jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zubair Akram | Ali Raza | Muhammad Mehdi | Anam Arshad | Xiling Deng | Shiguo Sun
Source: Nanomaterials (review, 2023-08-14)
Field: Materials Science — Materials Chemistry

Recent Advancements in Metal and Non-Metal Mixed-Doped Carbon Quantum Dots: Synthesis and Emerging Potential Applications In nanotechnology, the synthesis of carbon quantum dots (CQDs) by mixed doping with metals and non-metals has emerged as an appealing path of investigation. This review offers comprehensive insights into the synthesis, properties, and emerging applications of mixed-doped CQDs, underlining their potential for revolutionary advancements in chemical sensing, biosensing, bioimaging, and, thereby, contributing to advancements in diagnostics, therapeutics, and the under standing of complex biological processes. This synergistic combination enhances their sensitivity and selectivity towards specific chemical analytes. The resulting CQDs exhibit remarkable fluorescence properties that can be involved in precise chemical sensing applications. These metal-modified CQDs show their ability in the selective and sensitive detection from Hg to Fe and Mn ions. By influencing their exceptional fluorescence properties, they enable precise detection and monitoring of biomolecules, such as uric acid, cholesterol, and many antibiotics. Moreover, when it comes to bioimaging, these doped CQDs show unique behavior towards detecting cell lines. Their ability to emit light across a wide spectrum enables high-resolution imaging with minimal background noise. We uncover their potential in visualizing different cancer cell lines, offering valuable insights into cancer research and diagnostics. In conclusion, the synthesis of mixed-doped CQDs opens the way for revolutionary advancements in chemical sensing, biosensing, and bioimaging. As we investigate deeper into this field, we unlock new possibilities for diagnostics, therapeutics, and understanding complex biological processes. Nanotechnology ; Biomolecule ; Carbon quantum dots ; Biosensor ; Materials science ; Metal ions in aqueous solution ; Fluorescence ; Quantum dot ; Doping ; Metal ; Optoelectronics ; Physics Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Promeet K. Saha | Abhijit Mallick | Andrew T. Turley | Aisha N. Bismillah | Andrew Danos | Andrew P. Monkman | Alyssa‐Jennifer Avestro | Dmitry S. Yufit | Paul R. McGonigal
Source: Nature Chemistry (article, 2023-03-06)
Field: Chemistry — Organic Chemistry

Rupturing aromaticity by periphery overcrowding The balance between strain relief and aromatic stabilization dictates the form and function of non-planar π-aromatics. Overcrowded systems are known to undergo geometric deformations, but the energetically favourable π-electron delocalization of their aromatic ring(s) is typically preserved. In this study we incremented the strain energy of an aromatic system beyond its aromatic stabilization energy, causing it to rearrange and its aromaticity to be ruptured. We noted that increasing the steric bulk around the periphery of π-extended tropylium rings leads them to deviate from planarity to form contorted conformations in which aromatic stabilization and strain are close in energy. Under increasing strain, the aromatic π-electron delocalization of the system is broken, leading to the formation of a non-aromatic, bicyclic analogue referred to as 'Dewar tropylium'. The aromatic and non-aromatic isomers have been found to exist in rapid equilibrium with one another. This investigation demarcates the extent of steric deformation tolerated by an aromatic carbocycle and thus provides direct experimental insights into the fundamental nature of aromaticity. Aromaticity ; Chemistry ; Steric effects ; Planarity testing ; Delocalized electron ; Ring strain ; Electron delocalization ; Ring (chemistry) ; Computational chemistry ; Stereochemistry ; Crystallography ; Molecule ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Organoboron and organosilicon chemistry ; Fullerene Chemistry and Applications
Authors: Anthony Brown | Qingfei Pan | Li Fan | Émilie Indersie | Cheng Tian | Nikolai A. Timchenko | Liyuan Li | Baranda S. Hansen | Haiyan Tan | Meifen Lu | Junmin Peng | Shondra M. Pruett‐Miller | Jiyang Yu | Stefano Cairo | Liqin Zhu
