jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yikai Xu | Wafaa Aljuhani | Yingrui Zhang | Ziwei Ye | Chunchun Li | Steven E. J. Bell
Source: Chemical Society Reviews (review, 2024-11-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A practical approach to quantitative analytical surface-enhanced Raman spectroscopy Many of the features of SERS, such as its high sensitivity, molecular specificity and speed of analysis make it attractive as an analytical technique. However, SERS currently remains a specialist technique which has not yet entered the mainstream of analytical chemistry. Therefore, this review draws out the underlying principles for analytical SERS and provides practical tips and tricks for SERS quantitation. The aim is to show the readers how to rationally design their SERS experiments to improve quantitation performance. We begin by introducing the three core components in SERS analysis: (1) the enhancing substrate material, (2) the Raman instrument and (3) the processed data that is used to establish a calibration curve. This is followed by discussion of the analytical figures of merit relevant to SERS. In the following sections each of the three essential components in SERS quantitation and how they affect the quality of the analysis are described in more detail using examples from the literature. Finally, we highlight the current challenges in applying SERS to the analysis of complex real-life samples and briefly introduce the state-of-the-art developments on multifunctional substrates, digital SERS and AI-assisted data processing, which will help SERS rise to the challenge of moving out into routine real-world analysis. Nanotechnology ; Raman spectroscopy ; Sensitivity (control systems) ; Surface-enhanced Raman spectroscopy ; Materials science ; Computer science ; Analytical technique ; Chemistry ; Raman scattering ; Engineering ; Physics ; Optics ; Electronic engineering ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser-induced spectroscopy and plasma
Authors: Noah M. Lancaster | Pavel Sinitcyn | Patrick Forny | Trenton M. Peters-Clarke | Caroline Fecher | Andrew J. H. Smith | Evgenia Shishkova | Tabiwang N. Arrey | Anna Pashkova | Margaret Lea Robinson | Nicholas L. Arp | Jing Fan | J N Hansen | Andrea Galmozzi | Lia R. Serrano | Julie Rojas | Audrey P. Gasch | Michael S. Westphall | Hamish Stewart | Christian Hock | Eugen Damoc | David J. Pagliarini | Vlad Zabrouskov | Joshua J. Coon
Source: Nature Communications (article, 2024-08-15)
Field: Chemistry — Spectroscopy

Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology. Phosphorylation ; Orbitrap ; Protein phosphorylation ; Context (archaeology) ; Computational biology ; Mass spectrometry ; Biology ; Cell biology ; Chemistry ; Protein kinase A Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yuxin Shi | Yang Zhang | Zhibin Wang | Ting Yuan | Ting Meng | Yunchao Li | Xiaohong Li | Fanglong Yuan | Zhan’ao Tan | Louzhen Fan
Source: Nature Communications (article, 2024-04-08)
Field: Materials Science — Materials Chemistry

Onion-like multicolor thermally activated delayed fluorescent carbon quantum dots for efficient electroluminescent light-emitting diodes Carbon quantum dots are emerging as promising nanomaterials for next-generation displays. The elaborate structural design is crucial for achieving thermally activated delayed fluorescence, particularly for improving external quantum efficiency of electroluminescent light-emitting diodes. Here, we report the synthesis of onion-like multicolor thermally activated delayed fluorescence carbon quantum dots with quantum yields of 42.3-61.0%. Structural, spectroscopic characterization and computational studies reveal that onion-like structures assembled from monomer carbon quantum dots of different sizes account for the decreased singlet-triplet energy gap, thereby achieving efficient multicolor thermally activated delayed fluorescence. The devices exhibit maximum luminances of 3785-7550 cd m<sup>-2</sup> and maximum external quantum efficiency of 6.0-9.9%. Importantly, owing to the weak van der Waals interactions and adequate solution processability, flexible devices with a maximum luminance of 2554 cd m<sup>-2</sup> are realized. These findings facilitate the development of high-performance carbon quantum dots-based electroluminescent light-emitting diodes that are promising for practical applications. Electroluminescence ; Quantum dot ; Fluorescence ; Materials science ; Quantum efficiency ; Optoelectronics ; Light-emitting diode ; Diode ; Carbon fibers ; Nanotechnology ; Optics ; Physics Carbon and Quantum Dots Applications ; Organic Light-Emitting Diodes Research ; Nanocluster Synthesis and Applications
Authors: Zhijun Li | Xiaowen Lu | Cong Guo | Siqi Ji | Hongxue Liu | Chunmin Guo | Xue Lü | Chao Wang | Wensheng Yan | Bingyu Liu | Wei Wu | J. Hugh Horton | Shixuan Xin | Yu Wang
Source: Nature Communications (article, 2024-04-12)
Field: Chemistry — Organic Chemistry

Solvent-free selective hydrogenation of nitroaromatics to azoxy compounds over Co single atoms decorated on Nb2O5 nanomeshes The solvent-free selective hydrogenation of nitroaromatics to azoxy compounds is highly important, yet challenging. Herein, we report an efficient strategy to construct individually dispersed Co atoms decorated on niobium pentaoxide nanomeshes with unique geometric and electronic properties. The use of this supported Co single atom catalysts in the selective hydrogenation of nitrobenzene to azoxybenzene results in high catalytic activity and selectivity, with 99% selectivity and 99% conversion within 0.5 h. Remarkably, it delivers an exceptionally high turnover frequency of 40377 h<sup>-1</sup>, which is amongst similar state-of-the-art catalysts. In addition, it demonstrates remarkable recyclability, reaction scalability, and wide substrate scope. Density functional theory calculations reveal that the catalytic activity and selectivity are significantly promoted by the unique electronic properties and strong electronic metal-support interaction in Co<sub>1</sub>/Nb<sub>2</sub>O<sub>5</sub>. The absence of precious metals, toxic solvents, and reagents makes this catalyst more appealing for synthesizing azoxy compounds from nitroaromatics. Our findings suggest the great potential of this strategy to access single atom catalysts with boosted activity and selectivity, thus offering blueprints for the design of nanomaterials for organocatalysis. Catalysis ; Selectivity ; Azoxy ; Combinatorial chemistry ; Chemistry ; Substrate (aquarium) ; Density functional theory ; Computational chemistry ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Hanwen Zhang | Veronika Jurásková | Fernanda Duarte
Source: Nature Communications (article, 2024-07-20)
Field: Materials Science — Materials Chemistry

Modelling chemical processes in explicit solvents with machine learning potentials Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner. Chemical space ; Chemical reaction ; Computer science ; Chemical process ; Biochemical engineering ; Stability (learning theory) ; Reaction mechanism ; Biological system ; Mechanism (biology) ; Artificial intelligence ; Machine learning ; Chemistry ; Catalysis ; Physics ; Organic chemistry ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Zhiyuan Han | An Chen | Zejian Li | Mengtian Zhang | Zhilong Wang | Lixue Yang | Runhua Gao | Yeyang Jia | Guanjun Ji | Zhoujie Lao | Xiao Xiao | Kehao Tao | Jing Gao | Wei Lv | Tianshuai Wang | Jinjin Li | Guangmin Zhou
Source: Nature Communications (article, 2024-09-29)
Field: Materials Science — Materials Chemistry

Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of transition metal-based electrocatalytic materials in the sulfur-based positive electrode. However, the atomic-level interactions among multiple electrocatalytic sites are not fully understood. Here, to improve the understanding of electrocatalytic sites, we propose a multi-view machine-learned framework to evaluate electrocatalyst features using limited datasets and intrinsic factors, such as corrected d orbital properties. Via physicochemical characterizations and theoretical calculations, we demonstrate that orbital coupling among sites induces shifts in band centers and alterations in the spin state, thus influencing interactions with polysulfides and resulting in diverse Li-S bond breaking and lithium migration barriers. Using a carbon-coated Fe/Co electrocatalyst (synthesized using recycled Li-ion battery electrodes as raw materials) at the positive electrode of a Li | |S pouch cell with high sulfur loading and lean electrolyte conditions, we report an initial specific energy of 436 Wh kg<sup>-1</sup> (whole mass of the cell) at 67 mA and 25 °C. Lithium (medication) ; Sulfur ; Lithium–sulfur battery ; Materials science ; Nanotechnology ; Computer science ; Electrochemistry ; Chemistry ; Electrode ; Metallurgy ; Biology Machine Learning in Materials Science ; Fuel Cells and Related Materials ; X-ray Diffraction in Crystallography
Authors: Chunyi Zhang | Marcos F. Calegari Andrade | Zachary K. Goldsmith | Abhinav S. Raman | Yifan Li | Pablo M. Piaggi | Xifan Wu | Roberto Car | Annabella Selloni
Source: Nature Communications (article, 2024-11-26)
Field: Chemistry — Electrochemistry

Molecular-scale insights into the electrical double layer at oxide-electrolyte interfaces The electrical double layer (EDL) at metal oxide-electrolyte interfaces critically affects fundamental processes in water splitting, batteries, and corrosion. However, limitations in the microscopic-level understanding of the EDL have been a major bottleneck in controlling these interfacial processes. Herein, we use ab initio-based machine learning potential simulations incorporating long-range electrostatics to unravel the molecular-scale picture of the EDL at the prototypical anatase TiO<sub>2</sub>-electrolyte interface under various pH conditions. Our large-scale simulations, capable of capturing interfacial water dissociation/recombination reactions and electrolytic proton transport, provide unprecedented insights into the detailed structure of the EDL. Moreover, the larger capacitance of the EDL under basic relative to acidic conditions, originating from the higher affinity of the cations for the oxide surface, is found to give rise to distinct charging mechanisms on negative and positive surfaces. Our results are validated by the agreement between the computed EDL capacitance and experimental data. Electrolyte ; Oxide ; Chemical physics ; Capacitance ; Materials science ; Ab initio ; Electrostatics ; Dissociation (chemistry) ; Nanotechnology ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physical chemistry ; Electrode Electrochemical Analysis and Applications ; Electronic and Structural Properties of Oxides ; Electrocatalysts for Energy Conversion
Authors: Linus C. Erhard | Jochen Rohrer | Karsten Albe | Volker L. Deringer
Source: Nature Communications (article, 2024-03-02)
Field: Materials Science — Materials Chemistry

Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning. Silicon ; Context (archaeology) ; Nanoscopic scale ; Nanotechnology ; Amorphous silica ; Materials science ; Workflow ; Nanometre ; Atomic units ; Silicon dioxide ; Semiconductor ; Amorphous solid ; Scale (ratio) ; Computer science ; Chemistry ; Chemical engineering ; Physics ; Optoelectronics ; Crystallography ; Engineering Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research
Authors: Yinuo Yang | Shuhao Zhang | Kavindri Ranasinghe | Olexandr Isayev | Adrián E. Roitberg
Source: Annual Review of Physical Chemistry (article, 2024-06-28)
Field: Materials Science — Materials Chemistry

Machine Learning of Reactive Potentials In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning. Computer science ; Machine learning ; Artificial neural network ; Kernel (algebra) ; Artificial intelligence ; Focus (optics) ; Physics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xin Wang | Zongrui Wang | Xiang Wang | Fangyuan Kang | Qianfeng Gu | Qichun Zhang
Source: Angewandte Chemie International Edition (review, 2024-09-21)
Field: Materials Science — Materials Chemistry

