assurance and Regulatory Control, Plenum Press, New York, 1991, pp. 355-370. Surfer, Version 4 (Golden Software, Inc. Golden, CO 80402, USA). C.K. Bayne and LB. Rubin, Practical Experimental Designs and Optimization Methods for Chemists, VCH Publishers, Deerfield Beach, FL, 1986. D. Betteridge, A.P. Wade and A.G. Howard, Reflections on the modified simplex-II, Talanta, 32 (1985) 723-734. S.N. Deming and S.L. Morgan, Teaching the fundamentals of experimental design, Analytica Chimica Acta, 150 (1983) 183-198. R. Matsuda, M. Ishibashi and Y. Takeda, Simplex optimization of reaction conditions with an automated system, Chemical and Pharmaceutical Bulletin, 36 (1988) 35123518.