jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan Su | Ken‐ichi Otake | Jia‐Jia Zheng | Ping Wang | Qing Lin | Susumu Kitagawa | Cheng Gu
Source: Nature Communications (article, 2024-04-04)
Field: Chemistry — Inorganic Chemistry

Diffusion-rate sieving of propylene and propane mixtures in a cooperatively dynamic porous crystal Selective molecular recognition is an important alternative to the energy-intensive industrial separation process. Porous coordination polymers (PCPs) offer designing platforms for gas separation because they possess precise controllability over structures at the molecular level. However, PCPs-based gas separations are dominantly achieved using strong adsorptive sites for thermodynamic recognition or pore-aperture control for size sieving, which suffer from insufficient selectivity or sluggish kinetics. Developing PCPs that work at high temperatures and feature both high uptake capacity and selectivity is urgently required but remains challenging. Herein, we report diffusion-rate sieving of propylene/propane (C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub>) at 300 K by constructing a PCP material whose global and local dynamics cooperatively govern the adsorption process via the mechanisms of the gate opening for C<sub>3</sub>H<sub>6</sub> and the diffusion regulation for C<sub>3</sub>H<sub>8</sub>, respectively, yielding substantial differences in both uptake capacity and adsorption kinetics. Dynamic separation of an equimolar C<sub>3</sub>H<sub>6</sub>/C<sub>3</sub>H<sub>8</sub> mixture reveals outstanding sieving performance with a C<sub>3</sub>H<sub>6</sub> purity of 99.7% and a separation factor of 318. Propane ; Diffusion ; Adsorption ; Selectivity ; Chemical engineering ; Kinetics ; Materials science ; Porosity ; Molecular diffusion ; Chemistry ; Nanotechnology ; Physical chemistry ; Thermodynamics ; Organic chemistry ; Catalysis ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Dinghui Chen | Zizheng Tong | Qiushi Rao | Xingchen Liu | Hong Meng | Wei Huang
Source: Nature Communications (article, 2024-09-30)
Field: Materials Science — Polymers and Plastics

High-Performance Black Copolymers Enabling Full Spectrum Control in Electrochromic Devices Black-to-transparent electrochromism is hailed as the holy grail of organic optoelectronics. Despite its potential, designing black electrochromic materials that fully absorb visible light remains a significant challenge. Electroactive materials that simultaneously possess excellent cyclic stability, fast switching times, and high coloration efficiency are rare. In this study, we successfully designed copolymers that fully absorb the entire visible spectrum by judiciously selecting four types of monomers. We incorporated two types of polar side chains to synergistically enhance the ionic conductivity of the copolymers, thus improving the performance of electrochromic devices. Among these electrochromic devices, the P2-a device exhibits cycling stability exceeding 10<sup>5</sup> cycles, and the P2-c device demonstrates a coloring/ bleaching time of 0.82 s/0.86 s and achieves a coloration efficiency of 1078 cm²/C. This study proposes a strategy for designing and synthesizing high-performance black electrochromic copolymers. Electrochromism ; Copolymer ; Materials science ; Optoelectronics ; Electrochromic devices ; Control (management) ; Nanotechnology ; Computer science ; Chemistry ; Polymer ; Electrode ; Composite material Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Perovskite Materials and Applications
Authors: Yifan Si | Shuo Shi | Jinlian Hu
Source: Matter (article, 2024-02-01)
Field: Materials Science — Biomaterials

Electrospinning and electrospraying synergism: Twins-tech collaboration across dimensions Electrospinning ; Nanotechnology ; Materials science ; Composite material Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Supercapacitor Materials and Fabrication
Authors: Jia Ren | Mingxue Gao | Zhenjiang Liu | Yujie Yang | Ruimin Wu | Jiayi Liang | Jie Yang | Manman Fang | Zhen Li
Source: Advanced Functional Materials (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

The Impact of Molecular Packing on Organic Room Temperature Phosphorescence and Corresponding Stimulus Response Effect Abstract Organic luminescent materials exhibiting persistent room temperature phosphorescence (RTP) have garnered considerable interest due to their versatile applications. However, the mechanism is still unclear, especially for the molecular structure‐molecular arrangement‐property relationship. Herein, six derivatives of phenothiazine 5,5‐dioxide are synthesized, revealing their distinct RTP properties and the associated underlying mechanisms. Careful analyses of their single crystal structures, combined with initial theoretical calculations, indicate that the persistent RTP effect is facilitated by effective intermolecular π–π interactions in the solid state led by folded conformation. Specifically, PTZO‐CF 3 ‐3F exhibits distinctive photo‐activated phosphorescence both in crystal and grinding state in response to the changes of molecular arrangement. Besides, a porous structure is found to be present in PTZO‐CF 3 ‐2F crystal, offering it great sensitivity to oxygen for both RTP intensity and lifetime. Accordingly, the visual oxygen warning device is fabricated successfully. Furthermore, PTZO‐CF 3 ‐3F with photo‐activated RTP effect in a grinding state is successfully utilized in the applications of anti‐counterfeiting and optical logic gates, expanding the scope of stimulus‐responsive phosphorescence materials for broader applications. Phosphorescence ; Materials science ; Intermolecular force ; Luminescence ; Photochemistry ; Nanotechnology ; Crystal (programming language) ; Chemical physics ; Molecule ; Optoelectronics ; Fluorescence ; Organic chemistry ; Chemistry ; Optics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: Yilin Jiang | Bin Su | Jincheng Yu | Zhanran Han | Haihua Hu | Hua‐Lu Zhuang | Hezhang Li | Jinfeng Dong | Jingwei Li | Chao Wang | Zhen-Hua Ge | Jing Feng | Fu‐Hua Sun | Jing‐Feng Li
Source: Nature Communications (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Exceptional figure of merit achieved in boron-dispersed GeTe-based thermoelectric composites GeTe is a promising p-type material with increasingly enhanced thermoelectric properties reported in recent years, demonstrating its superiority for mid-temperature applications. In this work, the thermoelectric performance of GeTe is improved by a facile composite approach. We find that incorporating a small amount of boron particles into the Bi-doped GeTe leads to significant enhancement in power factor and simultaneous reduction in thermal conductivity, through which the synergistic modulation of electrical and thermal transport properties is realized. The thermal mismatch between the boron particles and the matrix induces high-density dislocations that effectively scatter the mid-frequency phonons, accounting for a minimum lattice thermal conductivity of 0.43 Wm<sup>-1</sup>K<sup>-1</sup> at 613 K. Furthermore, the presence of boron/GeTe interfaces modifies the interfacial potential barriers, resulting in increased Seebeck coefficient and hence enhanced power factor (25.4 μWcm<sup>-1</sup>K<sup>-2</sup> at 300 K). Consequently, we obtain a maximum figure of merit Z<sub>max</sub> of 4.0 × 10<sup>-3 </sup>K<sup>-1</sup> at 613 K in the GeTe-based composites, which is the record-high value in GeTe-based thermoelectric materials and also superior to most of thermoelectric systems for mid-temperature applications. This work provides an effective way to further enhance the performance of GeTe-based thermoelectrics. Thermoelectric effect ; Materials science ; Figure of merit ; Thermal conductivity ; Thermoelectric materials ; Seebeck coefficient ; Boron ; Composite material ; Doping ; Phonon ; Condensed matter physics ; Optoelectronics ; Thermodynamics ; Chemistry Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Thermal Radiation and Cooling Technologies
Authors: Shiqi Fang | Ning Xu | Lin Zhou | Tianqi Wei | Yuhan Yang | Yongmin Liu | Jia Zhu
Source: Science Advances (article, 2024-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-assembled skin-like metamaterials for dual-band camouflage Skin-like soft optical metamaterials with broadband modulation have been long pursued for practical applications, such as cloaking and camouflage. Here, we propose a skin-like metamaterial for dual-band camouflage based on unique Au nanoparticles assembled hollow pillars (NPAHP), which are implemented by the bottom-up template-assisted self-assembly processes. This dual-band camouflage realizes simultaneously high visible absorptivity (~0.947) and low infrared emissivity (~0.074/0.045 for mid-/long-wavelength infrared bands), ideal for visible and infrared dual-band camouflage at night or in outer space. In addition, this self-assembled metamaterial, with a micrometer thickness and periodic through-holes, demonstrates superior skin-like attachability and permeability, allowing close attachment to a wide range of surfaces including the human body. Last but not least, benefiting from the extremely low infrared emissivity, the skin-like metamaterial exhibits excellent high-temperature camouflage performance, with radiation temperature reduction from 678 to 353 kelvin. This work provides a new paradigm for skin-like metamaterials with flexible multiband modulation for multiple application scenarios. Camouflage ; Metamaterial ; Emissivity ; Infrared ; Materials science ; Cloaking ; Optics ; Optoelectronics ; Multi-band device ; Far infrared ; Terahertz radiation ; Nanotechnology ; Physics ; Antenna (radio) ; Telecommunications ; Computer science Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Polydiacetylene-based materials and applications
Authors: Hongyuan Sheng | Jingwen Sun | Oliver Rodríguez | Benjamin B. Hoar | Weitong Zhang | Danlei Xiang | Tianhua Tang | Avijit Hazra | Daniel S. Min | Abigail G. Doyle | Matthew S. Sigman | Cyrille Costentin | Quanquan Gu | Joaquín Rodríguez‐López | Chong Liu
Source: Nature Communications (article, 2024-03-30)
Field: Chemistry — Electrochemistry

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention. Workflow ; Computer science ; Mechanism (biology) ; Electrochemistry ; Automation ; Biochemical engineering ; Electron transfer ; Electrophile ; Combinatorial chemistry ; Nanotechnology ; Biological system ; Chemistry ; Materials science ; Catalysis ; Electrode ; Physics ; Database Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts ; Electrochemical sensors and biosensors
Authors: Mohammad H. Samha | Lucas J. Karas | David B. Vogt | Emmanuel C. Odogwu | Jennifer M. Elward | Jennifer M. Crawford | Janelle E. Steves | Matthew S. Sigman
Source: Science Advances (article, 2024-01-17)
Field: Chemistry — Organic Chemistry

Predicting success in Cu-catalyzed C–N coupling reactions using data science Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment. Workflow ; Software deployment ; Computer science ; Coupling (piping) ; Ligand (biochemistry) ; Throughput ; Process (computing) ; Biochemical engineering ; Data science ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Software engineering ; Database ; Materials science ; Engineering ; Programming language Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis ; Catalytic C–H Functionalization Methods
Authors: Longze Li | Jiang Chang | Aleksandar Vakanski | Yachun Wang | Tiankai Yao | Min Xian
Source: Scientific Reports (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs. Uncertainty quantification ; Computer science ; Machine learning ; Inference ; Probabilistic logic ; Artificial intelligence ; Gaussian process ; Bayesian inference ; Artificial neural network ; Kriging ; Markov chain Monte Carlo ; Regression ; Bayesian probability ; Property (philosophy) ; Gaussian ; Mathematics ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Probabilistic and Robust Engineering Design
Authors: Jinchuan Long | Lei Deng | Junsong Jin | Mao Zhang | Xuefeng Tang | Pan Gong | Xinyun Wang | Gangfeng Xiao | Qinxiang Xia
Source: Journal of Magnesium and Alloys (article, 2024-01-24)
Field: Materials Science — Biomaterials

Enhancing constitutive description and workability characterization of Mg alloy during hot deformation using machine learning-based Arrhenius-type model Hot deformation is a commonly employed processing technique to enhance the ductility and workability of Mg alloy. However, the hot deformation of Mg alloy is highly sensitive to factors such as temperature, strain rate, and strain, leading to complex flow behavior and an exceptionally narrow processing window for Mg alloy. To overcome the shortcomings of the conventional Arrhenius-type (AT) model, this study developed machine learning-based Arrhenius-type (ML-AT) models by combining the genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural network (ANN). Results indicated that when describing the flow behavior of the AQ80 alloy, the PSO-ANN-AT model demonstrates the most prominent prediction accuracy and generalization ability among all ML-AT and AT models. Moreover, an activation energy-processing (AEP) map was established using the reconstructed flow stress and activation energy fields based on the PSO-ANN-AT model. Experimental validations revealed that this AEP map exhibits superior predictive capability for microstructure evolution compared to the one established by the traditional interpolation methods, ultimately contributing to the precise determination of the optimum processing window. These findings provide fresh insights into the accurate constitutive description and workability characterization of Mg alloy during hot deformation. Materials science ; Particle swarm optimization ; Deformation (meteorology) ; Arrhenius equation ; Flow stress ; Constitutive equation ; Alloy ; Strain rate ; Activation energy ; Artificial neural network ; Characterization (materials science) ; Ductility (Earth science) ; Microstructure ; Artificial intelligence ; Biological system ; Composite material ; Machine learning ; Computer science ; Thermodynamics ; Nanotechnology ; Physics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: Sumanta Sahoo | Rajesh Kumar | Iftikhar Hussain | Kaili Zhang
Source: Advanced Powder Materials (article, 2024-10-05)
Field: Materials Science — Materials Chemistry

