Computational chemistry for water-splitting electrocatalysis

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(6), P. 2771 - 2807

Published: Jan. 1, 2024

This review presents the basics of electrochemical water electrolysis, discusses progress in computational methods, models, and descriptors, evaluates remaining challenges this field.

Language: Английский

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CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers

Computers & Chemical Engineering, Journal Year: 2023, Volume and Issue: 177, P. 108339 - 108339

Published: June 28, 2023

Language: Английский

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Promoting Electroreduction of CO₂ and NO₃⁻ to Urea via Tandem Catalysis of Zn Single Atoms and In₂O_3‐x

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 16, 2024

Abstract Urea electrosynthesis from co‐electrolysis of CO 2 and NO 3 − (UECN) offers an innovative route for converting waste /NO into valuable urea. Herein, Zn single atoms anchored on oxygen vacancy (OV)‐rich In O 3‐x (Zn 1 /In ) are developed as a highly active selective UECN catalyst, delivering the highest urea yield rate 41.6 mmol h −1 g urea‐Faradaic efficiency 55.8% at −0.7 V in flow cell, superior to most previously reported catalysts. situ spectroscopic measurements theoretical calculations unveil synergy In/Zn sites OVs promoting process via tandem catalysis mechanism, where ‐OV site activates form * NH while In‐OV CO. The formed spontaneously migrates nearby then couples with generate CONH which is ultimately converted

Language: Английский

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First-Principles-Based Kinetic Monte Carlo Model of Hydrogen Evolution Reaction under Realistic Conditions: Solvent, Hydrogen Coverage and Electric Field Effects

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(4), P. 2696 - 2708

Published: Feb. 7, 2024

The hydrogen evolution reaction (HER) plays an important role in electrocatalytic water splitting. Despite the progress on development of HER catalysts, dynamic under realistic electrochemical conditions considering electric field, solvent, and coverage effects is still unclear. In this study, a first-principles-based H surface potential-dependent kinetic Monte Carlo (KMC) model Pt (111)/Pt (100) presented. kinetics electronic structure analysis surfaces presence dihydrated proton (H5O2+) investigated using density functional theory (DFT). KMC was developed based DFT-calculated energetics. simulation results showed that consideration H5O2+ species essential for accurate description catalyst, which fits well with polarization data. Moreover, sensitivity shows (111) mainly affected by Tafel step. On Pt(100) surface, primarily governed Heyrovsky pathway. Surface demonstrates high working potential accelerated formation [Pt-2H] species, leading to increased accelerating process. predicted weakened binding strength at verified situ attenuated total reflection Fourier transformed infrared spectroscopy analysis. Overall, proposed DFT-KMC represents state-of-art catalytic reaction, providing insights into operation conditions.

Language: Английский

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Magneto‐Electrochemical Ammonia Synthesis: Boosting Nitrite Reduction Activity by the Optimized Magnetic Field Induced Spin Polarized System

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(42)

Published: Sept. 25, 2024

Abstract Using low and optimized magnetic field along with electric is a novel strategy to facilitate electrochemical nitrite reduction reaction (NO 2 RR). Herein, the assisted electrocatalytic ammonia synthesis employing spin‐thrusted β‐MnPc at 95 mT explored. The calculated rate of generation 16603.4 µg h −1 mg cat , which almost twice that nonpolarized manganese phthalocyanine (MnPc) catalyst. Additionally, Faradaic efficiency (FE) –0.9 V versus RHE found be 92.9%, significantly higher compared MnPc In presence external field, catalysts provide better electron transfer channel results in lower charge resistance hence performances. Density functional theory (DFT) result further verifies induced has potential barrier (0.51 eV) for protonation NO* than (1.08 eV), confirms enhanced ammonia.

Language: Английский

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Integrated learning‐based estimation and nonlinear predictive control of an ammonia synthesis reactor

AIChE Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Abstract This paper presents an advanced machine learning‐based framework designed for predictive modeling, state estimation, and feedback control of ammonia synthesis reactor dynamics. A high‐fidelity two‐dimensional multiphysics model is employed to generate a comprehensive dataset that captures variable dynamics under various operational conditions. Surrogate long short‐term memory neural networks are trained enable real‐time predictions model‐based control. Additionally, feedforward network developed estimate the outlet concentration hotspot temperature using spatially distributed readings, thereby addressing challenges associated with maximum measurements. The modeling estimation methods integrated into architecture regulate synthesis. Simulation results demonstrate learning surrogates accurately represent nonlinear process minimal discrepancy while reducing optimization costs compared model, ensuring adaptability effective guidance desired set points.

Language: Английский

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Expanding the frontiers of electrocatalysis: advanced theoretical methods for water splitting

Nano Convergence, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 24, 2025

Abstract Electrochemical water splitting, which encompasses the hydrogen evolution reaction (HER) and oxygen (OER), offers a promising route for sustainable production. The development of efficient cost-effective electrocatalysts is crucial advancing this technology, especially given reliance on expensive transition metals, such as Pt Ir, in traditional catalysts. This review highlights recent advances design optimization electrocatalysts, focusing density functional theory (DFT) key tool understanding improving catalytic performance HER OER. We begin by exploring DFT-based approaches evaluating activity under both acidic alkaline conditions. then shifts to material-oriented perspective, showcasing catalyst materials theoretical strategies employed enhance their performance. In addition, we discuss scaling relationships that exist between binding energies electronic structures through use charge-density analysis d -band theory. Advanced concepts, effects adsorbate coverage, solvation, applied potential behavior, are also discussed. finally focus integrating machine learning (ML) with DFT enable high-throughput screening accelerate discovery novel water-splitting comprehensive underscores pivotal role plays electrocatalyst its shaping future Graphical

Language: Английский

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Introducing Hybrid Modeling with Time-Series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization

Industrial & Engineering Chemistry Research, Journal Year: 2023, Volume and Issue: 62(49), P. 21278 - 21291

Published: Nov. 29, 2023

Given the hesitance surrounding direct implementation of black-box tools due to safety and operational concerns, fully data-driven deep-neural-network (DNN)-based digital twins are facing an hurdle. To address this, hybrid models combining physics-based, first-principles with machine learning have gained traction. These perceived as "best both worlds" solution. However, existing simplistic DNN fall short predicting long-term evolution process data. Recently, time-series transformers (TSTs), which utilize a multiheaded attention mechanism capture long short-term dynamics, demonstrated superior performance. Consequently, first-of-a-kind, TST-based modeling framework for batch crystallization has been developed, offering improved accuracy interpretability when compared conventional models. Particularly, two different configurations, series parallel, were constructed compared. They normalized-mean-square-error within range [10, 50] × 10–4 R2 value over 0.99.

Language: Английский

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Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane

Applied Catalysis B Environment and Energy, Journal Year: 2023, Volume and Issue: 343, P. 123454 - 123454

Published: Nov. 9, 2023

Conventional methods for developing heterogeneous catalysts are inefficient in time and cost, often relying on trial-and-error. The integration of machine-learning (ML) catalysis research using data can reduce computational costs provide valuable insights. However, the lack interpretability black-box models hinders their acceptance among researchers. We propose an interpretable ML framework that enables a comprehensive understanding complex relationships between variables. Our incorporates tools such as Shapley additive explanations partial dependence values effective preprocessing result analysis. This increases prediction accuracy model with improved R2 value 0.96, while simultaneously expanding catalyst component variety. Furthermore, case dry reforming methane, we tested validity recommendation through dedicated experimental tests. outstanding performance has potential to expedite rational design catalysts.

Language: Английский

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Boosting Selective Nitrogen Oxidation to Nitric Acid by Synergizing Cobalt Phthalocyanine on Carbon Nitride Surface

Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown

Published: June 14, 2024

Abstract The Ostwald process, which is producing HNO 3 for commercial use, involves the catalytic oxidation of NH and a series chemical reactions conducted under severe operating conditions. Due to their energy‐intensive nature, these activities play major role in greenhouse gas emissions global energy consumption. In response urgent requirements environmental sectors, there an increasingly critical need develop novel, highly efficient, environmentally sustainable methods. Herein, CoPc/C N 4 electrocatalyst, integrating CoPc nanotubes with C nanosheets, shown. electrocatalyst demonstrates yield rate 871.8 µmol h −1 g cat at 2.2 V, corresponding Faradaic efficiency (FE) 46.4% 2.1 notably surpasses that CoPc. Through combination experimental investigations density functional theory (DFT) calculations, this study shows anchored on effectively simplifies adsorption activation chemically inactive nitrogen molecules. improved activity composite system may be reason re‐distribution charges over CoPc, tuning valence orbital Co center due presence 2D layer . This mechanism significantly lowers barrier required breaking inert 2 , ultimately leading significant improvement efficiency.

Language: Английский

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