Recent Development of Anion Exchange Membrane Fuel Cells and Performance Optimization Strategies: A Review.

Anion exchange membrane fuel cells (AEMFCs) are the most promising low-temperature fuel cells and have received extensive attention. Compared to PEMFCs, the cost per unit of power can be significantly reduced for AEMFCs because, in theory, they allow the usage of non-precious metal catalysts and low-cost cell components. Owing to the development of advanced materials and performance improvement strategies, AEMFCs have achieved new records in both initial performance and durability. However, the high performance currently achieved is contingent on certain conditions, e. g., high Pt loading, large gas flowrates, and operation in pure O2 , which are far from practical applications. Therefore, the transition to commercially relevant performance and durability is the next goal of AEMFCs. This paper reviews the performance data of H2 -fueled AEMFCs since 2010 and summarizes possible performance optimization schemes, which can provide useful insights for developing next-generation AEMFCs.

Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset.

Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery. While several machine learning models have sought to address the task of predicting reaction products, their extension to predicting reaction mechanisms has been impeded by the lack of a corresponding mechanistic dataset. In this study, we construct such a dataset by imputing intermediates between experimentally reported reactants and products using expert reaction templates and train several machine learning models on the resulting dataset of 5,184,184 elementary steps. We explore the performance and capabilities of these models, focusing on their ability to predict reaction pathways and recapitulate the roles of catalysts and reagents. Additionally, we demonstrate the potential of mechanistic models in predicting impurities, often overlooked by conventional models. We conclude by evaluating the generalizability of mechanistic models to new reaction types, revealing challenges related to dataset diversity, consecutive predictions, and violations of atom conservation.

RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing.

Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature. The reaction diagrams can be arbitrarily complex; thus, robustly parsing them into structured data is an open challenge. In this paper, we present RxnScribe, a machine learning model for parsing reaction diagrams of varying styles. We formulate this structured prediction task with a sequence generation approach, which condenses the traditional pipeline into an end-to-end model. We train RxnScribe on a dataset of 1378 diagrams and evaluate it with cross validation, achieving an 80.0% soft match F1 score, with significant improvements over previous models. Our code and data are publicly available at https://github.com/thomas0809/RxnScribe.

MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation.

Molecular structure recognition is the task of translating a molecular image into its graph structure. Significant variation in drawing styles and conventions exhibited in chemical literature poses a significant challenge for automating this task. In this paper, we propose MolScribe, a novel image-to-graph generation model that explicitly predicts atoms and bonds, along with their geometric layouts, to construct the molecular structure. Our model flexibly incorporates symbolic chemistry constraints to recognize chirality and expand abbreviated structures. We further develop data augmentation strategies to enhance the model robustness against domain shifts. In experiments on both synthetic and realistic molecular images, MolScribe significantly outperforms previous models, achieving 76-93% accuracy on public benchmarks. Chemists can also easily verify MolScribe's prediction, informed by its confidence estimation and atom-level alignment with the input image. MolScribe is publicly available through Python and web interfaces: https://github.com/thomas0809/MolScribe.

Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction.

Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not efficient for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boost empirical performance. Here, we describe a novel Graph2SMILES model that combines the power of Transformer models for text generation with the permutation invariance of molecular graph encoders that mitigates the need for input data augmentation. In our encoder, a directed message passing neural network (D-MPNN) captures local chemical environments, and the global attention encoder allows for long-range and intermolecular interactions, enhanced by graph-aware positional embedding. As an end-to-end architecture, Graph2SMILES can be used as a drop-in replacement for the Transformer in any task involving molecule(s)-to-molecule(s) transformations, which we empirically demonstrate leads to improved performance on existing benchmarks for both retrosynthesis and reaction outcome prediction.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Rational Design of Trimetallic Sulfide Electrodes for Alkaline Water Electrolysis with Ampere-Level Current Density.

Electrochemical water splitting is considered an environmentally friendly approach to hydrogen generation. However, it is difficult to achieve high current density and stability. Herein, we design an amorphous/crystalline heterostructure electrode based on trimetallic sulfide over nickel mesh substrate (NiFeMoS/NM), which only needs low overpotentials of 352 mV, 249 mV, and 360 mV to achieve an anodic oxygen evolution reaction (OER) current density of 1 A cm-2 in 1 M KOH, strong alkaline electrolyte (7.6 M KOH), and alkaline-simulated seawater, respectively. More importantly, it also shows superior stability with negligible decay after continuous work for 120 h at 1 A cm-2 in the strong alkaline electrolyte. The excellent OER performance of the as-obtained electrode can be attributed to the strong electronic interactions between different metal atoms, abundant amorphous/crystalline hetero-interfaces, and 3D porous nickel mesh structure. Finally, we coupled NiFeMoS/NM as both the anode and cathode in the anion exchange membrane electrolyzer, which can achieve low cell voltage and high stability at ampere-level current density, demonstrating the great potential of practicability.

Improving the performance of models for one-step retrosynthesis through re-ranking.

Retrosynthesis is at the core of organic chemistry. Recently, the rapid growth of artificial intelligence (AI) has spurred a variety of novel machine learning approaches for data-driven synthesis planning. These methods learn complex patterns from reaction databases in order to predict, for a given product, sets of reactants that can be used to synthesise that product. However, their performance as measured by the top-N accuracy in matching published reaction precedents still leaves room for improvement. This work aims to enhance these models by learning to re-rank their reactant predictions. Specifically, we design and train an energy-based model to re-rank, for each product, the published reaction as the top suggestion and the remaining reactant predictions as lower-ranked. We show that re-ranking can improve one-step models significantly using the standard USPTO-50k benchmark dataset, such as RetroSim, a similarity-based method, from 35.7 to 51.8% top-1 accuracy and NeuralSym, a deep learning method, from 45.7 to 51.3%, and also that re-ranking the union of two models' suggestions can lead to better performance than either alone. However, the state-of-the-art top-1 accuracy is not improved by this method.

A Low-Temperature Carbon Encapsulation Strategy for Stable and Poisoning-Tolerant Electrocatalysts.

Carbon encapsulation is an effective strategy for enhancing the durability of Pt-based electrocatalysts for the oxygen reduction reaction (ORR). However, high-temperature treatment is not only energy-intensive but also unavoidably leads to possible aggregation. Herein, a low-temperature polymeric carbon encapsulation strategy (≈150 °C) is reported to encase Pt nanoparticles in thin and amorphous carbonaceous layers. Benefiting from the physical confinement effect and enhanced antioxidant property induced by the surface carbon species, significantly improved stabilities can be achieved for polymeric carbon species encapsulated Pt nanoparticles (Pt@C/C). Particularly, a better antipoisoning capability toward CO, SOx , and POx is observed in the case of Pt@C/C. To minimize the thickness of the catalyst layer and reduce the mass transfer resistance, the high mass loading Pt@C/C (40 wt%) is prepared and applied to high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs). At 160 °C, a peak power density of 662 mW cm-2 is achieved with 40% Pt@C/C cathode in H2 -O2 HT-PEMFCs, which is superior to that with 40% Pt/C cathode. The facile strategy provides guidance for the synthesis of highly durable carbon encapsulated noble metal electrocatalysts toward ORR.

Iron-Doped Nickel Phosphide Nanosheet Arrays: An Efficient Bifunctional Electrocatalyst for Water Splitting.

Exploring efficient and earth-abundant electrocatalysts for water splitting is crucial for various renewable energy technologies. In this work, iron (Fe)-doped nickel phosphide (Ni2P) nanosheet arrays supported on nickel foam (Ni1.85Fe0.15P NSAs/NF) are fabricated through a facile hydrothermal method, followed by phosphorization. The electrochemical analysis demonstrates that the Ni1.85Fe0.15P NSAs/NF electrode possesses high electrocatalytic activity for water splitting. In 1.0 M KOH, the Ni1.85Fe0.15P NSAs/NF electrode only needs overpotentials of 106 mV at 10 mA cm-2 and 270 mV at 20 mA cm-2 to drive the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Furthermore, the assembled two-electrode (Ni1.85Fe0.15P NSAs/NF∥Ni1.85Fe0.15P NSAs/NF) alkaline water electrolyzer can produce a current density of 10 mA cm-2 at 1.61 V. Remarkably, it can maintain stable electrolysis over 20 h. Thus, this work undoubtedly offers a promising electrocatalyst for water splitting.

Particle-assisted semidirect breath figure method: a facile way to endow the honeycomb-structured petri dish with molecular recognition capability.