Recent Advances of Organic Cocrystals in Emerging Cutting‐Edge Properties and Applications Organic cocrystals, representing one type of new functional materials, have gathered significant interest in various engineering areas. Owing to their diverse stacking modes, rich intermolecular interactions and abundant functional components, the physicochemical properties of organic cocrystals can be tailored to meet different requirements and exhibit novel characteristics. The past few years have witnessed the rapid development of organic cocrystals in both fundamental characteristics and various applications. Beyond the typical properties like ambipolarity, emission tuning ability, ferroelectricity, etc. that are previously well demonstrated, many novel, impressive and cutting-edge properties and applications of cocrystals are also emerged and advanced recently. Especially during the nearest five years, photothermal conversion, room-temperature phosphorescence, thermally activated delay fluorescence, circularly polarized luminescence, organic solid-state lasers, near-infrared sensing, photocatalysis, batteries, and stimuli responses have been reported. In this review, these new properties are carefully summarized. Besides, some neoteric architecture and methodologies, such as host-guest structures and machine learning-based screening, are introduced. Finally, the potential future developments and expectations for organic cocrystals are discussed for further investigations on multiple functions and applications. Nanotechnology ; Stacking ; Materials science ; Phosphorescence ; Luminescence ; Fluorescence ; Chemistry ; Optoelectronics ; Organic chemistry ; Physics Luminescence and Fluorescent Materials ; Crystallography and molecular interactions ; Organic and Molecular Conductors Research
Authors: Mohammad Ali Abdelkareem | Qaisar Abbas | Enas Taha Sayed | Nabila Shehata | Javad B. M. Parambath | Abdul Hai Alami | A.G. Olabi
Source: Energy (article, 2024-04-03)
Field: Chemistry — Inorganic Chemistry

Recent advances on metal-organic frameworks (MOFs) and their applications in energy conversion devices: Comprehensive review Nanotechnology ; Metal-organic framework ; Supercapacitor ; Stacking ; Materials science ; Electrochemical energy conversion ; Energy transformation ; Energy storage ; Nanomaterials ; Hydrogen production ; Redox ; Hydrogen storage ; Electrochemistry ; Catalysis ; Chemistry ; Electrode Metal-Organic Frameworks: Synthesis and Applications ; Supercapacitor Materials and Fabrication ; Advanced battery technologies research
Authors: Iftikhar Hussain | Waqas Ul Arifeen | Shahid Ali Khan | Sikandar Aftab | Muhammad Sufyan Javed | Sajjad Hussain | Muhammad Ahmad | Xi Chen | Jiyun Zhao | P. Rosaiah | Khaled Fahmi Fawy | Adnan Younis | Sumanta Sahoo | Kaili Zhang
Source: Nano-Micro Letters (review, 2024-06-14)
Field: Materials Science — Materials Chemistry

M4X3 MXenes: Application in Energy Storage Devices MXene has garnered widespread recognition in the scientific community due to its remarkable properties, including excellent thermal stability, high conductivity, good hydrophilicity and dispersibility, easy processability, tunable surface properties, and admirable flexibility. MXenes have been categorized into different families based on the number of M and X layers in M<sub>n+1</sub>X<sub>n</sub>, such as M<sub>2</sub>X, M<sub>3</sub>X<sub>2</sub>, M<sub>4</sub>X<sub>3</sub>, and, recently, M<sub>5</sub>X<sub>4</sub>. Among these families, M<sub>2</sub>X and M<sub>3</sub>X<sub>2</sub>, particularly Ti<sub>3</sub>C<sub>2</sub>, have been greatly explored while limited studies have been given to M<sub>5</sub>X<sub>4</sub> MXene synthesis. Meanwhile, studies on the M<sub>4</sub>X<sub>3</sub> MXene family have developed recently, hence, demanding a compilation of evaluated studies. Herein, this review provides a systematic overview of the latest advancements in M<sub>4</sub>X<sub>3</sub> MXenes, focusing on their properties and applications in energy storage devices. The objective of this review is to provide guidance to researchers on fostering M<sub>4</sub>X<sub>3</sub> MXene-based nanomaterials, not only for energy storage devices but also for broader applications. MXenes ; Flexibility (engineering) ; Materials science ; Nanotechnology ; Energy storage ; Thermal stability ; Chemical engineering ; Engineering ; Physics ; Mathematics MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications