jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin-Yu Zhang | Juan Du | Lin Yang | Frédéric Mompiou | Shigenobu Ogata | Wen-Zheng Zhang
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Dislocation-ledge coupling drives non-conservative migration of semicoherent precipitate interfaces Precipitate shape and size control the strength and stability of many structural alloys, yet the microscopic mechanism by which semicoherent precipitate interfaces migrate remains unclear. In particular, how dense interfacial dislocation networks move while accommodating transformation strain has resisted direct, time-resolved characterization. Here, we show that non-conservative motion of interfacial dislocations is intrinsically coupled to the nucleation and lateral propagation of nanoscale growth ledges, providing a defect-based kinetic description of lath growth. Phase-field-crystal simulations of a prototypical face-centered cubic/body-centered cubic (FCC/BCC) transformation resolve strongly anisotropic interface kinetics: the end face advances continuously along the lath long axis, whereas facets thicken by discrete ledge sweeps accompanied by mixed glide-climb reactions in a closed dislocation network. Crystallographic analyses predict the dislocation arrangements, rationalize the anisotropy via the geometry of misfit localization, and show how dislocation motion accommodates the transformation strain. In situ transmission electron microscopy of austenite precipitates in duplex stainless steel captures rapid ledge propagation on habit planes, consistent with the predicted migration mode. Our results bridge point-defect transport, dislocation reactions, and interface mobility, enabling quantitative, transferable predictions of precipitate morphology evolution. Lath ; Dislocation ; Nucleation ; Faceting ; Transmission electron microscopy ; Coupling (piping) ; Anisotropy ; Instability Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Marisa Menezes Leal | Nairiane dos Santos Bilhalva | Rosana Santos de Moraes | Dthenifer Cordeiro Santana | Larissa Pereira Ribeiro Teodoro | Paulo Eduardo Teodoro | Paulo Carteri Coradi
Source: European Food Research and Technology (article, 2026-03-06)
Field: Chemistry — Analytical Chemistry

A method for classifying the physicochemical quality of soybean grains using near-infrared spectroscopy, hyperspectral sensor technology, and artificial intelligence models Soybean quality is crucial for the industry’s competitiveness, but traditional grading methods are slow, subjective, and inaccurate. Non-destructive technologies combined with machine learning algorithms emerge as promising alternatives to overcome these limitations. This study aimed to investigate the use of Near-Infrared (NIR) Spectroscopy and hyperspectral sensors as fast and accurate tools to analyze the physicochemical properties of soybean grains, as well as to identify the machine learning (ML) models with the highest accuracy in classifying the different types. One hundred whole-grain samples were used for each of Types I, II, Basic Standard, and Off-Type. The moisture, crude protein, starch, lipid, crude fiber, and ash contents were determined using a FOSS NIR DS2500 spectrometer. Hyperspectral reflectance data were acquired with an ASD FieldSpec 4 Jr sensor. The instrument covers the 350 to 2500 nm spectral range. The acquired spectra were processed and averaged into representative intervals prior to model development. Six ML models were tested in WEKA 3.8.6 softwares: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), Zero-R (SL), J48 Decision Tree (J48), and REPTree, evaluated using the metrics correct classification percentage, Kappa, and F-score. Among the models evaluated, the SVM model showed the best performance in classifying the physicochemical quality of soybean grains, achieving 99.5% accuracy, followed by ANN (88.1%) and J48 (76.4%). For the SVM model, the regularization (C) and radial basis kernel (γ) parameters were maintained at the standard values ​​defined by the WEKA 3.8.6 software. The Basic Standard and Off-Type classes obtained the highest class-specific accuracies among all grain types. The integration of NIR, hyperspectral sensors, and ML enabled the characterization of the physicochemical quality of the soybeans, demonstrating classification efficiency and outperforming conventional methods. The average analysis time per sample was approximately one minute, highlighting the potential of this approach to automate soybean grading, with greater reliability, agility, and standardization of the process. Hyperspectral imaging ; Quality (philosophy) ; Pattern recognition (psychology) ; Artificial neural network ; Applications of artificial intelligence Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biopolymer Synthesis and Applications
Authors: R.N. Czerwinski | Anna L. Clayborn | Aisley Fleming | Andrew J. Boydston | Aaron A. Hoskins | Randall H. Goldsmith
Source: ChemRxiv (article, 2026-03-10)
Field: Chemistry — Organic Chemistry

Molecule by Molecule Characterization of a Polymer Molecular Mass Distribution via Mass Photometry Polymer analysis tools form the backbone of modern polymer technology. However, traditional tools like size exclusion chromatography (SEC) fractionate based on hydrodynamic volume. This analysis assumes all molecules in a fraction are identical, an increasingly strained assumption for complex macromolecules. Importantly, characterization of the molecular mass distribution (MMD) is typically reduced to reporting several moments of the MMD, like number and weight-averaged molecular masses, a significant loss of information. In contrast, a molecule-by-molecule elucidation of the full MMD would yield an extremely informative new perspective on macromolecule diversity, while eliminating limitations and time commitment associated with fractionation. Here, we show that mass photometry (MP), whereby the interferometric scattering signal of individual molecules is correlated with mass, of high-molecular weight polyethylene oxide (PEO) allows elucidation of the full MMD. In some cases, multiple subpopulations are directly revealed. This effort provides the first single-molecule measurement of the MMD of a synthetic polymer, bypassing artifacts introduced by bulk-scale measurements. Further, we show that Dispersity (Đ) can be precisely determined in a manner that is largely calibration-free, allowing determination of Đ in functional polymers where monodisperse standards are unavailable. Integration of MP with SEC conspicuously shows how multiple populations are present in collected fractions. Dispersity ; Polymer ; Macromolecule ; Size-exclusion chromatography ; Molecule ; Characterization (materials science) ; Photometry (optics) ; Molecular mass Advanced Polymer Synthesis and Characterization ; Material Dynamics and Properties ; Rheology and Fluid Dynamics Studies
Authors: Hari Madhav | Ashutosh Panda | Dipanwita Datta | Kashfa Shaheen | Abdur Rahman | Azhar Tariq Khan | Souvik Bhattacharjee | Dhiraj Kumar | Pawan Malhotra | Nasimul Hoda
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Development of substituted 2-(4-(sulfonyl)piperazin-1-yl)quinazoline molecular hybrids as a new class of antimalarials The rapid emergence of drug resistance makes malaria elimination a global challenge despite the prevalence of artemisinin-based combination therapies (ACTs), thus highlighting the urgent need for the development of new antimalarials with novel modes of action. The present study aimed to develop new quinazoline hybrid antimalarials using bioactive small building blocks. The antimalarial activity results revealed that most molecular hybrids have IC<sub>50</sub> values below 10 µM for the drug-sensitive <i>Pf</i>3D7 strain. The study identified molecular hybrids 19, <i>N</i>-(2-chloro-4-((4-(4-(((tetrahydrofuran-2-yl)methyl)amino)quinazolin-2-yl)piperazin-1-yl)sulfonyl)phenyl)acetamide and 27, 2-(4-((2-nitrophenyl)sulfonyl)piperazin-1-yl)-4-(3-(trifluoromethyl)-5,6-dihydro-[1,2,4]triazolo[4,3-<i>a</i>]pyrazin-7(8<i>H</i>)-yl)quinazoline as potent antimalarials with an IC<sub>50</sub> value of 3.4 µM and 2.9 µM against <i>Pf</i>3D7, respectively. The cytotoxicity investigation against mammalian A549 cells and activated macrophages derived from THP1 monocytes revealed that the compounds were relatively non-cytotoxic, and their antimalarial activity was not associated with cytotoxicity. <i>In silico</i> studies were conducted to predict plausible drug targets of the compounds, and the results suggested that the antimalarial activity of the compounds may be due to the inhibition of zinc metalloprotease <i>Pf</i>FLN, with concurrent inhibition of cysteine proteases <i>Pf</i>FP2 and <i>Pf</i>FP3. The MM-GBSA analysis revealed that the binding free energies of 19 and 27 with <i>Pf</i>FLN were -50.3223 and -51.5066 kcal mol<sup>-1</sup>, respectively. The predicted ADME properties of the compounds fall within the Schrödinger range, which encompasses 95% of all known medications. The study thus emphasised the significance of the molecular hybridisation approach and highlighted compounds 19 and 27 as potent hit molecules that could be further optimised for the development of new antimalarials. Quinazoline ; ADME ; Druggability ; Antimalarial Agent ; Malaria ; Cytotoxicity ; Drug ; Drug development Quinazolinone synthesis and applications ; Malaria Research and Control ; Synthesis and biological activity
Authors: Y. Mishin
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

On a relationship between grain boundary free energy, grain boundary segregation, and grain boundary diffusion We present a detailed analysis of the universal relationship between grain boundary (GB) free energy and GB self-diffusion coefficient derived by Borisov et al. (1964). This relationship was expressed by a simple equation that was used in many publications to predict GB energies on the basis of experimental diffusion data. Meanwhile, the physical assumptions and approximations underlying the Borisov model are poorly understood. As a result, the Borisov equation was often used outside its intended limits. Here, we re-derive the Borisov equation from ground up, identifying its underlying assumptions, correcting some errors and inconsistencies, and extending the original model to the case of impurity diffusion and diffusion by the interstitialcy and interstitial-dumbbell mechanisms. The meaning of the key assumption of the Borisov model, related to the free energy of the activated complex, is discussed, and ways to test this assumption are proposed. Diffusion ; Boundary (topology) ; Grain boundary diffusion coefficient ; Effective diffusion coefficient ; Grain boundary ; Impurity ; Energy (signal processing) Microstructure and mechanical properties ; Magnetic Properties and Applications ; Solidification and crystal growth phenomena
Authors: Chao Ruan | Xinkuo Li | Ke Sun | Jianrong Qiu | Dezhi Tan
Source: Opto-Electronic Advances (article, 2026-01-01)
Field: Materials Science — Ceramics and Composites

Perovskite nanocrystals in glass for high efficiency and ultra-high resolution dynamic holographic multicolor display Incorporating perovskite nanocrystals (PNCs) in the glass matrix has been demonstrated to be an effective route to improve their stability for long-term operation. However, simultaneously achieving high luminance and high photoluminescence (PL) quantum yield (QY) is challenging. Herein, we report a strategy that employs fluoride ion doping to modify the three-dimensional glass network, thereby optimizing the crystallization behavior of PNCs and achieving both high luminance and high PLQY in full-spectrum. Leveraging these high-performance transparent composites, we constructed a dynamic holographic multicolor display system by integrating with a spatial light modulator (SLM), achieving a pixel density as high as 20,247 pixels per inch (PPI). We further propose a vertically stacked, multilayer full-color display architecture that overcomes the limitations of color filters in light-utilization efficiency and the bottlenecks of conventional planar sub-pixel layouts in terms of spatial utilization and resolution. Holography ; Luminance ; Pixel ; Photoluminescence ; Planar ; Nanocrystal ; Perovskite (structure) ; Spatial light modulator ; Transmittance Glass properties and applications ; Phase-change materials and chalcogenides ; Photorefractive and Nonlinear Optics
Authors: Timofey Frolov | Jörg Neugebauer | Y. Mishin
Source: MRS Bulletin (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Thermodynamics of grain-boundary phases Abstract The past decade has seen a significant increase in research efforts aimed at understanding the thermodynamics of low-dimensional phases existing in many materials systems, ranging from two-dimensional materials to core regions of extended defects in crystalline solids. We review the current status of theoretical, computational, and experimental research on the “defect phases,” focusing on grain boundaries (GBs) in elemental and multicomponent polycrystalline materials. After reviewing the generalized concept of a phase of any dimensionality, we discuss recent progress in atomistic computer simulations of GB phase transformations and phase coexistences, including the observation of one-dimensional defects separating GB phases (defects in defects). Computational predictions compare well with experimental observations of multiple GB phases and segregation-induced phase transformations. An intriguing open question of GB thermodynamics is whether the GB free energy can be driven to a zero value by increasing solute segregation. We review recent efforts to understand this ultimate thermodynamic stabilization of GB phases and the possible polycrystalline microstructures that may arise. An outlook for future research in the field is discussed. Graphical Abstract Crystallite ; Phase (matter) ; Grain boundary ; Field (mathematics) ; Microstructure ; Core (optical fiber) ; Energy (signal processing) Microstructure and mechanical properties ; 2D Materials and Applications ; Surface and Thin Film Phenomena
Authors: Pavan Karjol | Kumar Shubham | Prathosh AP
Source: ArXiv.org (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Spectral Discovery of Continuous Symmetries via Generalized Fourier Transforms Continuous symmetries are fundamental to many scientific and learning problems, yet they are often unknown a priori. Existing symmetry discovery approaches typically search directly in the space of transformation generators or rely on learned augmentation schemes. We propose a fundamentally different perspective based on spectral structure. We introduce a framework for discovering continuous one-parameter subgroups using the Generalized Fourier Transform (GFT). Our central observation is that invariance to a subgroup induces structured sparsity in the spectral decomposition of a function across irreducible representations. Instead of optimizing over generators, we detect symmetries by identifying this induced sparsity pattern in the spectral domain. We develop symmetry detection procedures on maximal tori, where the GFT reduces to multi-dimensional Fourier analysis through their irreducible representations. Across structured tasks, including the double pendulum and top quark tagging, we demonstrate that spectral sparsity reliably reveals one-parameter symmetries. These results position spectral analysis as a principled and interpretable alternative to generator-based symmetry discovery. Homogeneous space ; Fourier transform ; Symmetry (geometry) ; Function (biology) ; Perspective (graphical) ; Fourier analysis ; Discrete Fourier transform (general) ; Irreducible representation ; Space (punctuation) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: G. H. P. Ganegoda | Rohan S. Dassanayake | Danushika C. Manatunga | K. K. Asanka Sanjeewa | K. G. L. R. Jayathunge | Renuka Nilmini | Yang Zhou | Yuanyuan Liu
Source: Sustainable Food Technology (article, 2026-01-01)
Field: Materials Science — Biomaterials

Polysaccharide-based pH-responsive intelligent halochromic food packaging materials: a review The pH-responsive intelligent food packaging materials serve as freshness indicators, changing colour in response to pH shifts related to food spoilage and the release of microbial metabolites. Food spoilage ; Food packaging ; Active packaging ; Food processing ; Food products ; Food industry ; Food preservation ; Food storage Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Biopolymer Synthesis and Applications
Authors: Rafia Noor | Madiha Sahar Khan | Samman Ikram | Hira Mubeen
Source: Research Square (preprint, 2026-02-20)
Field: Chemistry — Organic Chemistry

Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation Quinoxaline ; Docking (animal) ; In silico ; AutoDock ; Molecular model ; Antibacterial activity ; Broad spectrum Synthesis and Biological Evaluation ; Quinazolinone synthesis and applications ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Аскар Абдыкадыров | Akerke Dyussenbiyeva | Abdurazak Kasimov | Muratbek Yermekbaev | Koptleu Bazhikov | Nurlan Kystaubayev | Yessen Bagdollauly | Altyngul Turebekova | Aidar Kuttybayev | Sunggat Marxuly
Source: Advances in Science and Technology – Research Journal (article, 2026-02-26)
Field: Chemistry — Physical and Theoretical Chemistry

Control and optimization of ozone synthesis using optical technologies Ozone is a compound that plays a crucial role in environmental and industrial applications.However, traditional methods of ozone synthesis require high energy consumption and lack precision.This study focuses on optimizing the process and improving efficiency in ozone production by utilizing optical technologies.The primary goal of the research is to monitor ozone concentration in real-time using optical methods while reducing energy consumption.The ETRO-02 ozone generator was employed during the study, and methods such as ultraviolet spectrophotometry and laser spectroscopy were utilized.It was demonstrated that ozone concentration can be measured with an accuracy of 0.1 ppm, and energy consumption can be reduced from 15 kWh/kg to 10 kWh/kg.The results indicate that optical monitoring methods increase production efficiency by 30% and precisely determine linear relationships between ozone concentration and synthesis rates.This approach enables the automation of industrial processes while ensuring environmental sustainability.Future work suggests the development of real-time monitoring algorithms and the integration of new optical methods. Ozone ; Process control ; Control (management) ; Control system ; Production (economics) Chemical Reactions and Mechanisms ; Atmospheric Ozone and Climate ; Laser Design and Applications
Authors: Chrysi Panagopoulou | Panagiotis Bousoulas | Apostolos Kalafatis | Spyros Orfanoudakis | Charalampos Tsioustas | Alexandros Banis | Polychronis Tsipas | Athanassios G. Kontos | Thomas Stergiopoulos | Dimitris Tsoukalas
Source: Journal of Materials Chemistry C (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Optoelectronic inhibitory synapses in the visible range from PbS nanocrystal arrays Optoelectronic inhibitory synapses play critical roles in modulating neural activity and maintaining the balance between excitation and inhibition within artificial neural circuits. Nanocrystal ; Inhibitory postsynaptic potential ; Excitation ; Modulation (music) ; Range (aeronautics) ; Visible spectrum Quantum Dots Synthesis And Properties ; Advanced Memory and Neural Computing ; Chemical and Physical Properties of Materials
Authors: Oliver Knitter | Dan Zhao | Stefan Leichenauer | Shravan Veerapaneni
Source: Machine Learning Science and Technology (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Large language model scaling laws for neural quantum states in quantum chemistry Abstract Scaling laws have been used to describe how large language model (LLM) performance scales with model size, training data size, or amount of computational resources. Motivated by the fact that the field of neural quantum states (NQS) has increasingly adopted LLM-based components, we seek to understand NQS scaling laws, thereby shedding light on the scalability and optimal performance-resource trade-offs of NQS ansätze. In particular, we identify scaling laws that predict the performance, as measured by absolute error and V -score, for transformer-based NQS as a function of problem size in second-quantized quantum chemistry applications. By performing analogous compute-constrained optimization of the obtained parametric curves using a simplified compute estimate model, we find that the relationship between model size and training time is highly dependent on loss metric and ansatz, and does not follow the approximately linear compute-optimal tradeoff found by the Chinchilla laws. Quantum ; Scaling ; Metric (unit) ; Function (biology) ; Parametric statistics ; Scaling law ; Scalability Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Hee Min Kim | Ha Yeon Park | Woo Yeong Jeong | Seung-Won Yoo | Hee Jung Park | Yong-Seok Choi
Source: Ceramist (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Assessment of Layered Oxides (H2Ti3O7) as Viable Alternative to Perovskite (BaZr1-xYxO3-y) for Protonic Ceramic Fuel Cells: Insights from Atomic-Scale Simulations Owing to its intermediate operation temperature (500–800°C), protonic ceramic fuel cells (PCFCs) suffer from poor proton migration kinetics that greatly hampers the hydrogen conversion efficiency. To overcome such drawbacks, BaZr₁₋ₓYₓO₃₋ᵧ (BZYO) with a perovskite structure has been widely employed as a representative proton-conducting electrolyte for PCFCs. Its high proton conductivity originates from the presence of abundant oxygen vacancies, favorable hydration reactions, and a large lattice free volume that facilitates proton migration. Such characteristics, interestingly, are also reported in layered oxides. H&lt;sub&gt;2&lt;/sub&gt;Ti&lt;sub&gt;3&lt;/sub&gt;O&lt;sub&gt;7&lt;/sub&gt;, for instance, possesses a large lattice free volume and readily forms oxygen vacancies as intrinsic defects, implying its capability to achieve high proton conductivity. In this study, we investigate and compare the proton conduction mechanisms of H&lt;sub&gt;2&lt;/sub&gt;Ti&lt;sub&gt;3&lt;/sub&gt;O&lt;sub&gt;7&lt;/sub&gt; and BZYO using density functional theory and molecular dynamics simulations, with the aim of assessing the feasibility of layered H&lt;sub&gt;2&lt;/sub&gt;Ti&lt;sub&gt;3&lt;/sub&gt;O&lt;sub&gt;7&lt;/sub&gt; as a proton-conducting electrolyte for PCFC applications. Electrolyte ; Perovskite (structure) ; Proton ; Ceramic ; Conductivity ; Oxygen ; Hydrogen ; Thermal conduction ; Lattice (music) Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Thermal Expansion and Ionic Conductivity