Source: Communications Biology (article, 2023-03-07)
Field: Chemistry — Inorganic Chemistry

Ribonucleotide reductase subunit switching in hepatoblastoma drug response and relapse Prognosis of children with high-risk hepatoblastoma (HB), the most common pediatric liver cancer, remains poor. In this study, we found ribonucleotide reductase (RNR) subunit M2 (RRM2) was one of the key genes supporting cell proliferation in high-risk HB. While standard chemotherapies could effectively suppress RRM2 in HB cells, they induced a significant upregulation of the other RNR M2 subunit, RRM2B. Computational analysis revealed distinct signaling networks RRM2 and RRM2B were involved in HB patient tumors, with RRM2 supporting cell proliferation and RRM2B participating heavily in stress response pathways. Indeed, RRM2B upregulation in chemotherapy-treated HB cells promoted cell survival and subsequent relapse, during which RRM2B was gradually replaced back by RRM2. Combining an RRM2 inhibitor with chemotherapy showed an effective delaying of HB tumor relapse in vivo. Overall, our study revealed the distinct roles of the two RNR M2 subunits and their dynamic switching during HB cell proliferation and stress response. Hepatoblastoma ; Ribonucleotide reductase ; Protein subunit ; Drug ; Pharmacology ; Medicine ; Chemistry ; Cancer research ; Internal medicine ; Biology ; Genetics ; Gene Metal-Catalyzed Oxygenation Mechanisms ; RNA modifications and cancer ; Cancer, Hypoxia, and Metabolism
Authors: Shengli Zhuang | Dong Chen | Wai‐Pan Ng | Lijuan Liu | Mengying Sun | Dongyi Liu | Tehseen Nawaz | Qi Xia | Xia Wu | Yong‐Liang Huang | Seungkyu Lee | Jun Yang | Jian He
Source: Angewandte Chemie International Edition (article, 2023-06-16)
Field: Materials Science — Materials Chemistry

Phosphine‐Triggered Structural Defects in Au<sub>44</sub> Homologues Boost Electrocatalytic CO<sub>2</sub> Reduction The systematic induction of structural defects at the atomic level is crucial to metal nanocluster research because it endows cluster-based catalysts with highly reactive centers and allows for a comprehensive investigation of viable reaction pathways. Herein, by substituting neutral phosphine ligands for surface anionic thiolate ligands, we establish that one or two Au<sub>3</sub> triangular units can be successfully introduced into the double-stranded helical kernel of Au<sub>44</sub> (TBBT)<sub>28</sub> , where TBBT=4-tert-butylbenzenethiolate, resulting in the formation of two atomically precise defective Au<sub>44</sub> nanoclusters. Along with the regular face-centered-cubic (fcc) nanocluster, the first series of mixed-ligand cluster homologues is identified, with a unified formula of Au<sub>44</sub> (PPh<sub>3</sub> )<sub>n</sub> (TBBT)<sub>28-2n</sub> (n=0-2). The Au<sub>44</sub> (PPh<sub>3</sub> )(TBBT)<sub>26</sub> nanocluster having major structural defects at the bottom of the fcc lattice demonstrates superior electrocatalytic performance in the CO<sub>2</sub> reduction to CO. Density functional theory calculations indicate that the active site near the defects significantly lowers the free energy for the *COOH formation, the rate-determining step in the whole catalytic process. Phosphine ; Reduction (mathematics) ; Electrocatalyst ; Materials science ; Chemistry ; Electrochemistry ; Electrode ; Catalysis ; Physical chemistry ; Mathematics ; Organic chemistry Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Yuzhen Zhao | Chaonian Li | Tingting Lang | Jianjing Gao | Huimin Zhang | Yang Zhao | Zhun Guo | Zongcheng Miao
Source: Molecules (review, 2023-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research Progress on Intrinsically Conductive Polymers and Conductive Polymer-Based Composites for Electromagnetic Shielding Electromagnetic shielding materials are special materials that can effectively absorb and shield electromagnetic waves and protect electronic devices and electronic circuits from interference and damage by electromagnetic radiation. This paper presents the research progress of intrinsically conductive polymer materials and conductive polymer-based composites for electromagnetic shielding as well as an introduction to lightweight polymer composites with multicomponent systems. These materials have excellent electromagnetic interference shielding properties and have the advantages of electromagnetic wave absorption and higher electromagnetic shielding effectiveness compared with conventional electromagnetic shielding materials, but these materials still have their own shortcomings. Finally, the paper also discusses the future opportunities and challenges of intrinsically conductive polymers and composites containing a conductive polymer matrix for electromagnetic shielding applications. Electromagnetic shielding ; Materials science ; Electromagnetic interference ; Composite material ; Electrical conductor ; Electromagnetic radiation ; Conductive polymer ; Polymer ; Electromagnetic compatibility ; Electrical engineering ; Physics ; Engineering ; Optics Electromagnetic wave absorption materials ; Environmental Engineering and Cultural Studies
Authors: Dan Guevarra | Kevin Kan | Yungchieh Lai | Ryan J. R. Jones | Lan Zhou | Phillip Donnelly | Matthias H. Richter | Helge S. Stein | John M. Gregoire
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Orchestrating nimble experiments across interconnected labs Human researchers multi-task, collaborate, and share resources. HELAO-async is a multi-workflow automation software that helps realize these attributes in materials acceleration platforms. Workflow ; Task (project management) ; Automation ; Computer science ; Software engineering ; Software ; Asynchronous communication ; Systems engineering ; Engineering ; Operating system ; Database ; Telecommunications ; Mechanical engineering Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Malihe Sadat Razavi | Alyeh Abdollahi | Atefeh Malek-Khatabi | Negin Mousavi Ejarestaghi | Ali Atashi | Nazanin Yousefi | Pedram Ebrahimnejad | Mohamed A. Elsawy | Rassoul Dinarvand
Source: Journal of Drug Delivery Science and Technology (article, 2023-05-20)
Field: Materials Science — Biomaterials

Recent advances in PLGA-based nanofibers as anticancer drug delivery systems PLGA ; Nanofiber ; Drug delivery ; Nanotechnology ; Biocompatible material ; Materials science ; Targeted drug delivery ; Controlled release ; Biomedical engineering ; Nanoparticle ; Medicine Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Graphene and Nanomaterials Applications
Authors: Haruna Adamu | Sani I. Abba | Paul Betiang Anyin | Yusuf Sani | Mohammad Qamar
Source: Energy Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials With increased awareness of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) can facilitate fast development of high-performance electrochemical energy storage systems (EESSs). Electrochemical energy storage ; Computer science ; Feature (linguistics) ; Artificial intelligence ; Energy storage ; Electrochemistry ; Nanotechnology ; Materials science ; Supercapacitor ; Chemistry Machine Learning in Materials Science ; Advancements in Battery Materials ; Transition Metal Oxide Nanomaterials
Authors: Christopher Karpovich | Elton Pan | Zach Jensen | Elsa Olivetti
Source: Chemistry of Materials (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by heuristics and chemists’ prior knowledge and experience, built upon experimental trial-and-error that can be both time and resource consuming. Recent developments in natural language processing have enabled large-scale text mining of scientific literature, providing open-source databases of synthesis information on realized compounds, material precursors, and reaction conditions (temperatures, times). We employ supervised classification machine learning (ML) models to distinguish between solid-state, sol–gel, and solution (hydrothermal, precipitation) synthesis routes based on specified reaction target material and/or precursor materials. We demonstrate regression ML models that are able to predict suitable temperatures and times for the crucial inorganic synthesis steps of calcination and sintering given the reaction target and precursor materials. We contrast this regression-based condition modeling with a conditional variational autoencoder neural network that can generate appropriate distributions for the synthesis conditions of interest. We evaluate model interpretability using the Shapley additive explanations approach to gain insight into factors influencing suitability of synthesis route and reaction conditions. We find that the aforementioned models are capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route that are present in the inorganic synthesis space. Moreover, they generalize well to unseen chemical entities, outperform common heuristics in the field, and show promise for predicting appropriate reaction routes and conditions for previously unsynthesized compounds of interest. Computer science ; Interpretability ; Heuristics ; Machine learning ; Artificial intelligence ; Artificial neural network ; Synthetic data ; Chemical space ; Autoencoder ; Chemistry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Pooya Jafari | Ruoran Zhang | Siqi Huo | Qingsheng Wang | Jianming Yong | Hong Min | Ravinesh C. Deo | Hao Wang | Pingan Song
Source: Composites Communications (article, 2023-12-31)
Field: Materials Science — Polymers and Plastics

Machine learning for expediting next-generation of fire-retardant polymer composites Machine learning algorithms have emerged as an effective and popular decision-making tool for solving complicated engineering problems and challenges. Although introducing these algorithms can accelerate the optimization of fire retardants for polymeric materials by replacing traditional tedious and time-consuming trial-and-error methods, this tool remains at the elementary stage of designing fire retardants for polymeric materials, and thus to date there is a lack of insightful yet review on this topic. Herein, we review the most practical and accurate algorithms used to predict flame retardancy features, such as limiting oxygen index (LOI) and cone calorimetry results, of their polymeric materials. We highlight the merits of some current algorithms, including artificial neural network (ANN), Lasso, Ridge, ANN (L-ANN), and extreme gradient boosting (XGB). Finally, key challenges with existing algorithms for predicting next-generation fire retardants, followed by some proposed solution and future directions. This review will help expedite the development of optimized fire retardants accelerated by machine learning. Fire retardant ; Limiting oxygen index ; Computer science ; Limiting ; Artificial neural network ; Expediting ; Algorithm ; Machine learning ; Artificial intelligence ; Materials science ; Systems engineering ; Engineering ; Mechanical engineering ; Char ; Composite material Flame retardant materials and properties ; Covalent Organic Framework Applications ; Thermal and Kinetic Analysis
Authors: Chunfeng Cui | Yuwen Zhang | Tao Ouyang | Mingxing Chen | Chao Tang | Qiao Chen | Chaoyu He | Jin Li | Jianxin Zhong
Source: Physical Review Materials (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

On-the-fly machine learning potential accelerated accurate prediction of lattice thermal conductivity of metastable silicon crystals In this paper, we propose a convenient strategy to accelerate the evaluation of lattice thermal conductivity through combining phonon Boltzmann transport equation (PBTE) and on-the-fly machine learning potential (FMLP). The thermal conductivity of diamond silicon ($d\text{\ensuremath{-}}\mathrm{Si}$) is evaluated firstly by density functional theory (DFT), FMLP, and empirical potential with PBTE, respectively. The results demonstrate the proposed strategy integrates the prediction accuracy of DFT and computational speed of empirical potential, breaking the dilemma of traditional thermal conductivity assessment schemes. Based on this, the efficient strategy is applied to predict thermal conductivity of 102 low-energy metastable silicon crystals with energies between $d\text{\ensuremath{-}}\mathrm{Si}$ and experimentally ${\mathrm{Si}}_{24}$. Among them, the $Cmcm\text{\ensuremath{-}}{\mathrm{Si}}_{16}, P6/mmm\text{\ensuremath{-}}{\mathrm{Si}}_{36}$-2, $Pnma\text{\ensuremath{-}}{\mathrm{Si}}_{32}$-2 are predicted to host lowest lattice thermal conductivity in $xx$ (8.213 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$), $yy$ (10.917 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$), and $zz$ (11.807 ${\mathrm{Wm}}^{\ensuremath{-}1}{\mathrm{K}}^{\ensuremath{-}1}$) directions, respectively. Such low lattice thermal conductivity benefits from the combined effect of low phonon group velocity and intense phonon scattering caused by distorted $s{p}^{3}$ hybrid states in these metastable silicon crystals. The findings presented in this work provide new candidates and insights of silicon-based materials with ultra-low thermal conductivity, which will greatly expand the applications in thermoelectric and thermal insulation fields. Thermal conductivity ; Metastability ; Materials science ; Phonon ; Silicon ; Lattice (music) ; Condensed matter physics ; Boltzmann equation ; Physics ; Thermodynamics ; Quantum mechanics ; Optoelectronics Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Chunfeng Cui | Yuwen Zhang | Tao Ouyang | Chao Tang | Chaoyu He | Jin Li | Mingxing Chen | Jianxing Zhong
Source: Applied Physics Letters (article, 2023-10-09)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials as efficient tools for obtaining reasonable phonon dispersions and accurate thermal conductivity: A case study of typical two-dimensional materials The accurate description of phonon dispersion of two-dimensional (2D) materials demonstrates significance in many research fields of condensed matter physics. In this paper, we systematically calculate the phonon spectra and transport properties of six representative 2D materials (encompassing single-element and binary compounds with flat, buckled, and puckered backbone geometries) by means of density functional theory (DFT) and two machine learning interatomic potentials [MLIPs, on-the-fly machine learning potential (FMLP), and moment tensor potential (MTP)]. The results show that the acoustic out-of-plane flexural (ZA) dispersion of the 2D materials are always and easily exhibiting non-quadratic dispersion phenomena near the center of the Brillouin zone by using the pure DFT calculation method. This phenomenon contradicts physics and reflects intuitively from the non-zero group velocity at Γ point. However, no matter which MLIP (FMLP/MTP) the calculation is based on, it could solve such behavior perfectly, where the ZA mode conforms to the quadratic dispersion relationship in the long-wavelength limit. Our results further demonstrate that compared to the pure DFT calculation, the FMLP and MTP method could quickly and relatively accurately obtain the lattice thermal conductivities of graphene, silicene, phosphorene, SiC, MoS2, and GeS. The findings presented in this work provide a solution about the pseudophysical phenomenon of ZA dispersions in 2D materials with the pure DFT calculation, which will greatly facilitate research areas such as phonon thermal transport, flexural mechanics, and electron–acoustic coupling. Phonon ; Density functional theory ; Condensed matter physics ; Brillouin zone ; Long wavelength limit ; Thermal conductivity ; Materials science ; Graphene ; Silicene ; Physics ; Quantum mechanics ; Nanotechnology Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Luis T. Díaz Müller | Mikhail Galkin | Christopher G. Morris | Ladislav Rampášek
Source: arXiv (Cornell University) (preprint, 2023-02-08)
Field: Materials Science — Materials Chemistry

Attending to Graph Transformers Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks. So far, they have shown promising empirical results, e.g., on molecular prediction datasets, often attributed to their ability to circumvent graph neural networks' shortcomings, such as over-smoothing and over-squashing. Here, we derive a taxonomy of graph transformer architectures, bringing some order to this emerging field. We overview their theoretical properties, survey structural and positional encodings, and discuss extensions for important graph classes, e.g., 3D molecular graphs. Empirically, we probe how well graph transformers can recover various graph properties, how well they can deal with heterophilic graphs, and to what extent they prevent over-squashing. Further, we outline open challenges and research direction to stimulate future work. Our code is available at https://github.com/luis-mueller/probing-graph-transformers. Computer science ; Transformer ; Theoretical computer science ; Graph ; Machine learning ; Engineering Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms