Optimizing organic electrosynthesis through controlledvoltage dosing and artificial intelligenceDaniela E. Blancoa, Bryan Leea, and Miguel A. Modestinoa,1

aDepartment of Chemical and Biomolecular Engineering, New York University, Brooklyn, NY 11201

Edited by Richard Eisenberg, University of Rochester, Rochester, New York, and approved July 30, 2019 (received for review June 10, 2019)

Organic electrosynthesis can transform the chemical industry byintroducing electricity-driven processes that are more energy effi-cient and that can be easily integrated with renewable energysources. However, their deployment is severely hindered by thedifficulties of controlling selectivity and achieving a large energyconversion efficiency at high current density due to the low solu-bility of organic reactants in practical electrolytes. This control canbe improved by carefully balancing the mass transport processesand electrocatalytic reaction rates at the electrode diffusion layerthrough pulsed electrochemical methods. In this study, we explorethese methods in the context of the electrosynthesis of adiponitrile(ADN), the largest organic electrochemical process in industry. Sys-tematically exploring voltage pulses in the timescale between 5and 150 ms led to a 20% increase in production of ADN and a250% increase in relative selectivity with respect to the state-of-the-art constant voltage process. Moreover, combining this sys-tematic experimental investigation with artificial intelligence (AI)tools allowed us to rapidly discover drastically improved electro-synthetic conditions, reaching improvements of 30 and 325% inADN production rates and selectivity, respectively. This powerfulAI-enhanced experimental approach represents a paradigm shiftin the design of electrified chemical transformations, which canaccelerate the deployment of more sustainable electrochemicalmanufacturing processes.

organic electrosynthesis | neural network | voltage dosing | electrochemicalpulse techniques | artificial intelligence

The electrification of the chemical industry is a major step inthe integration of renewable electricity in the industrial sec-

tor (1, 2). Such an endeavor can be achieved through the imple-mentation of scalable electrochemical processes for chemicalmanufacturing. More than 75% of the chemicals produced in-dustrially are organic in nature (3, 4), and while there are hun-dreds of known organic electrochemical transformations (5), theirindustrial implementation is limited to only a few examples (6, 7).Organic electrosynthetic processes offer synthetic routes that relyon more environmentally benign reacting media, often water-based electrolytes, and require mild pressures and temperaturesfor operation. However, severe obstacles are faced given the lowreactant solubility in water, the limited electrochemical stability ofthe electrolyte, and the difficulty in controlling multiple reactionpathways (1, 8) (Fig. 1). Overcoming these obstacles requires arational design of electrocatalytic processes—encompassing boththe selection of efficient electrocatalysts as well as control over theelectrochemical environment surrounding them.The largest industrial organic electrosynthetic process is the

electrohydrodimerization of acrylonitrile (AN) to adiponitrile(ADN), the main precursor to Nylon 6,6. The chemistry of thisreaction was first discovered in the 1960s, and a productionprocess was subsequently developed by Monsanto, which con-tinues to be implemented in industry to date (9–34). In a recentstudy, our group thoroughly explored this reaction and provideda description of the electrochemical and transport processes thataffect production rates and selectivity. This study demonstratedthat the selectivity of each of the products—hydrogen, propionitrile(PN), ADN, and AN-derived oligomers—was controlled by mass

transport (35). PN is the most common by-product in the pro-cess, and its production is favored at high current densities whenthe reactant concentration in the electrical double layer (EDL) islow (Fig. 1). Conversely, AN-derived oligomers, such as 1,3,6tricyanohexane, are favored at low current densities when the ANconcentration in the EDL is high. Hydrogen is another importantby-product, and its production is controlled by the combined useof cadmium or lead electrodes with high overpotential for thehydrogen evolution reaction and the addition of tetraalkylammoniumions to the electrolyte to restrict the presence of water moleculesat the EDL.The complex reaction dynamics involved in ADN production

require a careful control of reactant diffusive fluxes to the elec-trode to maintain intermediate concentrations of AN in the EDLat high current densities. One way that this can be achieved is bydynamically regulating the electron transfer rate and thus, mod-ulating the consumption of reactants and the generation ofcharged intermediates. Such dynamic use of electrochemical rateshas been widely used for mechanistic and kinetic studies (36) inthe form of electrochemical pulsed techniques. Electrochemicalpulsing results in the periodic renewal of the diffusion layer (37),enhancing the local concentration of reactants and thus, providingan additional handle to control the composition of the EDL.Despite its possible advantages, the implementation of pulsedorganic electrosynthesis has been limited to a few examples