Recently, we have developed a semidirect breath figure (sDBF) method for direct fabrication of large-area and ordered honeycomb structures on commercial polystyrene (PS) Petri dishes without the use of an external polymer solution. In this work, we showed that both the pore size and the pore uniformity of the breath figure patterns were controllable by solvent amount. The cross-sectional image shows that only one layer of pores was formed on the BF figure patterns. By combing the sDBF method and Pickering emulsion and using the modular building blocks, we endowed the honeycomb-structured Petri dish with molecular recognition capability via the decoration of molecularly imprinted polymer (MIP) nanoparticles into the honeycomb pores. The radioligand binding experiments show that the MIP nanoparticles on the resultant honeycomb structures maintained high molecular binding selectivity. The reusability study indicates that MIP-BF patterns had excellent mechanical stability during the radioligand binding process. We believe that the modular approach demonstrated in this work will open up further opportunities for honeycomb structure-based chemical sensors for drug analysis, substrates for catalysts, and scaffold for cell growth.

Decorating titanate nanotubes with protonated 1,2,4-triazole moieties for anhydrous proton conduction.

Anhydrous proton conducting materials based on surface attachment of protonated 1,2,4-triazole moieties on titanate nanotubes are prepared through self-assembly technique. (1)H MAS NMR spectra have revealed that the triazole moieties located at the outer surface of nanotubes. The distance between two ionic groups at the surface is observed to be shorter than 1nm, which may facilitate the formation of continuous proton conducting domains, leading to easy proton transport through segmental motion and structural re-organization in the absence of water. The maximum proton conductivity of the synthesized materials reaches about 2.4×10(-3)Scm(-1) at 160°C under anhydrous conditions.

Proton conduction of polyAMPS brushes on titanate nanotubes.

Proton conducting materials having reasonable proton conductivity at low humidification conditions are critical for decrease in system complexity and improvement of power density for polymer electrolyte membrane fuel cells. This study shows that polyelectrolyte brushes on titanate nanotubes formed through surface-initiated free radical polymerization exhibit less humidity-dependent proton conduction because of the high grafting density of polymer electrolyte chains and well-distribution of ionic groups. The results described in this study provide an idea for design of new proton conductors with effective ion transport served at relatively low humidification levels.

Molecularly imprinted photocatalyst with a structural analogue of template and its application.

To realize selective mineralization of low-level chlorophenols (CPs) in the presence of high-level ordinary pollutants, molecularly imprinted polymers (MIPs) coated photocatalyst was prepared using substrate analog as template. The pseudo-template imprinted photocatalysts showed rapid decomposition ability toward a group of CPs. Based on the complete dechlorination and spectrophotometry, a new method was proposed to detect the total organochlorine on CPs in water samples. The method showed good linearity when the concentrations of the total organochlorine on CPs is in the range of 12.0-200.0µmolL(-1). The detection limit is 1µmolL(-1) for this method. When this method was applied to measure the total organochlorine of the CPs in both tap water and river water samples, an average recovery ranged from 96.3% to 105.1% was obtained with RSD values less than 5%. In this green and simple method, the common inorganic ions in water showed no interference for the detection. The determination of the total organochlorine on the CPs might be used for estimation of the toxicity and the persistence of the water samples.

Anhydrous proton conducting materials based on sulfonated dimethylphenethylchlorosilane grafted mesoporous silica/ionic liquid composite.

Efficient membrane proton conductivity at elevated temperatures (>100 °C) and reduced humidification conditions is a critical issue hindering fuel cell commercialization. Herein, proton conducting materials consisting of high surface area acid catalyzed mesoporous silica functionalized with sulfonated dimethylphenethylchlorosilane was investigated under anhydrous conditions. The organic moiety covalently bonded to the silica substrate via active hydroxyl groups on the silica pore surface. The structure and dynamic phases of the attached organic molecule were characterized and qualitatively determined by XRD, TEM, FT-IR, and solid state NMR. The amount of grafted organic molecules was estimated to be 2.45 µmol m(-2) by carbon elemental analysis. The so-formed composite materials showed adequate thermal stability up to 300 °C as determined by TGA. Under anhydrous conditions, ionic conductivity of the composite material upon ionic liquid impregnation reaches a peak value of 1.14 × 10(-2) S cm(-1) at 160 °C associated with the activation energy of 9.24 kJ mol(-1) for proton transport.