jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richard Schier | Caterina Cocchi
Source: Physical Review Materials (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Electronic and optical excitations of K-Sb and Na-Sb crystals Recent advances in experimental techniques and computational methods have significantly expanded the family of alkali antimonides, a class of semiconducting materials used as photocathodes in particle accelerators, unveiling new crystal structures and stoichiometries with improved stability and quantum efficiency. This work investigates the electronic and optical properties of eight Na- and K-based alkali antimonide binary crystals with 3:1 and 1:1 alkali-to-antimony ratios, which were predicted to be stable in a recent high-throughput screening study. Employing density functional theory and many-body perturbation theory, we find that all systems exhibit direct band gaps, except for monoclinic <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:msub><a:mi>Na</a:mi><a:mn>8</a:mn></a:msub><a:msub><a:mi>Sb</a:mi><a:mn>8</a:mn></a:msub></a:mrow></a:math>, which has a nearly degenerate indirect gap. Optical spectra are characterized by near-infrared absorption onsets and intense visible excitations. Our analysis highlights the significant role of electron-hole correlations, particularly in K-based compounds, leading to exciton binding energies above 100 meV and sharper absorption peaks. In-depth inspection of the electronic contributions to the excited states provides additional insight into the role of excitonic effects. By shedding light on the fundamental properties of alkali antimonide binary crystals, our results are relevant for the design and optimization of next-generation electron sources for particle accelerators. Materials science ; Antimony ; Optoelectronics ; Condensed matter physics ; Crystallography ; Metallurgy ; Physics Solid-state spectroscopy and crystallography ; Photorefractive and Nonlinear Optics ; Luminescence Properties of Advanced Materials
Authors: Fatemeh Kamali | Seyedehmahrokh Moosavimordghafari | Majid Ghasemian | Naghmeh Kamali | Seyedeh Mahta Mojab | Fatemeh Nikfard | Zahra Nikfard | Saber Bakhtiaryfar
Source: Polish Journal of Cardio-Thoracic Surgery (review, 2025-01-01)
Field: Materials Science — Biomaterials

Artificial intelligence nanoparticle-based drug delivery systems and targeted delivery of therapeutics for the treatment of cardiovascular diseases: a systematic review This article reviews the progress of nanoparticles as drug carriers in the treatment of cardiovascular diseases, and how nanoparticles can deliver anti-inflammatory, anti-proliferative, and anticoagulant drugs directly to the surgical site in surgical procedures. For this, 45 articles published between 2005 and 2024 with keywords including "Artificial intelligence Nanoparticle", "Emergency Medicine Unit", and "Therapeutic for the treatment of cardiovascular diseases" in Scopus, Elsevier, Web of Science and ScienceDirect were reviewed. Nanoparticle-based drug delivery systems have demonstrated several noteworthy therapeutic benefits in the treatment of cardiovascular disorders since they were first used in the cancer field to deliver safer and more effective chemotherapeutics. By delivering targeted and site-specific medications with controlled release, nanoparticle-based drug delivery systems modify the biodistribution of therapeutic agents. An optimal material for cardiovascular therapeutics would be a nanoparticle based on metal, lipid, or polymer. Drug delivery ; SAFER ; Drug ; Targeted drug delivery ; Applications of artificial intelligence ; Precision medicine ; MEDLINE Nanoparticle-Based Drug Delivery ; Chemotherapy-induced cardiotoxicity and mitigation ; Cancer, Stress, Anesthesia, and Immune Response
Authors: Atik Setyani | Hendy Roesma Wardhana | Nur Amin | Nina Fapari Arif | A. Niko | Dwi Putra Prihandoyo
Source: RSF Conference Series Engineering and Technology (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Review of Shape Memory Alloys: Fundamental, Microstructure Property, and Emerging Trends in Industrial Applications Shape Memory Alloys (SMAs) are an exceptional category of functional materials that can restore predefined shapes or sustain substantial reversible deformations through thermoelastic martensitic transformations.In recent years, SMAs have attracted significant interest due to their unique combination of mechanical adaptability, durability, and multifunctionality, making them highly relevant for advanced engineering applications.This review provides a detailed examination of SMAs, including their classification into Ni, Cu, Febased, and emerging high-entropy alloy (HEA) systems, as well as a historical perspective on the development of the Shape Memory Effect (SME) and Superelasticity (SE).The discussion addresses critical factors influencing SMA performance, such as alloy chemistry, microstructural characteristics, processing techniques, quenching and homogenization treatments, and transformation temperatures (M , Mf, As, Af).Applications are explored with a focus on NiTi alloys in biomedical devices and precision actuators, Fe-Mn-Si for structural use, and Cu-Zn-Al for energy-efficient and cryogenic applications.Emerging directions, including soft robotics, thermal energy devices, and architected SMA structures enabled by additive manufacturing, are reviewed, along with strategies to mitigate issues such as cyclic degradation, hysteresis, and reliability concerns.By integrating theoretical insights with practical considerations, this review highlights the growing industrial relevance of SMAs as versatile, high-performance materials for next-generation adaptive systems. Microstructure ; Shape-memory alloy ; Field (mathematics) Shape Memory Alloy Transformations
Authors: Stefania Patti | S.A. De Rose | Michail N. Isupov | Ilaria Magrini Alunno | Sergio Riva | Erica Elisa Ferrandi | Jennifer A. Littlechild | Daniela Monti
Source: Research Square (preprint, 2025-05-13)
Field: Materials Science — Materials Chemistry

Functional and structural insights into a thermostable (S)-selective amine transaminase and its improved substrate scope by protein engineering Scope (computer science) ; Substrate (aquarium) ; Transaminase ; Protein engineering ; Amine gas treating ; Substrate specificity ; Chemistry ; Materials science ; Biochemistry ; Enzyme ; Computer science ; Biology ; Organic chemistry Enzyme Structure and Function ; Amino Acid Enzymes and Metabolism ; Cancer Research and Treatments
Authors: Guan-Ming Chen | Thijs Stuyver
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Harnessing surrogate models for data-efficient predictive chemistry: descriptors <i>vs.</i> learned hidden representations When data are scarce, surrogate models trained on QM descriptors help. We report that their hidden features, not the predicted descriptors, provide superior inputs for downstream predictive chemistry tasks. Pattern recognition (psychology) ; Artificial neural network ; Predictive modelling ; Feature selection ; Surrogate model ; Feature (linguistics) Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Laiba Ali | Dhia Hadi Hussain
Source: Journal of Kufa for Chemical Sciences (article, 2025-01-06)
Field: Chemistry — Inorganic Chemistry

Removal of methyl orange dye pollutant from aqueous solution by ghraphene oxide/ sodium algirate nono composite Abstract In order to provide inexpensive and efficient technique for wastewater treatment, the present research, aimed to prepare nano composites out of graphene oxide, namely GO/SA aerogels and focused on its application to remove methyl orange dye from industrial wastewater. Energy dispersive X-ray analysis (EDX), field-emission scanning electron microscopy (FE-SEM), and X-ray diffraction (XRD) were employed to identify the compound of GO/SA composite aerogel. High porosity was shown in nanomaterials, sample purity was demonstrated by EDX analysis, and the impact of temperature on dye absorption was investigated. In other words, the process is endothermic, as the data revealed that raising the temperature improved absorption efficiency. The results conclude that the GO/SA composite aerogel can be an effective adsorbent for eliminating the pollutants such as dyes from an aqueous environment. Aqueous solution ; Composite number ; Methyl orange ; Orange (colour) ; Pollutant ; Oxide ; Chemistry ; Sodium ; Materials science ; Composite material ; Organic chemistry ; Food science Pigment Synthesis and Properties ; Fuzzy and Soft Set Theory
Authors: Aboosaleh Mohammad Sharifi
Source:  (article, 2025-12-12)
Field: Materials Science — Biomaterials

A thorough and expansive Review of the Structure, Stability, and Phase Transformations of LPSO Phases in Magnesium-Rare Earth-Transition Metal Alloys The long-period stacking ordered (LPSO) phases in magnesium-based alloys, particularly Mg-RE-TM systems (where RE represents rare-earth elements and TM represents transition metals), offer a promising pathway to overcome inherent limitations such as l Expansive ; Phase (matter) ; Stacking ; Phase diagram ; Metal ; Transition metal Magnesium Alloys: Properties and Applications ; Metallurgical and Alloy Processes ; Quasicrystal Structures and Properties
Authors: Ankit Jain | Yagyank Srivastava | Amey G. Gokhale | Nidheesh Virakante | Hardik L. Kagdada
Source: ArXiv.org (preprint, 2025-05-29)
Field: Materials Science — Materials Chemistry

Higher-order thermal transport theory for phonon thermal transport in semiconductors using lattice dynamics calculations and the Boltzmann transport equation The phonon thermal conductivity of semiconducting periodic solids can be obtained using the lattice dynamics calculations along with the Boltzmann transport equation and with input from density functional theory calculations. These calculations have resulted in an excellent agreement with experiments without requiring any fitting parameters. However, over the last decade, many material systems have been identified where the lowest level lattice dynamics theory, which is based on the relaxation time approximation solution of the Boltzmann transport equation and considers potential energy surface sampling around the static equilibrium positions of atoms with only three-phonon scatterings, is proved insufficient in describing the thermal transport physics. In this article, we review these higher-order developments in the lattice dynamics theory to describe thermal transport in periodic semiconducting solids. We start with a brief discussion of the lowest-order theory and discuss its limitations along with proposed developments to address these limitations. We discuss prominent success cases of these higher-order developments and present our recommendations on their use for various material systems. Considering that many of these higher-order developments are computationally more demanding compared to the lowest-order theory, we also discussed data-driven approaches to accelerate these calculations. This review article is intended to serve as a reference for both novice and experienced researchers in this field. Boltzmann equation ; Phonon ; Thermal conductivity ; Lattice (music) ; Transport theory ; Thermal ; Convection–diffusion equation ; Lattice Boltzmann methods Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity
Authors: Ioan Barabulica | Marius Sebastian Secula | Eugenia Teodora Iacob-Tudose | Ioan Mămăligă
Source: Processes (article, 2025-06-28)
Field: Materials Science — Materials Chemistry

Experimental Study on the Influence of Magnesium on the Separation of Carbon Dioxide from Gas Mixtures with Nitrogen by Combustion Processes The goal of this paper consists in the experimental evaluation of the possibility to separate industrial gases using magnesium combustion in carbon dioxide–nitrogen mixtures of various concentrations. The choice was made primarily due to the chemical inertness of these two gases. The study investigates how the Mg combustion changes the concentration of the initial gas mixture and the possibility to apply this process to separate this gas mixture. On the other hand, due to its greenhouse effect, CO2 separation is a process of high interest in itself. Mg reacts exothermically with CO2, so a potential use for this purpose will also benefit from a significant amount of recovered thermal energy. N2 has a particular importance due to its potential to be purified using Mg combustion, and this application might be an economical alternative to air distillation, which is widely used for N2 production at industrial scale. In practice, the CO2-N2 mixtures are commonly used as flue gases resulting from various combustion systems. Mg combustion residue is analyzed by means of energy-dispersive X-ray spectroscopy. It is found that Mg can substantially reduce the concentration of CO2, even more than the stoichiometric reaction for the formation of MgO would suggest. The percentage decrease in CO2 concentration reaches values over 10 vol.%. A secondary yet notable effect is the heat generated by the Mg and CO2 reaction, which is currently being studied as an energy source alternative. Flue gas ; Combustion ; Carbon dioxide ; Chemistry ; Nitrogen ; Magnesium ; Air separation ; Distillation ; Flue-gas emissions from fossil-fuel combustion ; Stoichiometry ; Oxygen ; Organic chemistry Magnesium Oxide Properties and Applications ; Catalytic Processes in Materials Science ; Layered Double Hydroxides Synthesis and Applications
Authors: Harshan Reddy Gopidi | Ruiqi Zhang | Yanyong Wang | A. K. Patra | Jianwei Sun | Adrienn Ruzsinszky | John P. Perdew | Pieremanuele Canepa
Source: arXiv (Cornell University) (preprint, 2025-06-25)
Field: Materials Science — Materials Chemistry

Reducing Self-Interaction Error in Transition-Metal Oxides with Different Exact-Exchange Fractions for Energy and Density Density functional theory (DFT) in chemistry and materials science aims for "chemical accuracy," but this goal is challenged by the need to approximate the exact exchange-correlation (XC) energy functional. The r$^2$SCAN, meta-generalized gradient approximation to the XC functional fulfills 17 exact constraints of the XC energy, and has significantly boosted prediction accuracy for molecules and materials. However, r$^2$SCAN remains inadequate at predicting properties of open \textit{d} and \textit{f} transition-metal strongly correlated compounds, such as band gaps, magnetic moments, and oxidation energies. Prediction inaccuracies of r$^2$SCAN energies arise from functional and density-driven errors, mainly resulting from the DFT self-interaction error. We propose the r$^2$SCANY@r$^2$SCANX method to mitigate the self-interaction error of XC functionals for the accurate simulations of electronic, magnetic, and thermochemical properties of transition metal oxides. r$^2$SCANY@r$^2$SCANX uses different fractions of exact Hartree-Fock exchange: X for the electronic density and Y for the density functional approximation of the total energy, thereby simultaneously addressing functional-driven and density-driven inaccuracies. Building just on 1 (or maximum 2) parameters that apply unchanged to \emph{s-p}-bonded systems, we demonstrate that, r$^2$SCANY@r$^2$SCANX improves upon the r$^2$SCAN predictions for 20 highly correlated oxides and even outperforms the highly parameterized DFT(r$^2$SCAN)+\emph{U} method -- the state-of-the-art approach to predict strongly correlated materials. Prediction uncertainties for oxidation energies and magnetic moments of transition metal oxides are significantly reduced by r$^2$SCAN10@r$^2$SCAN50 and band gaps with r$^2$SCAN10@r$^2$SCAN. r$^2$SCAN10@r$^2$SCAN50 diminishes the density-driven error of the energy in r$^2$SCAN and r$^2$SCAN10. Parameterized complexity ; Density functional theory ; Set (abstract data type) ; Hybrid functional ; Energy (signal processing) ; Oxide ; Transition metal Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Magnetic and transport properties of perovskites and related materials
Authors: Alwin Drichel | Yves Garmshausen | Stefan Hecht
Source: Angewandte Chemie (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Dynamic Covalent Spiropyran Exchange for Rapid Structural Diversification Abstract Here we disclose that spiropyrans are able to undergo dynamic covalent exchange via their corresponding merocyanine isomers. In the latter, the indolinium moieties can be exchanged by a Michael‐type addition‐elimination sequence, in which a methylene indoline attacks a merocyanine and subsequently the initial indoline fragment is cleaved. The rate and position of the exchange equilibrium strongly depend on the reaction conditions as well as the substitution pattern on the methylene indoline fragments. Importantly, spiropyran cross exchange is catalyzed by indolinium salts and provides the opportunity for rapid in situ structural diversification of spiropyrans. This was used as synthetic tool to access various, hitherto inaccessible spiropyrans as potential dual‐color photoinitiators for xolography. Moreover, we foresee application to screen dynamic covalent libraries of spiropyrans for photochromic properties and to exploit their light‐sensitivity to bias the thermal equilibrium. Spiropyran ; Covalent bond ; Dynamic covalent chemistry ; Chemistry ; Diversification (marketing strategy) ; Nanotechnology ; Materials science ; Photochemistry ; Photochromism ; Molecule ; Organic chemistry ; Business ; Supramolecular chemistry Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; DNA and Nucleic Acid Chemistry
Authors: Shawn M. Gibford | Mohammad Reza Boskabadi | Christopher J. Savoie | Seyed Soheil Mansouri
Source: ArXiv.org (preprint, 2025-10-20)
Field: Materials Science — Materials Chemistry

Quantum Synthetic Data Generation for Industrial Bioprocess Monitoring Data scarcity and sparsity in bio-manufacturing poses challenges for accurate model development, process monitoring, and optimization. We aim to replicate and capture the complex dynamics of industrial bioprocesses by proposing the use of a Quantum Wasserstein Generative Adversarial Network with Gradient Penalty (QWGAN-GP) to generate synthetic time series data for industrially relevant processes. The generator within our GAN is comprised of a Parameterized Quantum Circuit (PQC). This methodology offers potential advantages in process monitoring, modeling, forecasting, and optimization, enabling more efficient bioprocess management by reducing the dependence on scarce experimental data. Our results demonstrate acceptable performance in capturing the temporal dynamics of real bioprocess data. We focus on Optical Density, a key measurement for Dry Biomass estimation. The data generated showed high fidelity to the actual historical experimental data. This intersection of quantum computing and machine learning has opened new frontiers in data analysis and generation, particularly in computationally intensive fields, for use cases such as increasing prediction accuracy for soft sensor design or for use in predictive control. Bioprocess ; Process (computing) ; Soft sensor ; Key (lock) ; Generator (circuit theory) ; Synthetic data ; Experimental data ; Data modeling Machine Learning in Materials Science ; Neural Networks and Reservoir Computing ; Quantum Computing Algorithms and Architecture
Authors: Zhuo Diao | Hayato Yamashita | Masayuki Abe
Source: Research Square (preprint, 2025-04-22)
Field: Materials Science — Materials Chemistry

A metaverse laboratory setup for interactive atom visualization and manipulation with scanning probe microscopy Visualization ; Scanning Force Microscopy ; Atomic force microscopy ; Nanotechnology ; Computer graphics (images) ; Human–computer interaction ; Microscopy ; Computer science ; Optics ; Materials science ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Virtual Reality Applications and Impacts ; Cell Image Analysis Techniques
Authors: Carl H. Fleischer | Sean T. Holmes | Robert W. Schurko
Source: CrystEngComm (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) of zwitterionic organic HCl salts In this work, we benchmark quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) for determining the crystal structures of two zwitterionic organic HCl salts, l -ornithine HCl ( Orn ) and l -histidine HCl·H 2 O ( Hist ). Crystal structure ; Crystal (programming language) ; Nuclear magnetic resonance crystallography ; Crystal structure prediction ; Single crystal ; Nuclear magnetic resonance spectroscopy Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Various Chemistry Research Topics
Authors: Tetsuya Theodore. Fujita
Source: ChemRxiv (preprint, 2025-09-10)
Field: Chemistry — Organic Chemistry

Hyperstructures and SuperHyperstructures in Biochemical,Electrochemical, Geochemical, Photochemical, Radiochemical, and Neurochemical Systems The notion of a Classical Structure provides a broad mathematical framework, while a Hyperstructure emerges through the powerset construction, and an 𝑛-Superhyperstructure is obtained by iterating this process 𝑛 times [1]. Intuitively, the 𝑛-th powerset corresponds to 𝑛 successive applications of the powerset operator. Fundamental definitions and simple illustrative examples are recalled. A Chemical Hyperstructure is an algebraic system modeling redox interactions among chemical species, where hyperoperations map pairs of reactants to the set of dominant products determined by maximal electromotive force (EMF). A Chemical Superhyperstructure of order 𝑛 extends this idea by applying the construction to the 𝑛-th powerset, thereby modeling nested redox interactions across multiple structural layers using maximal EMF selection. In this paper, we extend these ideas and investigate Hyperstructures and SuperHyperstructures in Biochemical, Electrochemical, Geochemical, Photochemical, Radiochemical, and Neurochemical Systems. This framework provides a systematic way to describe hierarchical concepts within these domains, offering a clearer mathematical representation of their layered interactions. It should be noted that this paper presents a purely theoretical study. We hope that future research will include qualitative, quantitative, and experimental investigations by domain experts to further validate and expand upon the concepts proposed here. Representation (politics) ; Simple (philosophy) ; Process (computing) ; Neurochemical ; Domain (mathematical analysis) ; Set (abstract data type) Free Radicals and Antioxidants ; Graph theory and applications ; Molecular Junctions and Nanostructures