Heteroatom doping in 2D MXenes for energy storage/conversion applications MXenes (inorganic metal carbides, nitrides, and carbonitrides) are currently the rising star of two-dimensional (2D) family. After its discovery in 2011, initial research was concentrated on pristine MXenes only. However, in the last few years, the MXene family has been expanded with the exploration of novel double MXenes, synthesis of non-Ti MXenes, and heteroatom doping of MXenes. The current review article delivers an exclusive overview of the current research trends on the heteroatom doping of MXenes. The recent advances in heteroatom doping of MXenes (majorly Ti-MXenes) for energy storage/conversion applications including secondary batteries (Li-ion, Li–S, Na–S, Na-ion, K-ion, Zn-ion batteries), supercapacitors, electrocatalysis, etc. are summarized. A brief overview of the defects as well as doping in various 2D materials is included in the manuscript. Various doping strategies of MXenes are outlined. Moreover, the impact of artificial intelligence/machine learning on MXene research is also concisely discussed. Additionally, the advantages of doping on MXenes are discussed in detail. Lastly, the existing challenges and future prospects of doped MXenes are addressed. It is expected that the current review will open new prospects for the fabrication of advanced energy devices through heteroatom doping of MXenes. MXenes ; Heteroatom ; Doping ; Energy storage ; Materials science ; Chemistry ; Nanotechnology ; Optoelectronics ; Organic chemistry ; Physics ; Ring (chemistry) MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Memory and Neural Computing
Authors: Eslam M. Hamed | Fun Man Fung | Sam Fong Yau Li
Source: ACS Sensors (review, 2024-07-31)
Field: Materials Science — Materials Chemistry

Unleashing the Potential of Single-Atom Nanozymes: Catalysts for the Future Single-atom nanozymes (SANs) have become a breakthrough in atomically precise catalysis, which relies on the catalytic active site formed by the single-atom itself. From this angle, SANs and their advantages compared to natural enzymes as well as spaces for their application are emphasized. The SANs have outstanding control over their catalytic activities; this is compared with bulk materials and natural enzymes. The structure of the SANs has very promising potential for the next generation of biosensing and biomedical devices and environmental remediation. Although their capabilities are high, difficulties still arise. The specificity, scalability, biosafety, and catalysis mechanisms raise additional issues that require further research. We build up a vision of the perspectives of the better implementation of SANs, which are designed for diagnostic purposes, improving industrial technologies, and creating new sustainable technologies in the food processing industry. AI and machine learning systems may clarify the structure-performance relationship of SANs for improved material and process selectivity. The future of SANs is very promising, and by addressing these challenges and leveraging advancements in artificial intelligence and materials science, SANs have the potential to become powerful tools for a sustainable future. Nanotechnology ; Biosafety ; Scalability ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Biotechnology ; Biology Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Baoyin Yuan | Ning Wang | Chunmei Tang | Ling Meng | Lei Du | Qingwen Su | Yoshitaka Aoki | Siyu Ye
Source: Nano Energy (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Advances and challenges in high-performance cathodes for protonic solid oxide fuel cells and machine learning-guided perspectives Materials science ; Cathode ; Oxide ; Fuel cells ; Nanotechnology ; Conductivity ; Power density ; Power (physics) ; Chemical engineering ; Electrical engineering ; Engineering ; Chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials