Reconfigurable system for automated optimization of diverse chemical reactions

Article abstract of DOI:10.1126/science.aat0650

Chemical synthesis generally requires labor-intensive, sometimes tedious trial-and-error optimization of reaction conditions. Here, we describe a plug-and-play, continuous-flow chemical synthesis system that mitigates this challenge with an integrated combination of hardware, software, and analytics. The system software controls the user-selected reagents and unit operations (reactors and separators), processes reaction analytics (high-performance liquid chromatography, mass spectrometry, vibrational spectroscopy), and conducts automated optimizations. The capabilities of this system are demonstrated in high-yielding implementations of C-C and C-N cross-coupling, olefination, reductive amination, nucleophilic aromatic substitution (S_NAr), photoredox catalysis, and a multistep sequence. The graphical user interface enables users to initiate optimizations, monitor progress remotely, and analyze results. Subsequent users of an optimized procedure need only download an electronic file, comparable to a smartphone application, to implement the protocol on their own apparatus.

Full text of DOI:10.1126/science.aat0650

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can enhance the efficacy of HTE and accelerate
assaying of a library of potential biological mod-
ulators (25). Very recently, researchers at Pfizer
reported a continuous-flow droplet platform capa-
ble of screening >1500 reagent combinations for
a particular reaction as exemplified by a Suzuki-
Miyaura coupling (26).
RESEARCH ARTICLE
CHEMISTRY
Reconfigurable system for automated
optimization of diverse
All of the above important advances are tai-
lored to specific chemical reactions and/or tar-
gets. That is, the systems were not conceived with
the flexibility to perform a varied array of reactions
without some measure of redesign or reoptimiza-
tion. Therefore, we aspired to create a compact,
fully integrated, easily reconfigurable, benchtop
system that enables automated optimization of a
wide range of chemical transformations. During
the initial investigations of such a system, we
recognized that several challenges would need
to be addressed. These include the chemical com-
patibility of components and pumping mecha-
nisms; development of a unified, modular system
for truly plug-and-play operation; appropriate
software for system control and real-time moni-
toring (using established analytical methods) for
automated feedback optimization; and ultimate-
ly, integration into a single, small-footprint plat-
form that requires little user expertise with flow
chemistry. Presented herein is the realization of
these goals in a lab-scale, automated, and reconfig-
urable continuous-flow synthesis platform, demon-
strated in single-step and multistep sequences
encompassing several of the most widely used re-
actions in organic chemistry.
chemical reactions
Anne-Catherine Bédard1*, Andrea Adamo2*, Kosi C. Aroh2, M. Grace Russell1,
Aaron A. Bedermann1, Jeremy Torosian2, Brian Yue2,
Klavs F. Jensen2†, Timothy F. Jamison1†
Chemical synthesis generally requires labor-intensive, sometimes tedious trial-and-error
optimization of reaction conditions. Here, we describe a plug-and-play, continuous-flow
chemical synthesis system that mitigates this challenge with an integrated combination of
hardware, software, and analytics. The system software controls the user-selected
reagents and unit operations (reactors and separators), processes reaction analytics
(high-performance liquid chromatography, mass spectrometry, vibrational spectroscopy),
and conducts automated optimizations. The capabilities of this system are demonstrated in
high-yielding implementations of C-C and C-N cross-coupling, olefination, reductive
amination, nucleophilic aromatic substitution (SNAr), photoredox catalysis, and a multistep
sequence. The graphical user interface enables users to initiate optimizations, monitor
progress remotely, and analyze results. Subsequent users of an optimized procedure need
only download an electronic file, comparable to a smartphone application, to implement the
protocol on their own apparatus.
hemists invest substantial time in repet-
itive experimental tasks, such as reaction
monitoring and iterative optimization. These
activities limit the effort they would other-
wise direct toward innovation and creativ-
among the pharmaceuticals involved designing
and developing a synthetic route, selecting the
appropriate types and sizes of reactors, reassem-
bling the hardware, and optimizing the overall
process (9, 10). Recently, a team from Eli Lilly
described a continuous manufacturing process
to support phase 1 and 2 clinical trials of an
active pharmaceutical ingredient (11).
System concept and design
The system (Fig. 1) was designed to simplify
labor-intensive chemical experimentation, in a
manner similar to the way gas chromatography
and high-performance liquid chromatography
(HPLC) allow rapid automated analysis of sam-
ples with minimal technical training. To do so,
we targeted the development and integration
of three capabilities: hardware components that
perform the syntheses and purifications, in-line
analytical technologies that monitor reaction prog-
ress, and a user interface that provides software
control and monitoring. The resulting system
would thus be reconfigurable and perform a
wide range of chemical reactions. Users would
select the appropriate analytical instrument for a
given optimization, and user-friendly software
based in MATLAB and LabVIEW would provide
control of both the system and the analytical in-
strument. This design would additionally offer
the user a trio of modes to operate the system:
(i) automated optimization of a specific reaction
or sequence of reactions; (ii) synthesis of a range
of substrates under user-selected conditions, for
example, to investigate the scope of the transfor-
mation under conditions obtained from an
optimization; (iii) or scale-up of a selected synthe-
sis under conditions obtained from a previous
optimization.
C
ity and, in turn, inhibit the pace of discovery and
development in fields that depend upon molec-
ular synthesis (1–3). Here, we describe an inte-
grated, automated approach to mitigate these
challenges.
The opportunities offered by flow synthesis
in earlier stages of chemical discovery and de-
velopment also have attracted major attention
(2, 3). Automated peptide and oligonucleotide
synthesis revolutionized protein chemistry and
molecular biology by providing scientists rapid
access to complex biomolecules (12, 13). More
recently, Burke and co-workers developed an ap-
proach that uses a cross-coupling reaction to pro-
duce milligram quantities of organic molecules by
an iterative, deprotection-coupling-purification
sequence (14). A modular Vaportec-based ap-
proach by Seeberger streamlined the divergent
multistep syntheses of five active pharmaceu-
tical ingredients (15), and systems developed by
Ley feature monitoring and control of multistep
syntheses of several specific molecular targets
(16, 17). Multiple studies of automated optimi-
zation (18–22) have demonstrated the utility of
continuous-flow and droplet systems in identify-
ing optimal reaction conditions and catalysts for
selected reactions. Advances in high-throughput
experimentation (HTE) have shown that collec-
tions of molecules can be accessed in reduced
time (23, 24). For example, researchers at Eli Lilly
demonstrated that automation of batch processes
Critical in our investigations was the use of
continuous-flow synthesis (4). The standard for
commodity chemical manufacturing, this approach
has received substantial attention in the past
decade for its implementation in the synthesis
of many classes of complex organic molecules,
including pharmaceuticals (1, 5–8). Four years
ago, the end-to-end, fully integrated continuous
manufacturing of a formulated pharmaceutical
(aliskiren) was accomplished in a bespoke system
designed for a single specific purpose. Subse-
quently, several technological and chemical ad-
vances enabled the creation of a compact (1.26 m3)
reconfigurable system for the end-to-end synthe-
sis, purification, and formulation of four differ-
ent pharmaceuticals. Interchanging the system
1Department of Chemistry, Massachusetts Institute of
Technology, Cambridge, MA 02139, USA. 2Department
of Chemical Engineering, Massachusetts Institute of
Technology, Cambridge, MA 02139, USA.
*These authors contributed equally to this work.
†Corresponding author. Email: tfj@mit.edu (T.F.J.); kfjensen@
mit.edu (K.F.J.)
We aimed for chemically compatible plug-and-
play reaction components that would enable
rapid realization of different chemical trans-
formations on a general platform providing the
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necessary fluid and temperature control and
having an intuitive graphical user interface with
robust optimization algorithms. Realization of
the goal of plug-and-play use of the system pre-
sented, perhaps, the greatest challenge. In prin-
ciple, one could combine commercial technologies
(pumps, reactor tubes, in-line separators, among
others) for a particular optimization, but this
approach would offer only a minimal enhance-
ment in ease of use, and would require a large
footprint. Rather, we desired a system wherein
only reactor modules and separators would be at-
tached by the user in a matter of seconds, without
the need to reconfigure pumps, tubes, or other
flow components. This versatility was achieved
by the development of a universal bay (Fig. 1B), a
standardized and flexible interface that can host
any type of reaction module necessary for the
particular chemistry being performed. The pre-
sent system comprises five such bays, and six dif-
ferent modules have been developed thus far: a
heated reactor (up to 120°C), a cooled reactor (to
–20°C), a light-emitting diode (LED)–based photo-
chemistry reactor, a packed-bed reactor (for solid
supported reagents and catalysts, as well as pas-
sive mixing), a membrane-based liquid-liquid sep-
arator (purification via extraction), and a bypass
(for reagent addition in a minimal volume, mix-
ing, or unused bay). The volume of each reactor
ranges between 215 and 860 ml, depending on
the internal diameter of the disposable PFA
tubing used. Each bay is fed by a M6 Vici pump,
with the exception of bay 1, which is connected to
two pumps (Fig. 1B). In total, up to six different
solutions containing reagents and/or solvents
can be delivered into the system. The complete
platform is contained within a small footprint
and a total volume of 0.22 m3 [0.61 m (width) ×
0.86 m (length) × 0.41 m (height)].
To augment the versatility and enable intuitive
use of the system by those with only minimal
expertise, we designed a simple graphical inter-
face (Fig. 1D, details of the user interface and
automation scheme provided in figs. S8 and S10,
and accompanying text). Automated optimiza-
tion is enabled by integrating continuous reac-
tion monitoring and precise control of the key
reaction parameters using two pressure sensors,
two flow meters, one phase sensor, five infrared
(IR)–based temperature sensors, and two cam-
eras for web-based remote monitoring. Several
in-line analytical methods can be used with the
system to enable reaction monitoring and subse-
quent autonomous optimization. Although HPLC
provides the best balance of generality and in-
strument cost, IR spectroscopy (Mettler Toledo
ReactIR), Raman spectroscopy (Marqmetrix Raman
BallProbe), and mass spectrometry (Advion MS
with electrospray ionization) are also compatible
(27, 28). The control software behind the user
interface continuously analyzes and records the
data received from these devices.
Many optimization algorithms may be used
with the system, but we have found that the
stable noisy optimization by branch and fit
[SNOBFIT (29)] algorithm provides a convenient
means for global optimization of single- or multi-
step processes without the need for a theoreti-
cal model. This agnostic, “black-box” approach
Fig. 1. Plug-and-play, reconfigurable, continuous-flow chemical
synthesis system. (A) General four-step protocol for using the
system. (B) Representative configuration of the components in the
system. (C) CAD (computer-aided design) representation of the LED
reactor; shown is a view of the end that attaches to a universal bay
on the system. See figs. S2, S4, and S5 for details of the fluidic and
electrical connections in the universal bay. (D) Schematic representation
of the configuration shown in (B) and available modules.
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provides generality and flexibility to the system
(30, 31) by performing local optimizations around
the best conditions while continuously searching
other, more distant regions to ensure that a global
(rather than local) optimum is found. Alterna-
tively, faster optimization procedures could be
implemented, but with a trade-off in requiring
prior knowledge of the system and an attendant
loss of generality (31).
(56) of configurations of reactor modules are pos-
sible, we reasoned that the system should have
the intrinsic capacity to perform automated opti-
mization of a large range of chemical transfor-
mations and multistep syntheses. We selected six
important and widely used reactions to exem-
plify this point: Buchwald-Hartwig amination,
Horner-Wadsworth-Emmons olefination, reduc-
tive amination, Suzuki-Miyaura cross-coupling,
nucleophilic aromatic substitution (SNAr), and a
visible light photoredox reaction. We also selected
a two-step process that would inform us about the
amenability of the system toward multistep reac-
tion optimization, which is especially useful in
cases wherein the product of the first reaction is
of limited stability (for example, ketene genera-
tion followed by alkene cycloaddition).
For these investigations, we used a general
four-step protocol (Fig. 1A) in each optimization:
(i) design of the reaction sequence (selecting the
reagents, solvents, and catalysts); (ii) attachment
of the appropriate module to each bay and load-
ing of the reagents and solvent feeds; (iii) selection
of the parameter boundaries (time, temperature,
catalyst loading) within which the system will
perform the optimization; and (iv) execution of
the automated optimization. We also found that
remote operation and monitoring of the system
during an optimization are possible with any
smartphone, tablet, or computer that has inter-
net access.
coupling of 1-bromo-4-methoxybenzene (1) with
4-methoxyaniline (2) under basic conditions was
investigated using the recently reported (33)
BrettPhosPdG3 pre-catalyst (3). Figure 2A shows
the platform setup for the reaction optimization.
Bays 1 and 2 were used for reagent addition and
mixing, and the amination itself was performed
in bay 3 using a heated reactor. Introduction of
toluene and water in bay 4 then enabled a con-
tinuous liquid-liquid separation in bay 5. A 60-nl
sample of the organic layer was analyzed directly
by an in-line HPLC system, with the conversion
of aniline 2 assayed relative to an internal stan-
dard. The complete results of the automated
optimization sequence may be found in fig. S17
and tables S1 and S2. The desired secondary
amine 7 was obtained in a 72% yield after the
in-line purification. The reproducibility of the
system that we observed also merits comment.
The 72% yield obtained after the automated op-
timization of the Buchwald-Hartwig amination
was replicated (72% yield) by another user oper-
ating the system under the same conditions, i.e.,
without reoptimization.
With the optimum conditions in hand, we
next used the system to investigate the substrate
scope under identical conditions (Fig. 2C), thereby
providing the opportunity to study valuable
structure-reactivity relationships in an exception-
ally controlled manner. If desired, the user may
also optimize each individual case using this auto-
mated system. The palladium-catalyzed coupling
reactions of the anilines and arylbromides exam-
ined proceeded with overall chemical yields of 72
to 99% and with material throughput rates of 430
to 816 mg/hour.
Automated optimization of
chemical transformations
We began our tests of the system with a Paal-
Knorr pyrrole synthesis. In addition to being a
classic method for assembling important hetero-
cycles, it provided us the twofold opportunity to
compare the performance of the system with
others we had investigated (28) and also to vali-
date a suite of analytical methods (HPLC, MS,
IR, and Raman; see the supplementary materials
for details). Subsequently, these activities also
enabled us to evaluate the portability of the plat-
form by performing the same automated opti-
mization in two different laboratories. To this
end, after optimization of a Paal-Knorr pyrrole
synthesis (see supplementary materials) in one
laboratory (Jensen), the system was transferred
to another (Jamison), wherein the optimization
converged independently on the same reaction
conditions. In this exercise, only the platform and
the software were transferred between the groups;
two different HPLCs were used, providing addi-
tional validation of the flexibility and generality
of the system.
The Buchwald-Hartwig amination is central
to many areas of chemical research, including
the discovery and development of pharmaceuti-
cals, agricultural chemicals, and organic light-
emitting diodes (32). The palladium-catalyzed
We next evaluated the generality and ease of
use of the system described above. As thousands
Fig. 2. Buchwald-Hartwig cross-coupling automated optimization
and examples. (A) The system setup for the optimization of the
Buchwald-Hartwig cross-coupling using a heated reactor in bay 3 and
a liquid-liquid separator in bay 5. (B) Summary of the optimal conditions
found for the Buchwald-Hartwig amination (details in fig. S17 and
tables S1 and S2). (C) Substrate scope evaluated using optimized
conditions determined in (B). Isolated yield after column chromatography.
T, temperature; tR, residence time.
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Optimization of six additional reactions and
sequences allowed us to test the generality of the
system. Each of the following employed a unique
array of modules in the five universal bays: Horner-
Wadsworth-Emmons (HWE) olefination, reduc-
tive amination, Suzuki-Miyaura cross-coupling,
SNAr, the generation of an iminium electrophile
via photoredox catalysis, and ketene generation
with subsequent alkene cycloaddition (Figs. 3
and 4; complete details in supplementary mate-
rials). The HWE, for example, is a two-step pro-
cess (phosphonium ylide generation and alkene
formation), and optimization in the system used
heated reactors in bays 1 and 2. As shown in Fig. 3B,
after the automated optimization of the model
reaction (32 reaction conditions examined in a
continuous 10-hour period), a range of olefins
were obtained under the same conditions in high
yield with throughput up to 3.1 g/hour. Although
the structures of the aldehyde, ketone, and phos-
phonate reagents were varied, all those examined
underwent efficient HWE transformations under
the optimized conditions.
In a similar manner, but with another system
configuration, a two-step reductive amination se-
quence was optimized (33 experiments in 14 hours,
Fig. 3C). The system discovered that a total resi-
dence time of 2.05 min was sufficient for the
synthesis of amine 33. The stereochemical integ-
rity of chiral amines and aldehydes was preserved
in the two cases that the system examined (38
and 40). The more hindered amine 36 provided a
lower yield when subjected to the previously opti-
mized reaction condition; incomplete imine for-
mation (bay 1) followed by direct reduction of the
aldehyde (bay 2) afforded appreciable amounts
of an alcohol byproduct (details in supplemen-
tary materials). Nevertheless, highlighting another
feature of the system, a rapid reoptimization of the
imine formation step provided 36 in 97% yield.
The Suzuki-Miyaura cross-coupling optimization
(Fig. 3E) was performed using a packed-bed
Fig. 3. Three different transformations optimized and demonstrated in
the system. (A) HWE auto-optimization (details in fig. S17 and tables S1 and S3).
(B) Substrate scope evaluation under optimized conditions in (A). (C) Reductive
amination auto-optimization (details in fig. S18 and tables S1 and S4).
(D) Substrate scope evaluation under optimized conditions in (C). (E) Suzuki-
Miyaura cross-coupling auto-optimization (details in figs. S19 and S20 and
tables S1 and S5). (F) Substrate scope evaluation under optimized conditions in
(E). Isolated yield after column chromatography. T, temperature; tR, residence time.
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Fig. 4. Automated optimization of substitution and photochemical
reactions using the platform. (A) Summary of the optimal conditions
found for the SNAr (details in fig. S21 and tables S1 and S6). (B) Substrate
scope evaluated using optimized conditions established in (A). (C) Summary
of the optimal conditions found for the photoredox reaction (details in
figs. S22 and S23 and tables S1 and S7). (D) Substrate scope evaluated
using optimized conditions determined in (C). Isolated yield after column
chromatography. (E) Auto-optimization of ketene generation and [2 + 2]
cycloaddition (details in fig. S24 and tables S1 and S8). (F) Multistep
reaction sequence in (E) and substrate scope evaluation under optimized
conditions in (E). Isolated yield after column chromatography. T, temperature;
tR, residence time.
reactor containing the solid-supported catalyst
siliaCat DPP-Pd (34). Automated experimenta-
tion (30 experiments in 8 hours) provided condi-
tions that afforded 45 in 96% yield and were
transposed to a wide variety of other cross-coupling
partners (Fig. 3F, 46 to 54). Because the reagent
feed can be exchanged rapidly, only 8 hours were
required to provide 1 g of each of nine desired
products, demonstrating the ability of the system
to generate synthetically useful quantities of de-
sired materials in a short period of time.
obtained several distinct structures (60 to 68) in
high yields (88 to 99%). The rapid generation
by way of a reactive intermediate, iminium ion
74 (Fig. 4D). In this optimization, the boundary
parameters were selected based on batch prece-
dents from the Stephenson (35) and Rueping
(36–38) groups. Under the optimized conditions,
reactions were complete within 3 min, whereas
previous reports indicate that a 3-hour reaction
time was generally required in batch (4, 39, 40).
To investigate the ability of the system to opti-
mize multistep sequences, we targeted a ketene
generation and Lewis acid–promoted alkene cy-
cloaddition to form cyclobutanones, inspired by
a precedent from Brown (41). The first reaction
produces the reactive ketene intermediate 85
from the acyl chloride 81, and cycloaddition of
of a library of nine compounds (10 mg scale) was
achieved in a total time of only 20 min, demon-
strating the potential of this system for use in
discovery efforts in, for example, the pharmaceu-
tical industry. Furthermore, because the system
operates at steady state using continuous-flow
chemistry, scaling up of a reaction occurs readily
and rapidly (4). For example, 10 mg of 59 was
obtained in 5 min, corresponding to 7.7 g of
amine 59 per day. The LED photoreactor, which
includes active cooling for temperature control,
was then applied to an automated optimization
of a photoredox reaction (Fig. 4C) that proceeds
We next explored an SNAr reaction. Automated
optimization of the model reaction (Fig. 4A) re-
quired 12 hours and afforded tertiary amine 59 in
94% yield. Using the optimized conditions, we
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cyclohexene 82 affords cyclobutanone 84. In-
line quench and separation prior to analysis and
product collection purifies the reaction mixtures.
As the entire system may be operated under an
inert atmosphere (N2, for example), it provides
a means for the safe handling of the pyrophoric
Lewis acid ethylaluminum dichloride 83 by mini-
mizing direct manipulation of this reagent by the
user. Also noteworthy was that the objective func-
tion used in the optimization included terms for
both product yield and selectivity for 84, high-
lighting the flexibility of the optimization approach.
The desired cyclobutanone 84 was produced in
77% yield and 14:1 diastereomeric ratio, compa-
rable in both efficiency and selectivity to the
Brown precedent, but at much higher temper-
ature (78°C versus <23°C). This approach also
expanded the scope of this valuable transforma-
tion; under the optimized conditions, cyclobuta-
nones from tri- and tetrasubstituted alkenes, not
possible in the originally reported conditions (41),
may now be synthesized.
mizing literature procedures should thus dimin-
ish in its frequency. Moreover, the data obtained
in each optimization and evaluation may build a
foundation of knowledge useful in machine
learning pursuits.
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ACKNOWLEDGMENTS
Funding: This project was supported by the Army Research Office
(ARO) under contract W911NF-15-1-0183. Author contributions:
All authors participated in writing and revising the manuscript.
A.-C.B. developed chemistry, designed and executed optimizations,
analyzed optimization results, and refined the system; A.A.
designed, built, and refined the system hardware; K.C.A. developed
the user interface and optimization algorithms, refined the
system, designed and executed optimizations, and analyzed
optimization results; M.G.R. developed chemistry, designed and
executed optimizations, and analyzed optimization results; A.A.B.
developed chemistry and designed the system; J.T. and B.Y.
contributed to parts design, testing, and building the system; K.F.J.
and T.F.J. designed the system, analyzed optimization results,
provided oversight for the project, and secured funding for the
project. Competing interests: A.A. is the founder of Zaiput Flow
Technologies. T.F.J. is a cofounder of Snapdragon Chemistry,
Inc., and a scientific adviser for Zaiput Flow Technologies,
Continuus Pharmaceuticals, Paraza Pharmaceuticals, and
Asymchem. A.A., K.C.A., A.A.B., K.F.J., and T.F.J. are inventors on
patent application PCT/US2017/030649 (International Patent
Application WO2017/192595 A1) submitted by the Massachusetts
Institute of Technology that covers the system described in the
manuscript. Data and materials availability: The data from the
optimizations, the MATLAB code, and the LabView code are
available at https://github.com/kosiaroh/Reconfigurable_System_
for_Automated_Optimization_of_Diverse_Chemical_Reactions. The
other data reported in this paper are available in the article or in
the supplementary materials.
Outlook
In conclusion, we have developed a fully inte-
grated, versatile system and demonstrated the
automated optimization of a diverse array of
chemical reactions. The examination of the sub-
strate scope in each of the seven reactions and
multistep sequences afforded greater than 50
compounds in high yield. This reconfigurable
system has changed the way we approach ex-
perimentation and optimization in several ways.
It accelerates the synthesis of lab-scale quantities
of molecules and allows investigators to direct
more of their efforts toward the creative aspects
of research. The system’s generality and ease of
use obviates the need for expertise in flow chem-
istry to realize its benefits. The system also pro-
vides a means to optimize and evaluate the scope
of a reaction in a matter of hours or days and do
so under identical reaction conditions for each
substrate of interest, if desired. Transfer of ex-
perimental results is now direct, electronic, and
seamless; the time-consuming exercise of reopti-
SUPPLEMENTARY MATERIALS
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Materials and Methods
Figs. S1 to S81
28. J. S. Moore, K. F. Jensen, Org. Process Res. Dev. 16, 1409–1415 (2012).
29. W. Huyer, A. Neumaier, ACM Trans. Math. Softw. 35, 9:1–9:25 (2008).
30. V. Sans, L. Cronin, Chem. Soc. Rev. 45, 2032–2043 (2016).
31. B. J. Reizman, K. F. Jensen, Acc. Chem. Res. 49, 1786–1796 (2016).
32. P. Ruiz-Castillo, S. L. Buchwald, Chem. Rev. 116, 12564–12649 (2016).
Tables S1 to S8
22 January 2018; accepted 25 July 2018
10.1126/science.aat0650
Bédard et al., Science 361, 1220–1225 (2018)
21 September 2018
6 of 6

Reconfigurable system for automated optimization of diverse chemical reactions
Anne-Catherine Bédard, Andrea Adamo, Kosi C. Aroh, M. Grace Russell, Aaron A. Bedermann, Jeremy Torosian, Brian Yue,
Klavs F. Jensen and Timothy F. Jamison
Science 361 (6408), 1220-1225.
DOI: 10.1126/science.aat0650
A self-optimizing reactor
Chemists spend a great deal of time tweaking the conditions of known reactions. Small changes to temperature
and concentration can have a big influence over product yield. Bédard et al. present a flow-based reaction platform that
carries out this laborious task automatically. By using feedback from integrated analytics, the system converges on
optimal conditions that can then be applied with high precision afterward. A series of modules with heating, cooling,
mixing, and photochemical capabilities could be configured for a broad range of reactions. These include homogeneous
and heterogeneous palladium-catalyzed cross-coupling, reductive amination, and the generation of sensitive
intermediates under an inert atmosphere.
Science, this issue p. 1220
ARTICLE TOOLS
http://science.sciencemag.org/content/361/6408/1220
SUPPLEMENTARY
MATERIALS
http://science.sciencemag.org/content/suppl/2018/09/19/361.6408.1220.DC1
REFERENCES
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