A Review on the Recent Advances in Battery Development and Energy Storage Technologies

Journal of Renewable Energy, Journal Year: 2024, Volume and Issue: 2024, P. 1 - 35

Published: May 8, 2024

Energy storage is a more sustainable choice to meet net-zero carbon foot print and decarbonization of the environment in pursuit an energy independent future, green transition, uptake. The journey reduced greenhouse gas emissions, increased grid stability reliability, improved access security are result innovation systems. Renewable sources fundamentally intermittent, which means they rely on availability natural resources like sun wind rather than continuously producing energy. Due its ability address inherent intermittency renewable sources, manage peak demand, enhance make it possible integrate small-scale systems into grid, essential for continued development decentralization generation. Accordingly, effective system has been prompted by demand unlimited supply energy, primarily through harnessing solar, chemical, mechanical Nonetheless, order achieve transition mitigate climate risks resulting from use fossil-based fuels, robust necessary. Herein, need better, devices such as batteries, supercapacitors, bio-batteries critically reviewed. their low maintenance needs, supercapacitors facilities, most notably Moreover, possess charging discharging cycles, high power density, requirements, extended lifespan, environmentally friendly. On other hand, combining aluminum with nonaqueous charge materials conductive polymers each material’s unique capabilities could be crucial batteries. In general, density key component battery development, scientists constantly developing new methods technologies existing batteries proficient safe. This will design that powerful lighter range applications. When there imbalance between (ESS) offer way increasing effectiveness electrical They also play central role enhancing reliability excellence networks can deployed off-grid localities.

Language: Английский

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The Enigma of Methanol Synthesis by Cu/ZnO/Al₂O₃-Based Catalysts

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(8), P. 4543 - 4678

Published: April 2, 2024

The activity and durability of the Cu/ZnO/Al

Language: Английский

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Delocalized, asynchronous, closed-loop discovery of organic laser emitters

Science, Journal Year: 2024, Volume and Issue: 384(6697)

Published: May 16, 2024

Contemporary materials discovery requires intricate sequences of synthesis, formulation, and characterization that often span multiple locations with specialized expertise or instrumentation. To accelerate these workflows, we present a cloud-based strategy enabled delocalized asynchronous design-make-test-analyze cycles. We showcased this approach through the exploration molecular gain for organic solid-state lasers as frontier application in optoelectronics. Distributed robotic synthesis in-line property characterization, orchestrated by artificial intelligence experiment planner, resulted 21 new state-of-the-art materials. Gram-scale ultimately allowed verification best-in-class stimulated emission thin-film device. Demonstrating integration five laboratories across globe, workflow provides blueprint delocalizing-and democratizing-scientific discovery.

Language: Английский

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Data-Driven Discovery of Intrinsic Direct-Gap 2D Materials as Potential Photocatalysts for Efficient Water Splitting

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(3), P. 1336 - 1350

Published: Jan. 11, 2024

Intrinsic direct-gap two-dimensional (2D) materials hold great promise as photocatalysts, advancing the application of photocatalytic water splitting for hydrogen production. However, time- and resource-efficient exploration identification such 2D from a vast compositional structural chemical space present significant challenges within realm science research. To this end, we perform data-driven study to find with intrinsic desirable properties overall splitting. By implementing three-staged large-scale screening, which incorporates machine-learned data V2DB, high-throughput density functional theory (DFT), hybrid-DFT calculations, identify 16 promising photocatalysts. Subsequently, conduct comprehensive assessment that are related solar performance, include electronic optical properties, solar-to-hydrogen conversion efficiencies, carrier mobilities. Therefore, not only presents photocatalysts but also introduces rigorous approach future discovery currently unexplored spaces.

Language: Английский

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A reactive neural network framework for water-loaded acidic zeolites

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 17, 2024

Language: Английский

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“Cation‐Recognition” Effect of 2D Nanochannels in Graphene Oxide Membranes Intercalated with Ionic Liquid for High Desalination Performance

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

Published: Jan. 2, 2025

Water and ion transport in nanochannels is crucial for membrane-based technology biological systems. 2D materials, especially graphene oxide (GO), the most frequently used as starting material, are ideal building blocks developing synthetic membranes. However, selective exclusion of small ions while maintaining a pressured filtration process remains challenge GO Herein, novel "cation-recognition" effect introduced within reduced (rGO) membranes modified by ionic liquids (IL) to enhance desalination performance. The resulting IL-intercalated rGO (IL-rGO) exhibit remarkable stability even under prolonged exposure acidic basic conditions, without damage or delamination maintain approximately ultrahigh water permeance (≈32.0 L m

Language: Английский

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Catalytic reactivity descriptors of metal‐nitrogen‐doped carbon catalysts for electrocatalysis

EcoEnergy, Journal Year: 2023, Volume and Issue: 1(1), P. 154 - 185

Published: Sept. 1, 2023

Abstract Metal‐nitrogen‐doped carbon material have sparked enormous attentions as they show excellent electrocatalytic performance and provide a prototype for mechanistic understandings of reactions. Researchers spare no effort to find catalytic reactivity “descriptor”, which is correlated with catalytical properties could be utilized guiding the rational design high‐performance catalysts. In recent years, benefited from development computational technology, theoretical calculation came into being powerful tool understand mechanisms an atomic level well accelerate process finding descriptor promoting effective present review, we latest research toward energetic electronic descriptors metal‐nitrogen‐doped (M‐N‐C) materials, shown understanding This review uses density functional theory most advanced machine learning method describe exploration four kinds reaction descriptors, namely oxygen reduction reaction, dioxide hydrogen evolution nitrogen reaction. The aim this inspire future high‐efficiency M‐N‐C catalysts by providing in‐depth insights activity these materials.

Language: Английский

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Paving the road towards automated homogeneous catalyst design

ChemPlusChem, Journal Year: 2024, Volume and Issue: 89(7)

Published: Jan. 26, 2024

In the past decade, computational tools have become integral to catalyst design. They continue offer significant support experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning garnered considerable attention their expansive capabilities. This Perspective provides an overview of diverse initiatives in realm design introduces our automated tailored high-throughput silico exploration chemical space. While valuable insights are gained through methods analysis space, degree automation modularity key. We argue that integration data-driven, modular workflows is key enhancing homogeneous on unprecedented scale, contributing advancement research.

Language: Английский

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Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(2), P. 025058 - 025058

Published: May 13, 2024

Abstract For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation simulation. Correspondingly, order to reduce cost carbon footprint, training efficiency key. We introduce minimal multilevel (M3L) which optimizes set sizes using a loss function at multiple levels of reference minimize combination prediction error with overall acquisition costs (as measured by computational wall-times). Numerical evidence has been obtained for calculated atomization energies electron affinities thousands organic molecules various theory including HF, MP2, DLPNO-CCSD(T), DFHFCABS, PNOMP2F12, PNOCCSD(T)F12, treating them basis sets TZ, cc-pVTZ, AVTZ-F12. Our M3L benchmarks reaching chemical accuracy distinct compound sub-spaces indicate substantial reductions factors ∼1.01, 1.1, 3.8, 13.8, 25.8 compared heuristic sub-optimal (M2L) QM7b, QM9 LCCSD ( T stretchy="false">) , Electrolyte Genome Project, AE CCSD EA respectively. Furthermore, we use M2L investigate performance 76 density functionals used within building following drawn from hierarchy Jacobs Ladder: LDA, GGA, mGGA, hybrid functionals. Within considered, mGGAs do provide any noticeable advantage over GGAs. Among considered three average top performing GGA Hybrid correspond respectively PW91, KT2, B97D, τ -HCTH, B3LYP ∗ (VWN5), TPSSH.

Language: Английский

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Probing out-of-distribution generalization in machine learning for materials

Communications Materials, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 11, 2025

Abstract Scientific machine learning (ML) aims to develop generalizable models, yet assessments of generalizability often rely on heuristics. Here, we demonstrate in the materials science setting that heuristic evaluations lead biased conclusions ML and benefits neural scaling, through out-of-distribution (OOD) tasks involving unseen chemistry or structural symmetries. Surprisingly, many good performance across including boosted trees. However, analysis representation space shows most test data reside within regions well-covered by training data, while poorly-performing involve outside domain. For these challenging tasks, increasing size time yields limited adverse effects, contrary traditional scaling trends. Our findings highlight OOD tests reflect interpolation, not true extrapolation, leading overestimations benefits. This emphasizes need for rigorously benchmarks.

Language: Английский

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