Screening Of Electrocatalysts Research Articles

Electron distribution regulating of nonmetal doped monolayer g-GaN for enhanced electrocatalytic CO2 reduction

Exploring inexpensive electrocatalysts that can efficiently and selectively convert CO2 into hydrocarbon fuels is important to promote carbon neutrality and solve the energy crisis. Current electrocatalysts, such as Cu-based alloys, single-atom catalysts, and dual-atom catalysts, use the d states of metal in the electrocatalytic CO2 reduction reaction. Inspired by this, this work studies CO2 reduction reaction from another approach. Herein, using first principles study, we systematically investigate the prospect of nonmetal (B, C, O and F) doped monolayers g-GaN as electrocatalysts for the CO2 reduction reaction. We found that nonmetal doping can effectively regulate the electron distribution and p-band center of the active center (N site), which can adjust the initial adsorption, activation degree, charge transfer amount of CO2, and promote the formation of intermediates. Interestingly, B and C doped systems have better catalytic activity for CH4, with limiting potentials of −0.61 and −0.53 V, respectively. More importantly, F doped system has higher activity and selectivity for CH3OH production and inhibit competitive HER, with lower limiting potentials of −0.60 V. This study provides a new theoretical basis for the design and screening of electrocatalysts with high activity and product selectivity using nonmetal as the active site.

Electrochemical production of hydroxylamine from nitrate on metal electrodes: A comparative study of selectivity and efficiency

Despite the great potential of electrochemical nitrate reduction as a hydroxylamine production method, this strategy has not been sufficiently examined, and the effects of electrode material type on the selectivity and efficiency of this reduction remain underexplored. To bridge this gap, the present study evaluated six metals (Ag, Cu, Ni, Sn, Ti, and Zn) as cathode materials for the electrochemical reduction of nitrate to hydroxylamine, showing that the selectivity of hydroxylamine production was maximal for Sn, while the corresponding faradaic and energy utilization efficiencies were maximal for Ti. Although all tested materials favored nitrate reduction over hydrogen evolution, the disparity in the onset potentials of these reactions did not adequately explain the variations in nitrate removal efficiency, which was found to be influenced by material resistance and charge-transfer properties. The rate constants of elementary nitrate reduction steps determined from the time-dependent concentrations of nitrate and its reduction products (nitrous acid, hydroxylamine, and ammonium) were used to calculate the selectivity and efficiency of hydroxylamine production for each electrode. In turn, these selectivities and efficiencies were correlated with the density functional theory–computed adsorption energies of a key hydroxylamine precursor on different electrodes to afford a volcano-type plot with Ti and Sn at its pinnacle. Thus, this study introduces valuable descriptors and methods for the further screening of electrocatalysts for hydroxylamine generation and the establishment of more environmentally friendly hydroxylamine production techniques utilizing sustainable electricity.

Mechanism of electrocatalytic CO2 reduction reaction by borophene supported bimetallic catalysts

Bimetal atom catalysts (BACs) hold significant potential for various applications as a result of the synergistic interaction between adjacent metal atoms. This interaction leads to improved catalytic performance, while simultaneously maintaining high atomic efficiency and exceptional selectivity, similar to single atom catalysts (SACs). Bimetallic site catalysts (M2β12) supported by β12-borophene were developed as catalysts for electrocatalytic carbon dioxide reduction reaction (CO2RR). The research on density functional theory (DFT) demonstrates that M2β12 exhibits exceptional stability, conductivity, and catalytic activity. Investigating the most efficient reaction pathway for CO2RR by analyzing the Gibbs free energy (ΔG) during potential determining steps (PDS) and choosing a catalyst with outstanding catalytic performance for CO2RR. The overpotential required for Fe2β12 and Ag2β12 to generate CO is merely 0.05 V. This implies that the conversion of CO2 to CO can be accomplished with minimal additional voltage. The overpotential values for Cu2β12 and Ag2β12 during the formation of HCOOH were merely 0.001 and 0.07 V, respectively. Furthermore, the Rh2β12 catalyst exhibits a relatively low overpotential of 0.51 V for CH3OH and 0.65 V for CH4. The Fe2β12 produces C2H4 through the *CO-*CO pathway, while Ag2β12 generates CH3CH2OH via the *CO-*CHO coupling pathway, with remarkably low overpotentials of 0.84 and 0.60 V, respectively. The study provides valuable insights for the systematic design and screening of electrocatalysts for CO2RR that exhibit exceptional catalytic performance and selectivity.

Electrocatalytic valorization of lignocellulose-derived aromatics at industrial-scale current densities

Electrocatalytic hydrogenation of lignocellulosic bio-oil to value-added chemicals offers an attractive avenue to use the increasing intermittent renewable electricity and biomass-derived feedstocks. However, to date the partial current densities to target products of these reactions are lower than those needed for industrial-scale productivity, which limits its prospects. Here we report a flow-cell system equipped with a Rh diffusion electrode to hydrogenate lignocellulose-derived aromatic monomers, such as furans and lignin monomers, to value-added chemicals. We achieve high faradaic efficiencies up to 64% at industrial-scale current densities of 300–500 mA cm−2, representing high productivities to target products. A screening of electrocatalysts indicates that only by highly-electrolyte-permeable Rh diffusion electrodes are we able to unite current density with faradaic efficiency. We apply in-situ infrared reflection–absorption spectroscopy to investigate the electrode-potential-dependent reaction pathways and intermediates, confirming a wide potential window for efficient electrocatalytic hydrogenation of lignocellulose-derived aromatics to target products.

Investigation of Iridium-Based OER Catalyst Layers in a GDE Half-Cell Setup: Opportunities and Challenges

To achieve widespread commercialization of proton exchange membrane (PEM) water electrolyzers, the optimization of iridium (Ir) utilization is crucial. Traditional full-cell-based approaches are time-consuming and labor-intensive. In this work, the feasibility of using a gas diffusion electrode (GDE) half-cell as an alternative to full-cell setups for accelerated investigation of Ir-oxide-containing anode catalyst layers (CLs) is scrutinized. Using CLs composed of Ir oxides of different intrinsic oxygen evolution reaction (OER) activity as a probe, we show that a GDE can successfully reveal the differences in the performance of the CLs. Comparison of the results obtained in the GDE to those from rotating disk electrode (RDE) and full-cell membrane electrode assembly (MEA) measurements indicate that GDE data can closely mimic both setups. However, essential discrepancies are observed between GDE and MEA, which are linked to differences in the catalyst layer | membrane interface and the presence of liquid electrolyte in the GDE setup. Our findings reveal that even though the direct comparison of the OER performance to full-cell measurements is still partially hampered, GDE half-cell setups can already be used for fundamental assessments and accelerated screening of electrocatalysts and CLs at relevant current densities up to 1.5 A cm−2.

Galvanic Bipolar Electrode Arrays with Self-Driven Optical Readouts.

In this work, we report a bipolar electrode (BPE) array system with self-driven optical readouts of the faradic current flowing through the BPEs. The BPE array system is based on the spontaneous redox reactions that are respectively occurring at opposite poles of the BPEs with appropriate electrocatalysts on the poles; this system is analogous to one consisting of galvanic electrochemical cells. The galvanic BPE array system operates in a self-powered mode that requires there to be neither a direct electrical connection nor external electrical polarization to each BPE. Importantly, the appropriate electrocatalysts on the poles play a critical role in the galvanic BPE array system to induce the spontaneous redox reactions occurring at the poles of BPEs. Moreover, the galvanic BPE array system provides self-driven optical readouts, including fluorometric and colorimetric ones, to report the faradaic current resulting from the spontaneous redox reactions on the BPE poles. Based on the unique benefits that the galvanic BPE array system has over conventional BPEs, we demonstrated the promising potential of galvanic BPE arrays for the simple yet rapid and quantitative screening of electrocatalysts for the oxygen reduction reaction as well as sensitive sensing of H2O2 in parallel.

Small-Molecule Adsorption Energy Predictions for High-Throughput Screening of Electrocatalysts.

Predicting adsorption energies of small molecules (e.g., OH, OOH, CO) on electrocatalysts involved in electrochemical reactions aids in accelerating the design and screening of electrocatalysts. Avoiding computationally expensive electronic structure calculations increases the speed of such predictions. Geometric and electronic descriptors have been reported to characterize the environment around surface active sites and predict adsorption energies. However, these descriptors cannot be used to predict adsorption energies of small molecules on various substrates, e.g., metal-oxide and nonmetal electrocatalysts. We compare the performance of these descriptors in predicting adsorption energies of small molecules on various electrocatalysts with adsorption energies calculated from density functional theory. We show that two recently developed machine learning algorithms, Crystal Graph Convolutional Neural Network (CGCNN) and Atomistic Line Graph Neural Network (ALIGNN), outperform the reported descriptors based on geometric (coordination number of the active site and its nearest neighbors) and electronic (the bond-energy-integrated orbitalwise coordination number, the electronegativity, and the number of valence electrons of the active site) properties in predicting the adsorption energies. Our results suggest that ALIGNN is almost always more accurate than CGCNN in adsorption energy predictions. The improvement ranges from 0.02 to 1.0 eV in the mean absolute errors (MAEs). We also compare the performance of CGCNN and ALIGNN algorithms in predicting the overpotentials of the oxygen evolution reaction occurring on various electrocatalysts with MAEs of 0.06 and 0.05 V, respectively.

High‐Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor

AbstractDeveloping easily accessible descriptors is crucial but challenging to rationally design single‐atom catalysts (SACs). This paper describes a simple and interpretable activity descriptor, which is easily obtained from the atomic databases. The defined descriptor proves to accelerate high‐throughput screening of more than 700 graphene‐based SACs without computations, universal for 3–5d transition metals and C/N/P/B/O‐based coordination environments. Meanwhile, the analytical formula of this descriptor reveals the structure–activity relationship at the molecular orbital level. Using electrochemical nitrogen reduction as an example, this descriptor's guidance role has been experimentally validated by 13 previous reports as well as our synthesized 4 SACs. Orderly combining machine learning with physical insights, this work provides a new generalized strategy for low‐cost high‐throughput screening while comprehensive understanding the structure‐mechanism‐activity relationship.

High-Throughput Screening of Electrocatalysts for Nitrogen Reduction Reactions Accelerated by Interpretable Intrinsic Descriptor.

Developing easily accessible descriptors is crucial but challenging to rationally design single-atom catalysts (SACs). This paper describes a simple and interpretable activity descriptor, which is easily obtained from the atomic databases. The defined descriptor proves to accelerate high-throughput screening of more than 700 graphene-based SACs without computations, universal for 3-5d transition metals and C/N/P/B/O-based coordination environments. Meanwhile, the analytical formula of this descriptor reveals the structure-activity relationship at the molecular orbital level. Using electrochemical nitrogen reduction as an example, this descriptor's guidance role has been experimentally validated by 13 previous reports as well as our synthesized 4 SACs. Orderly combining machine learning with physical insights, this work provides a new generalized strategy for low-cost high-throughput screening while comprehensive understanding the structure-mechanism-activity relationship.

Materials Screening by the Descriptor G max(η): The Free-Energy Span Model in Electrocatalysis.

To move from fossil-based energy resources to a society based on renewables, electrode materials free of precious noble metals are required to efficiently catalyze electrochemical processes in fuel cells, batteries, or electrolyzers. Materials screening operating at minimal computational cost is a powerful method to assess the performance of potential electrode compositions based on heuristic concepts. While the thermodynamic overpotential in combination with the volcano concept refers to the most popular descriptor-based analysis in the literature, this notion cannot reproduce experimental trends reasonably well. About two years ago, the concept of G max(η), based on the idea of the free-energy span model, has been proposed as a universal approach for the screening of electrocatalysts. In contrast to other available descriptor-based methods, G max(η) factors overpotential and kinetic effects by a dedicated evacuation scheme of adsorption free energies into an analysis of trends. In the present perspective, we discuss the application of G max(η) to different electrocatalytic processes, including the oxygen evolution and reduction reactions, the nitrogen reduction reaction, and the selectivity problem of the competing oxygen evolution and peroxide formation reactions, and we outline the advantages of this screening approach over previous investigations.

High-Performance Oxygen Evolution Reaction Electrocatalysts Discovered via High-Throughput Aerogel Synthesis

A combinatorial high-throughput aerogel synthesis method is developed to systematically study the synergistic catalytic effects of multiple non-noble metal elements. A large number of aerogels with different compositions were synthesized and the corresponding ternary phase diagrams of the composition–electrocatalytic performance were constructed. A batch of aerogel catalysts with outstanding catalytic performance and stability for the electrochemical oxygen evolution reaction (OER) were discovered. Amorphous Fe3Co3Ni2-(oxy)hydroxide with excellent electrocatalytic performance (Tafel slope of 49 mV dec–1 and η100 of 421.48 ± 6.41 mV) was found to achieve a mass activity of 611.23 A/gmetal at 1.63 V (vs RHE). X-ray photoemission spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) studies reveal that the incorporation of a third atom (Ni and Ce) into the binary aerogels enlarges the Co–O atomic distance and reduces the average valence state of Co, which in turn speed up the oxygen evolution reaction kinetics. This work puts forward a strategy for high-throughput synthesis and screening of electrocatalysts and provides valuable data sets for future machine learning and prediction of high-performance electrocatalysts.

(Invited) Accelerating Electrochemistry: The Development of Rapid Impedance Methods and High-Throughput Screening of Novel Oxide Electrodes for Fuel Cells and Electrolyzers

As electrochemical technologies rise to ever greater prominence in the global energy landscape, new techniques to accelerate the discovery, design, and characterization of novel electrochemical materials and devices are needed. Motivated by this opportunity, here we present our recent work in the development of novel rapid multi-dimensional impedance characterization using the Distribution of Relaxation Times (DRT) combined with high-throughput impedance-based screening of electrocatalysts for ceramic fuel cells and electrolyzers, both of which offer opportunities to accelerate electrochemical materials research and development.The DRT has been established as a versatile tool for analysis of electrochemical impedance spectroscopy (EIS) data, providing insight into the relevant electrochemical processes of fuel cells, batteries, and other devices without the constraints of an a priori model. Here, we present a method for obtaining the DRT directly from rapid time-domain measurements. This technique provides the DRT on a time-scale that is 1-2 orders of magnitude faster than conventional EIS measurements, and opens new avenues for characterization and analysis that are inaccessible and/or impractical with conventional EIS. We demonstrate how this technique can be used to construct multidimensional DRT maps as a function of variables such as applied bias, temperature, changing gas conditions, or state-of-charge (Figure 1). In addition, the model can be adapted to account for time-varying sample states, enabling meaningful analysis of unstable samples and transient phenomena such as equilibration or degradation processes. Finally, we demonstrate how hardware sampling-rate limitations can be overcome via hybrid frequency- and time-domain measurements to scan a broad range of timescales without sacrificing measurement speed. The capability of this method to resolve the DRT along additional dimensions promises to enhance the interpretability of the DRT and provide new insight to guide materials and device optimization.We then discuss how rapid impedance characterization and analysis methods can be coupled to combinatorial thin-film synthesis and high-throughput automated characterization to investigate a large family of catalytically-active triple-ionic-electronic conducting oxide perovskite materials based on the Ba(Co,Fe,Zr,Y)O3-δ (BCFZY) compositional system, which show great promise for catalyzing the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) for ceramic fuel cells and electrolyzers (Figure 2). In total, we collected and analyzed more than 2,500 impedance spectra from three combinatorial thin-film electrode libraries, comprising 432 distinct compositions. These libraries were synthesized by pulsed laser deposition and measured at three temperatures under two different gas atmospheres, enabling a new scientific insight into the trends governing electrochemical performance. Our combinatorial experiments demonstrate that Co-rich compositions achieve the lowest overall polarization resistance under both dry air and humid N2, while high Fe content may impede the performance at low-to-intermediate temperatures. Reuslts from the combinatorial experiments are supported by isotope-labled SIMS trace diffusion studies as well as by protonic-ceramic fuel cell and electrolysis cell testing of both symmetric cells and full cells that incorporated select compositions of interest. Hierarchical Bayesian analysis indicates that the performance-limiting process depends on the chemical composition, measurement temperature, and atmospheric humidity. Thus, by combining rapid analysis methods with combinatorial experimentation, we achieve a composition map of the condition-dependent electronic properties of materials in the BCFZY perovskite family for application as air electrodes in protonic ceramic fuel cell and electrolyzers. Figure 1

Automated high-throughput activity and stability screening of electrocatalysts

Common high-throughput (HT) approaches rapidly assess the activity of electrocatalyst libraries toward electrochemical conversion reactions. The short time regime on which individual measurements are performed creates a false perception of catalyst durability, masking the true catalyst performance by the omission of detailed stability assessments during HT campaigns. Here, an automated scanning flow cell coupled to an inductively coupled plasma mass spectrometer was developed, allowing a simultaneous HT determination of the catalyst activity and stability. Fe-Ni and Fe-Ni-Co oxide libraries were automatically synthesized by a custom-programmed pipetting robot and examined as an oxygen evolution catalyst in neutral media, the advancement of which remains a great challenge. Ni-rich compositions in Fe-Ni oxides show higher activity but also significant catalyst loss due to the major Ni dissolution, which triggers Fe dissolution. Co-rich compositions in Fe-Ni-Co oxides attain the best synergy between activity and stability. • Automated scanning flow cell linked to inductively coupled plasma mass spectrometer • High-throughput platform allowing simultaneous activity and stability measurement • Neutral OER activity and in situ stability of Fe-Ni and Fe-Ni-Co oxides libraries • Automated synthesis using liquid-handling robot Transition metal oxides are an abundant and low-cost material class suitable as catalyst for electrochemical conversion reactions. The vast space of possible elemental combinations to construct such catalysts necessitates the use of accelerated laboratory experiments to rapidly screen material libraries for compositions with suitable properties. Activity and stability parameters of an electrocatalyst are key metrics that are decisive for their consideration as a promising candidate. Screening both performance indicators simultaneously in a high-throughput manner is crucial. The automation of an electrochemical flow cell coupled to an inductively coupled plasma mass spectrometer allows the rapid assessment of key catalyst parameters at the same time. Accelerating the material discovery exploration can help advancing key scientific areas and scientific knowledge needed for the energy transition of the society. High-throughput screening of multicompositional catalysts for electrocatalytic reactions significantly accelerates material development and discovery. While the activity of such catalyst libraries is the prime target of high-throughput investigations, the stability is often neglected, which plays a crucial role in defining the performance of electrocatalysts. The development of an automated platform involving a scanning flow cell coupled to an inductively coupled plasma mass spectrometer allows for a rapid measurement of the catalyst activity and stability simultaneously.

Research Progress in High-Throughput Screening of CO2 Reduction Catalysts

The conversion and utilization of carbon dioxide (CO2) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO2 is a promising way to convert and utilize CO2. However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO2 reduction are reviewed. First, the mechanism of CO2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO2 electrocatalysts.

Machine Learning: A New Paradigm in Computational Electrocatalysis.

Designing and screening novel electrocatalysts, understanding electrocatalytic mechanisms at an atomic level, and uncovering scientific insights lie at the center of the development of electrocatalysis. Despite certain success in experiments and computations, it is still difficult to achieve the above objectives due to the complexity of electrocatalytic systems and the vastness of the chemical space for candidate electrocatalysts. With the advantage of machine learning (ML) and increasing interest in electrocatalysis for energy conversion and storage, data-driven scientific research motivated by artificial intelligence (AI) has provided new opportunities to discover promising electrocatalysts, investigate dynamic reaction processes, and extract knowledge from huge data. In this Perspective, we summarize the recent applications of ML in electrocatalysis, including the screening of electrocatalysts and simulation of electrocatalytic processes. Furthermore, interpretable machine learning methods for electrocatalysis are discussed to accelerate knowledge generation. Finally, the blueprint of machine learning is envisaged for future development of electrocatalysis.

Single Particle Hopping as an Indicator for Evaluating Electrocatalysts.

The design and screening of electrocatalysts for gas evolution reactions suffer from little understanding of multiphase processes at the electrode-electrolyte interface. Due to the complexity of the multiphase interface, it is still a great challenge to capture gas evolution dynamics under operando conditions to precisely portray the intrinsic catalytic performance of the interface. Here, we establish a single particle imaging method to real-time monitor a potential-dependent vertical motion or hopping of electrocatalysts induced by electrogenerated gas nanobubbles. The hopping feature of a single particle is closely correlated with intrinsic activities of electrocatalysts and thus is developed as an indicator to evaluate gas evolution performance of various electrocatalysts. This optical indicator diminishes interference from heterogeneous morphologies, non-Faradaic processes, and parasitic side reactions that are unavoidable in conventional electrochemical measurements, therefore enabling precise evaluation and high-throughput screening of catalysts for gas evolution systems.

Electrochemical Mass Spectrometry: Evolution of the Cell Setup for On‐Line Investigation of Products and Screening of Electrocatalysts for Carbon Dioxide Reduction

AbstractOne of the most important current topics in the renewable and sustainable energy scenario is the CO2 electro‐reduction reaction (CO2RR), which is an alternative and important route for its conversion into various high value‐added chemicals, therefore making up a CO2 recycling process. Despite its importance and the works already developed in this field, many challenges still need to be overcome for CO2RR to reach high values of efficiency and selectivity. This is even more challenging considering that this reaction occurs with the transfer of several electrons, making the investigation and elucidation of the reaction mechanism a real need. Thus, several characterization techniques have been employed, and specially, the on‐line Electrochemical Mass Spectrometry (EC‐MS) technique emerges as a powerful tool, thus making possible to improve the understanding of reaction pathways, through the identification of products and intermediaries, and allowing the screening of electrocatalyst potentials for CO2RR. Herein, we present the evolution of adaptations of general electrochemical cell designs for the study of the CO2RR.

Cover Feature: Electrochemical Mass Spectrometry: Evolution of the Cell Setup for On‐Line Investigation of Products and Screening of Electrocatalysts for Carbon Dioxide Reduction (ChemElectroChem 11/2022)

The Cover Feature illustrates the rational design for the development of inlet interfaces and electrochemical cells for the electrochemical mass spectrometry (EC-MS) technique. Although important for different areas of electrochemistry, this technique is a powerful tool mainly for the study of the electrochemical conversion of CO2 due to the possibility of on-line monitoring the mass fragments of the reaction products. More information can be found in the Review by A. C. Queiroz et al.

Electrocatalyst Screening on a Massive Array of Closed Bipolar Microelectrodes

Herein, we report the use of a massive array of bipolar ultramicroelectrodes (UMEs) in conjunction with electrochemiluminescence (ECL) imaging as a novel electroanalytical platform for rapid screening of electrocatalysts. Following our recent work on carbon bipolar UME arrays, we have developed asymmetric carbon-gold bipolar UME arrays where each carbon UME is coated by a thin gold film on one side. To generate large quantities of compositionally varied electrocatalyst samples, a radial gradient of catalytic metal is electrodeposited on the surface of these UME arrays by delivering a plume of metal salt solution to the array surface with a micropipette while the entire array is biased at a reducing potential. We then utilize these bipolar UME arrays to investigate the impact of varied Ni(OH)2 coverage on the catalytic activity for hydrogen evolution reaction (HER) of an Au surface in alkaline solution, with the catalytic performance of the modified Au being confirmed to exhibit a peak-shaped dependence on increasing Ni(OH)2 coverage. Our future work will expand this unique platform to enable the screening of various metal alloys by incorporating additional micropipettes for delivering multiple metal salts to the array during the gradient electrodeposition process.

Optimizing the nickel boride layer thickness in a spectroelectrochemical ATR-FTIR thin-film flow cell applied in glycerol oxidation

The influence of the drop-casted nickel boride catalyst loading on glassy carbon electrodes was investigated in a spectroelectrochemical ATR-FTIR thin-film flow cell applied in alkaline glycerol electrooxidation. The continuously operated radial flow cell consisted of a borehole electrode positioned 50 µm above an internal reflection element enabling operando FTIR spectroscopy. It is identified as a suitable tool for facile and reproducible screening of electrocatalysts under well-defined conditions, additionally providing access to the selectivities in complex reaction networks such as glycerol oxidation. The fast product identification by ATR-IR spectroscopy was validated by the more time-consuming quantitative HPLC analysis of the pumped electrolyte. High degrees of glycerol conversion were achieved under the applied laminar flow conditions using 0.1 M glycerol and 1 M KOH in water and a flow rate of 5 µL min−1. Conversion and selectivity were found to depend on the catalyst loading, which determined the catalyst layer thickness and roughness. The highest loading of 210 µg cm−2 resulted in 73% conversion and a higher formate selectivity of almost 80%, which is ascribed to longer residence times in rougher films favoring readsorption and C–C bond scission. The lowest loading of 13 µg cm−2 was sufficient to reach 63% conversion, a lower formate selectivity of 60%, and, correspondingly, higher selectivities of C2 species such as glycolate amounting to 8%. Thus, only low catalyst loadings resulting in very thin films in the few μm thickness range are suitable for reliable catalyst screening.