In Situ Surface Reconstruction of a Ni-based Perovskite Hydroxide Catalyst for an Efficient Oxygen Evolution Reaction

One core reaction involved in many electrochemical energy conversion systems is the oxygen evolution reaction (OER), which usually dominates the overall polarization loss due to its sluggish kinetics. Activating O2...

NeuralTPL: a deep learning approach for efficient reaction space exploration

Computer-aided synthesis planning (CASP) has been helping chemists to synthesize novel molecules at an accelerated pace. The recent integration of deep learning with CASP has opened up new avenues for digitizing and exploring the vastly unknown chemical space, and has led to high expectations for fully automated synthesis plannings using machine-discovered novel reactions in the "future". Despite many progresses in the past few years, most deep-learning methods only focus on improving few aspects of CASP (e.g., top-k accuracy). In this work, we target specifically the efficiency of reaction space exploration and its impact on CASP. We propose NeuralTPL, a template-oriented generative approach, that performs impressively across a range of evaluation metrics including chemical validity, diversity, and novelty for various tasks in CASP. In addition, our Transformer-based model bears the potential to learn the core reaction transformation as it can efficiently explore the reaction space. We then perform several experiments and conduct a thorough analysis regarding the three metrics and demonstrate its chemical value for improving the existing deep-learning-driven CASP algorithms.

NeuralTPL: a deep learning approach for efficient reaction space exploration

Computer-aided synthesis planning (CASP) has been helping chemists to synthesize novel molecules at an accelerated pace. The recent integration of deep learning with CASP has opened up new avenues for digitizing and exploring the vastly unknown chemical space, and has led to high expectations for fully automated synthesis plannings using machine-discovered novel reactions in the "future". Despite many progresses in the past few years, most deep-learning methods only focus on improving few aspects of CASP (e.g., top-k accuracy). In this work, we target specifically the efficiency of reaction space exploration and its impact on CASP. We propose NeuralTPL, a template-oriented generative approach, that performs impressively across a range of evaluation metrics including chemical validity, diversity, and novelty for various tasks in CASP. In addition, our Transformer-based model bears the potential to learn the core reaction transformation as it can efficiently explore the reaction space. We then perform several experiments and conduct a thorough analysis regarding the three metrics and demonstrate its chemical value for improving the existing deep-learning-driven CASP algorithms.

Improving the organization and interactivity of metabolic pathfinding with precomputed pathways

Background The rapid growth of available knowledge on metabolic processes across thousands of species continues to expand the possibilities of producing chemicals by combining pathways found in different species. Several computational search algorithms have been developed for automating the identification of possible heterologous pathways; however, these searches may return thousands of pathway results. Although the large number of results are in part due to the large number of possible compounds and reactions, a subset of core reaction modules is repeatedly observed in pathway results across multiple searches, suggesting that some subpaths between common compounds were more consistently explored than others.To reduce the resources spent on searching the same metabolic space, a new meta-algorithm for metabolic pathfinding, Hub Pathway search with Atom Tracking (HPAT), was developed to take advantage of a precomputed network of subpath modules. To investigate the efficacy of this method, we created a table describing a network of common hub metabolites and how they are biochemically connected and only offloaded searches to and from this hub network onto an interactive webserver capable of visualizing the resulting pathways. Results A test set of nineteen known pathways taken from literature and metabolic databases were used to evaluate if HPAT was capable of identifying known pathways. HPAT found the exact pathway for eleven of the nineteen test cases using a diverse set of precomputed subpaths, whereas a comparable pathfinding search algorithm that does not use precomputed subpaths found only seven of the nineteen test cases. The capability of HPAT to find novel pathways was demonstrated by its ability to identify novel 3-hydroxypropanoate (3-HP) synthesis pathways. As for pathway visualization, the new interactive pathway filters enable a reduction of the number of displayed pathways from hundreds down to less than ten pathways in several test cases, illustrating their utility in reducing the amount of presented information while retaining pathways of interest. Conclusions This work presents the first step in incorporating a precomputed subpath network into metabolic pathfinding and demonstrates how this leads to a concise, interactive visualization of pathway results. The modular nature of metabolic pathways is exploited to facilitate efficient discovery of alternate pathways.

Continuous Preparation and Properties of Silica/Rubber Composite Using Serial Modular Mixing

In order to efficiently prepare high-performance silica/rubber composites for use in the tread of semi-steel radial tires, a serial modular continuous mixer was designed according to the principle of modular functionalization. The modular structure and serial process helped control the accuracy of the silanization reaction. Synchronous four-wing serrated rotors and reverse meshing reaction mixing twin-rotors utilized shear flow and elongation flow to improve the dispersion. In this paper, the mechanism of serial modular continuous mixing was analyzed, and the influence of the core reaction mixing zone (various mixing elements) on silica-filled compounds was investigated by cooling visualization experiments, including dispersion, and the silanization reaction degree. Meanwhile, a comparative experiment between serial mixing and two-stage mixing was conducted, which showed that the serial process comprehensively improved the dispersion, mechanical properties, and dynamic mechanical properties of silica/rubber vulcanizate.

Study on Kinetics of Straw Stalk Gasification in CO2 with Random Pore Model

The gasification of straw stalk in CO2 environment was studied by isothermal thermogravimetric analysis. The characteristics of rice straw and maize stalk gasification at different temperatures were examined under CO2 atmosphere. The relationship between reaction time and carbon conversion of two biomass chars was analyzed by the random pore model (RPM), and compared with the simulation of the shrinking core reaction model (SCRM). The results show that the random pore model is better to predict the experimental data at different temperatures. This means that the characteristics of pore structure for the influence of biomass chars gasification is well reflected by parameter ψ used in RPM. It indicates that the RPM can be applied to the comprehensive simulation of biomass chars gasification in CO2 environment.

An Improved Core Reaction Mechanism for C0-C4 Unsaturated Fuels and C0-C4 Fuel Blends

Simulation of the combustion of fuels used in transportation and energy applications requires accurate chemistry representation of the fuel. Surrogate fuels are typically used to represent liquid fuels, such as gasoline, diesel or jet fuel, where the surrogate contains a handful of components. For gaseous fuels, surrogates are effectively used as well, where methane may be used to represent natural gas, for example. An accurate chemistry model of a surrogate fuel means a detailed reaction mechanism that contains the kinetics of all the molecular components of the fuel model. Since large hydrocarbons break down to smaller molecules during combustion, the core chemistry of C0 to C4 carbon number is critical to all such fuel models, whether gaseous or liquid. The usual method of assessing how accurate the fuel chemistry is involves modeling of fundamental combustion experiments, where the experimental conditions are well enough defined and well enough represented by the reacting-flow model to isolate the kinetics in comparisons between predictions and data. In the work reported here, we have been focused on developing a more comprehensive and accurate core (C0−C4) mechanism. Recently, we revisited the core mechanism to improve predictions of the pure saturated components (J Eng. Gas Turbines Power (2012) 134; doi:10.1115/1.4004388). In the current work, we focused on combustion of unsaturated C0−C4 fuel components and on the blends of C0−C4 fuels, including saturated components. The aim has been to improve predictions for the widest range of fundamental experiments as possible, while maintaining the accuracy achieved by the existing mechanism and the previous study of saturated components. In the validation, we considered experimental measurements of ignition delay, flame speed and extinction strain rate, as well as species composition in stirred reactors, flames and flow reactors. These experiments cover a wide range of temperatures, fuel-air ratios, and pressures. As in the previous work for saturated compounds, we examined uncertainties in the core reaction mechanism; including thermochemical parameters derived from a wide variety of sources, including experimental measurements, ab initio calculations, estimation methods and systematic optimization studies. Using sensitivity analysis, reaction-path analysis, consideration of recent focused studies of individual reactions, and an enforcement of data consistency, we have identified key updates required for the core mechanism. These updates resulted in improvements to predictions of results, as validated through comparison with experiments, for all the fuels considered, while maintaining the accuracy previously reported for the saturated C0−C4 components. Rate constants that were modified to improve predictions for a small number of reactions remain within expected uncertainty bounds.