Significance

The electrification of chemical manufacturing can enable theintegration of renewable electricity sources into a sustainablechemical industry. The combined experimental and artificialintelligence-enabled approach discussed in this work representsa paradigm shift in the electrosynthesis field and can help ac-celerate the industry’s transformation. The strategy that wepresent improves reaction selectivity (by 325%) and productionrates (by 30%) for the largest organic electrochemical process inindustry, the electrosynthesis of adiponitrile (ADN). These ad-vances are achieved by carefully tuning the electrochemical en-vironment around the electrocatalyst surface and implementingdata-driven models to rapidly elucidate optimal reaction condi-tions unpredictable by existing physical models. Although thisapproach was demonstrated for ADN production, it can serve asa universal model for sustainable electrosynthesis development.

Author contributions: D.E.B. and M.A.M. designed research; D.E.B. and B.L. performedresearch; D.E.B. and M.A.M. analyzed data; and D.E.B. and M.A.M. wrote the paper.

Conflict of interest statement: D.E.B. and M.A.M. are cofounders of and hold a financialinterest in Sunthetics INC, a startup company operating in the sustainable chemicalmanufacturing space. A provisional patent titled “Controlling product distribution onthe electrohydrodimerization of acrylontirile to adiponitrile with electrochemical poten-tial pulses” (patent application no. US621827021) has been filed.

This article is a PNAS Direct Submission.

Published under the PNAS license.1To whom correspondence may be addressed. Email: modestino@nyu.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1909985116/-/DCSupplemental.

Published online August 21, 2019.

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where transport and reaction control can be implemented toobtain complex functionality in polymers, control enantiose-lectivity in complex organic reactions, or enhance the productionrate of oxidation products (38–46). More recently, pulsed elec-trosynthesis has been implemented in the electroreduction ofCO2, where potential pulses have been shown to strongly influencethe product distribution through a combination of electrode sur-face modifications, alteration of the intermediates’ desorptionenergies, and control of reactant concentration in the EDL (47–50). These examples demonstrate the extent of reactivity controlthat can be achieved by imposing electrochemical pulses andhow this control can be harnessed to enhance the performanceof electrocatalytic processes.In this study, we first systematically explore the effects of

voltage dosing in the production rates of desired and undesiredproducts in the context of ADN electrosynthesis. Given thecomplexity of the reaction and transport processes under dynamicconditions, machine learning tools are deployed to uncover theADN reaction landscape over a broad range of accessible pulsesequences, identify ideal operating conditions, and demonstrateoptimal electrochemical environments that suppress undesirableside products and maximize ADN production rates.

Understanding the Dynamics of Organic Electrosynthesisunder Pulsed PotentialsThe electrosynthesis of ADN under direct current operation ismass transport controlled, limiting the selectivity of the reactionat the desirable high production rates (9, 15, 24, 26, 27, 35).Square potential waveforms, as depicted in Fig. 2, can mitigatethese mass transport limitations through the renewal of reactantconcentration in the EDL and thus, enhance selectivity towardADN. However, the effect of electrochemical pulses can go be-yond increasing the AN concentration at the electrode interface.During cathodic times, the working electrode is charged negatively,

and the electron transfer rate to chemical species in the EDLdictates the conversion rate of AN to ADN and other by-products.In addition to reactive processes, charged species migrate accordingto the imposed electric field, affecting the composition of theEDL. During cathodic times, tetrabutylammonium (TBA) cationsmigrate toward the cathode, and their presence in the EDL hasbeen shown to enhance ADN selectivity by increasing the ANconcentration at the electrode surface (through favorable van derWaals interactions) while expelling water molecules.During resting times, faradaic reactions are limited or com-

pletely suppressed, allowing the diffusion of reactants from thebulk to the electrode to replenish their concentration in theEDL. At the same time, as the EDL discharges, TBA and pos-itively charged ions diffuse away from the cathode, affecting thewater concentration in the near-electrode region. This has pro-found effects in reactivity after cathodic potentials are reestab-lished, as the increased water concentration in the electrodesurface can enhance hydrogen evolution rates and favor the earlyprotonation of intermediate anions (Fig. 1), thus leading to PNformation. The following subsections systematically explore therelative impact of each of these phenomena by probing the ef-fects of pulse times, EDL composition, and current densities onADN production rates and selectivity.

Effect of Cathodic and Resting Times. Under square potentialwaves, a cathodic potential, Ec, is applied with a pulse length tc,while a resting potential, Er, is applied during a resting pulse oflength tr. Fig. 3 describes the effects on product distribution forpotential waves with Ec = −3.5 V and Er = 0 V (vs. Ag/AgCl) andvarying tc and tr pulse lengths. The Ec implemented results in acurrent density of −60 mA cm−2 under DC operation; a valuewhere mass transport limitations start to dominate (35). Productdistribution is described in terms of (i) average ADN productionrate, (ii) average PN production rate, and (iii) relative selectivity

Fig. 1. Proposed cathodic reaction pathways for the electrohydrodimerization of AN to ADN. The bar graph shows the production rates for ADN and PNunder DC operation with varying current densities. The electrolyte consisted of 0.5 M sodium phosphate, 0.03 M EDTA, 0.02 M TBA hydroxide, and 0.6 M AN inwater. The temperature was 25 °C, and the pH was maintained at 13.

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(as measured by the ratio between ADN and PN production rates)for 20-min bulk electrolysis experiments. The results demonstratethat the production rates for both species can be controlledthrough potential pulses (note the different scales for the pro-duction rate of each species). Furthermore, the average pro-duction rates of both ADN and PN increase with tc, but theproduction of PN is severely suppressed for tc < 150 ms withrespect to DC operation. However, a net increase in ADNproduction is found for tc = 150 ms and tr ranging from 5 to 20ms, with a maximum improvement of 20% in ADN generationat Ec = −3.5 V, Er = 0 V, tc = 150 ms, and tr = 10 ms. Moreover,the relative selectivity for ADN over PN, measured as the ratiobetween ADN and PN (Fig. 3), is significantly improved underpulsed electrosynthesis, reaching improvements of >250%.These observations suggest that mass transport effects areeffectively mitigated with the periodic regeneration of reac-tant concentration, limiting PN formation and favoring ADNproduction.To better understand the effects of pulsed potentials in the

electrosynthesis of ADN, it is important to recognize that largervalues of tc and lower values of tr result in longer overall reactiontimes (defined as the sum of all tc values in the 20-min bulkelectrolysis). This, in turn, increases the number of chargestransferred to AN, thus raising the average ADN and PN pro-duction rates. However, when the conversion rate of AN to ADNand PN is normalized by the overall cathodic time, an upwardtrend still persists with respect to tc and the overall cathodic time(SI Appendix, Fig. S1). This observation indicates that the netincrease in ADN and PN production rates cannot be fully ex-plained by increased overall cathodic times and that capacitiveeffects in the EDL need to be considered. The EDL capacitancewas estimated from electrochemical impedance spectroscopymeasurements (SI Appendix, Fig. S2) to be 46 μF, leading to acharacteristic charging time of 1.2 ms. This implies that chargingcurrents account for <3% of the faradaic current towards ADNand PN with tc = 150 ms and tr = 10 ms (SI Appendix has details oncalculation). However, the fraction of the current that is capac-itive in nature becomes more significant at smaller tc values, asthey approach the characteristic charging time. In these cases, a

larger fraction of the current does not contribute to the re-duction of AN, thus lowering the net production rate of ADN.In the same way, mass transport limitations are mitigated by theshortened faradaic currents, resulting in lower PN formation.

Fig. 3. ADN production rate, PN production rate, and ADN:PN productionratio at −3.5 V cathodic potential and 0 V resting potential (vs. Ag/AgCl) forvarious tc and tr combinations. The dotted lines represent the ADN:PN ratioand PN production obtained under DC operation at −3.5 V. Reported valueshave <3% SD.

Fig. 2. The graph in Left shows a DPA potential waveform, highlighting the resting potential (Er), cathodic potential (Ec), resting time (tr), and cathodic time(tc). The graphical representations in Center and Right describe the effects that cathodic and resting potentials, respectively, can have on the mass transport ofdifferent electrolyte species.

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Although PN production is effectively reduced with all pulsetimes tested, increasing ADN production requires careful balanceof cathodic times, as longer tc can help increase the fraction offaradaic current during each pulse but could also accelerate reactantdepletion, leading to higher PN production rates.

Effect of EDL Composition. To gain further insights into the com-position of the diffusion layer, a transient mass transport modelwith constant current pulses was built to estimate the changes inAN concentration as a function of tc and tr. Although the modeldoes not capture the complex capacitive behavior of the EDL, itqualitatively demonstrates that AN concentration at the elec-trode interface can be partly regenerated during resting times(Fig. 4) and that complete renewal is unlikely to be achieved withthe pulsed times explored in the experiments.

The increase in reactant concentration at the electrode surfaceseems to effectively mitigate mass transport limitations enhanc-ing AN conversion as observed in Fig. 3. Moreover, changes inproduct distribution can be also partly explained by changes inthe local concentration of reactants at the EDL. Low reactantconcentrations favor PN formation (as it requires 1 AN equiva-lent), while reactant accumulation can instead favor AN-derivedoligomers (requiring 3 or more AN equivalents). This suggeststhat intermediate concentrations of AN are required to promoteADN formation. The range of tc and tr values explored in thisstudy demonstrates that a suppression of over 80% in PN pro-duction can be achieved and that optimal surface concentrationsof reactants can be attained to promote ADN formation. Althoughhigher surface concentration of AN under pulsed electrosynthesiscan help explain this observed reduction in PN production, itdoes not explain the increased PN production observed at longertr. One possible explanation is that, as TBA ions diffuse away fromthe EDL during resting times, water molecules increase theircoverage of the electrode surface, thus favoring the early protonationof intermediate AN radical anions, leading to PN formation.

Effect of Current Density. Given that product distribution andconsumption rate of AN are strongly dependent on the masstransport of reactants to the EDL, it is expected that the mag-nitude of the cathodic potential (Ec) and its corresponding cur-rent density would have a strong effect in the electrosynthesisprocess. Fig. 5 shows the ADN:PN production ratio for varyingtc and tr values and Ec = (a) −2.5, (b) −3.5, and (c) −4.5 V(vs. Ag/AgCl). These potentials corresponded to current densitiesof −30, −60, and −90 mA cm−2, respectively, where weak, slightlydominating, and fully dominating mass transport limitations areexpected. Large variations in product distribution are observedfor different pulse durations, and the ratio obtained under DCconditions (dotted lines in Fig. 5) decreases with increasingcurrent density. The results demonstrate that optimal tc and trvalues to maximize the ADN:PN ratio depend on the currentdensity. For Ec = −2.5 V, the low cathodic current density resultsin a longer AN depletion time, allowing for the implementationof longer tc values (i.e., 100 to 1,500 ms). In this case, the ADNproduction rate seems to be strictly limited by the overall cathodictime (SI Appendix, Fig. S1), since the characteristic charging timeof the EDL (approximately milliseconds) is negligible with respectto the tc applied. Conversely, when Ec = −4.5 V, the consumptionof AN is so rapid that optimal tc values are limited to only 5 to20 ms. This implies that most of the cathodic currents are used inthe charging of the EDL, severely limiting the conversion of AN.The highest increase in ADN:PN ratio with respect to DC operationwas found at the intermediate Ec = −3.5 V, where mass transportlimitations begin to dominate and optimal tc values range between20 and 150 ms.

Fig. 4. Effect of (A) tr and (B) tc on the simulated reactant concentration atthe electrode surface at −60-mA cm−2 cathodic current and 0-mA cm−2

resting current. The solid black lines in A and B describe the concentrationprofile under DC conditions at −60 mA cm−2. Initial bulk AN concentration isfixed at 0.6 M, and only diffusion-based reactant replenishment is consideredin the model. The color map presented in C shows the average surface concen-tration of AN for combinations of tc and tr in the range of conditions studied.

Fig. 5. ADN:PN production ratio at (A) −2.5, (B)−3.5, and (C) −4.5 V vs. Ag/AgCl cathodic potentialsfor various tr and tc combinations with <4% SD.Resting potential is maintained at 0 V vs. Ag/AgClreference electrode. The dotted lines represent theADN:PN ratio obtained under DC operation at−2.5, −3.5, and −4.5 V vs. Ag/AgCl in A to C,respectively.

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An Artificial Intelligence Approach to Electrosynthesis ProcessDevelopment. The systematic experimental approach describedabove resulted in the identification of a pulse sequence thatimproved the production of ADN by 20% (Ec = −3.5 V, Er =0 V, tc = 150 ms, tr = 10 ms) or the ratio between ADN:PNproduction by 250% (Ec = −3.5 V, Er = 0 V, tc = 50 ms, tr = 5ms). While our observations and physical understanding couldqualitatively explain the trends of ADN and PN production ob-served for varying Ec, tc, or tr, quantitatively identifying the relation-ships between these parameters and electrosynthesis performancemetrics would require an extensive high-throughput experimentalcampaign. An alternative approach to achieve quantitative process–performance relationships is to implement data-derived modelsthrough the use of modern artificial intelligence (AI) techniques.Tools, such as artificial neural networks (ANNs), can use alimited experimental dataset to infer trends in performance forcomplex physical processes (51–54). For the purpose of acceleratingthe experimental efforts of this study, ANNs were implemented torapidly evaluate a complete landscape of pulse potential waveformsand identify optimum operation electrosynthesis conditions.Fig. 6, Top presents the predicted ADN production rate from

an ANN generated with tr and tc as inputs and trained with theexperimental data presented in Fig. 3. Ranges that maximize(higher tc and lower tr) or minimize (lower tc and higher tr) ADNproduction are easily identified with this representation and arein close agreement with the experimental data collected (i.e., <2%root mean square error). Furthermore, the behavior predicted bythe ANN showed a maximum on ADN production in the tc rangebetween 115 and 130 ms and the lowest tr (5 ms) accessible by ourinstrumentation. After experimentally evaluating the performanceof the reaction under these previously untested conditions, wefound that a combination of tc = 120 ms and tr = 5 ms led to anunprecedented increase of 30% in ADN production rate withrespect to DC operation (Fig. 6, Bottom). Furthermore, this en-hancement in ADN production was accompanied by a reductionin PN production, which led to an enhancement in relative se-lectivity of 325%.

ConclusionsThis study introduced an AI-enhanced approach to control andoptimize electrochemical processes through pulsed electrosyn-thesis. The results presented demonstrate the potential to improveselectivity and production rates by carefully controlling the com-position of the EDL using voltage pulses. The advantages of op-timized pulsed electrolysis were demonstrated in the context of themost important organic electrosynthetic process in industry: theelectrohydrodimerization of AN to ADN. We showed that pulsedelectrosynthesis techniques can be used to control reactant con-centration in the EDL, effectively mitigating mass transport limi-tations and enabling operation at high current densities. Moreover,potential pulse times influence multiple rates, including the diffu-sion rate of reactants and products, migration rate of intermediatesand supporting ions, and capacitive charging rate of the electrodesurface. The balance between these rates controls the compositionof the EDL and ultimately, determines the production rate of ADNand its selectivity.Under the experimental space explored, cathodic times in the

tens to hundreds of milliseconds range with short resting times inthe 1 to tens of milliseconds range led to the most significantimprovements in performance. Our initial systematic experi-mental campaign led to the discovery of pulse times that im-proved selectivity by 250% and ADN production rate by 20%.Using the experimentally obtained data to train an ANN allowedus to identify a set of pulse times that increased the improve-ments to 325% in selectivity and 30% in ADN production rate—the largest reported improvement since the discovery of thisreaction more than 50 y ago. This approach represents a para-digm shift in electrocatalysis research, where reaction selectivity

is controlled by carefully tuning the electrochemical environmentaround the electrocatalyst surface and data-driven models areused to elucidate optimal conditions unpredictable by existing physicalmodels. Although this approach was demonstrated for ADN pro-duction, it can have broad implications for the electrification of thechemical industry, serving as a universal model of electrosynthesisprocess development for a vast number of organic transformations.

MethodsMaterials. All chemicals were acquired from Sigma-Aldrich, including sodiumphosphate, TBA hydroxide, ethylenediaminetetraacetic acid disodium salt,and AN. A fresh aqueous catholyte solution with 0.5 M (8 wt %) sodiumphosphate, 0.03 M (1 wt %) EDTA, and 0.02 M (0.5 wt %) TBA hydroxide wasprepared before adding 0.6 M (3 wt %) AN for each experiment. A 1 Msulfuric acid solution was used as anolyte, and diffusion through the mem-brane was negligible given that the cathodic chamber pH remained constantthroughout the experiments.

Fig. 6. (Top) Prediction of ADN production rates with an ANN using tr and tcas inputs. The 2-layer ANN was trained using the Levenberg–Marquardtlearning function and 9 hidden neurons, achieving a root mean square errorof 2%. The colored circles represent the experimental data used for networktraining. (Bottom) Comparison of the ADN and PN production rates obtainedunder best operation conditions found through DC operation optimization, asystematic study of the effect of pulsed potentials, and an ANN-predicted optimalperformance prediction. All electrode potentials are measured against Ag/AgClreference electrode.

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A 1-cm2 cadmium foil (American Elements) working electrode, a 100-meshwoven platinum gauze (Alfa Aesar) counterelectrode, and an Ag/AgCl(4 M KCl) reference electrode from Pine Research Instrumentation were usedfor all experiments.

Electrochemical Characterization. A 3-electrode setup was used to study theeffect of pulsed potential techniques on the diffusion layer and the cathodichalf-cell reactions. Chronopotentiometry and differential pulse amperometry(DPA) techniques were performed for 20 min using a BioLogic VSP-300potentiostat, yielding AN conversions under 25%. The DPA technique wasimplemented with constant base potential and pulse potential values. Thebase or resting potential value was selected within the range where nofaradaic reaction occurs (0 V vs. Ag/AgCl).

A machined Teflon H cell was separated with a Nafion N117 membraneand sealed between 2 viton gaskets, avoiding the deposition of metal ionsfrom the anode on the cathode surface. Before experiments, the cadmiumelectrode surface was operated at −40 mA cm−2 for 10 to 12 h with the AN-containing catholyte solution. This electrode pretreatment enabled stableoperation over time as described previously (35). A constant electrolytevolume of 8 mL was vigorously stirred (700 rpm and a 1.2-cm-long stirringbar) in all experiments, temperature was maintained at 25 °C using a hotplate and a sand bath, and electrolyte pH was measured and kept at 11 witha B30PCI pH meter from VWR.

Chemical Analysis. The organic compounds were separated from the aqueouselectrolyte via liquid–liquid extraction with toluene. The organic phase wasthen analyzed in a Shimadzu gas chromatographer equipped with a massspectrometer GCMS-QP2010 and an Agilent 7890B gas chromatographerand 5977B mass spectrometer. Component identification and quantification

were performed using continuously updated calibration curves for AN, PN,and ADN. The presence of other minor AN-derived by-products, such as 1,3,6tricyanohexane, was not detected

Mass Transport Model. Time-dependent mass transport models were de-veloped using Matlab, describing diffusion processes from the bulk elec-trolyte to the electrode surface in a 1-dimensional geometry. Finite differenceapproximations were used to solve differential mass transport equations.Applied current density values were correlated to the reactant consumptionrate using Faraday’s law and square-wave potential waveforms wereimplemented to simulate pulsed potential techniques. Differential masstransport equations, boundary conditions, and initial conditions are furtherdescribed in SI Appendix.

ANN Simulation. A 2-layer feed-forward ANN, consisting of 9 hidden neurons,was built and trained with 16 experimental data points and a 70/15/15% datasplit for training, testing, and validation, respectively. Resting and cathodictimes were used as inputs to predict ADN production rates. Levenberg–Marquardt learning algorithm was used as the training function to optimizeperformance based on root mean square error and overall data fit. Detailson the selection of the training function and number of neurons and layersare further described in SI Appendix. Experimental data were collected with−3.5 V vs. Ag/AgCl cathodic potential and 0 V vs. Ag/AgCl resting potential.

ACKNOWLEDGMENTS. We acknowledge the support and work of Junyi Sha,Aaliyah Dookhith, Myriam Sbeiti, Dr. Brandon Fowler, and Prof. YoshiyukiOkamoto. We also acknowledge the financial support provided by the H&MFoundation through the Global Change Award and New York University,Tandon School of Engineering Startup Fund.

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