jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amal Alqahtani
Source: Dose-Response (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Glass-Based Bone-Equivalent Retrospective Dosimeters: A Comprehensive Review of Thermoluminescence and Structural Evolution This paper addresses recent studies on numerous glass materials for retrospective analysis to ascertain radiation dose after catastrophic, large-scale radioactive incidents. These glasses have shown to be versatile retrospective dosimeters in medical and industrial settings, providing great spatial resolution, sensitivity, and water resistance. A review analyzing the retrospective applications of the glass has been conducted, emphasizing the potential of these materials in measuring unintentional radiation exposures. The novelty of this work lies in providing a comparative framework linking thermoluminescence (TL) response, effective atomic number (Z<sub>eff</sub>), dose-response behavior, and radiation-induced structural alterations across multiple glass materials. The relevant scientific research was rigorously assessed to compare thermoluminescence (TL) response, effective atomic number (Z<sub>eff</sub>), dose response, and radiation-induced structural alterations in glass materials. From the review, effective atomic numbers ranged from 9.23 to 15.1, strongly aligning with the Z<sub>eff</sub> of bone, indicating the potential of glass materials as bone-equivalent retrospective dosimeters. The dose-response relationship demonstrates that TL intensity exhibits the most significant correlation with the administered dose and glow curve occurring between 150 to 273 °C, indicating stable charge trapping and recombination processes. Morphological and structural analysis confirmed the dose dependent structural alteration of the glass samples. Despite certain constraints, such as long-term stability, optical bleaching, and dependable dose reconstruction, glasses exhibit potential efficacy as a retrospective dosimeter. This study incorporates thermoluminescence, effective atomic number, and dose-induced structural evolution in a cohesive framework, providing a comprehensive comparative analysis to improve the understanding of glass-based materials as bone-equivalent retrospective dosimeters for post-accidental radiation evaluation. Thermoluminescence ; Dosimeter ; Retrospective cohort study ; Dosimetry ; Medical radiation ; Thermoluminescent dosimeter Luminescence Properties of Advanced Materials ; Radiation Effects and Dosimetry ; Radiation Shielding Materials Analysis
Authors: Rahul Sahu
Source: ChemRxiv (article, 2026-03-04)
Field: Materials Science — Biomaterials

Uncovering non-hydrogen bonded dormant states of perylene diimide monomer through molecular dynamics simulations and Markov state modeling Living supramolecular polymerization often involves manipulating the energy landscape of supramolecular polymerization to slow down thermodynamic processes and enhance kinetic barriers. This control allows for the precise assembly of complex supramolecular polymers. A deep understanding of the structural details and dynamic transitions of kinetically trapped intermediates is crucial for the rational design of supramolecular polymerization processes. Hydrogen bonding within the molecule is highly regarded as the primary contributor to the kinetic stability of monomeric species. The strucrual details of long-lived intermediates that delays the self-assembly are not well-understaood. In this study, we utilized molecular dynamics simulations and Markov state modeling to characterize the metastable states of perylene diimide (PDI) derivatives as a function of alkyl spacers of varying length (ethylene to pentylene chains). Our results demonstrate that these PDI derivatives become trapped in multiple metastable states, each characterized by distinct conformational pathways. All metastable states primarily exhibit intramolecular hydrogen-bonded structures regardless of alkyl spacer length. three states However, we also identied new conformation, lacking intramolecular hydrogen-bond, where the trialkoxybenzamide moiety is either parallel or slanted to the PDI π-plane. While molecules with ethylene, propylene, and pentylene spacers exhibit conformations that shield the π-surface, hindering self-assembly, molecule with butylene spacer adopt conformations that favour selfassembly. Interestingly, structural comparisons reveal similarities between molecules with ethylene and pentylene spacers, as well as between molecules with propylene and butylene spacers. We believe our work provides intruging insights into the dynamics of the kinetic trapped species, contributing to the understanding and advancement of living supramolecular polymerization. Supramolecular chemistry ; Diimide ; Intramolecular force ; Polymerization ; Molecular dynamics ; Perylene ; Monomer ; Supramolecular polymers Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Polydiacetylene-based materials and applications
Authors: Gyeonghoon Ko | Juho Lee
Source: arXiv (Cornell University) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Permutation-Symmetrized Diffusion for Unconditional Molecular Generation Permutation invariance is fundamental in molecular point-cloud generation, yet most diffusion models enforce it indirectly via permutation-equivariant networks on an ordered space. We propose to model diffusion directly on the quotient manifold $\tilde{\calX}=\sR^{d\times N}/S_N$, where all atom permutations are identified. We show that the heat kernel on $\tilde{\calX}$ admits an explicit expression as a sum of Euclidean heat kernels over permutations, which clarifies how diffusion on the quotient differs from ordered-particle diffusion. Training requires a permutation-symmetrized score involving an intractable sum over $S_N$; we derive an expectation form over a posterior on permutations and approximate it using MCMC in permutation space. We evaluate on unconditional 3D molecule generation on QM9 under the EQGAT-Diff protocol, using SemlaFlow-style backbone and treating all variables continuously. The results demonstrate that quotient-based permutation symmetrization is practical and yields competitive generation quality with improved efficiency. Permutation (music) ; Symmetrization ; Quotient ; Kernel (algebra) ; Heat kernel ; Diffusion ; Expression (computer science) ; Generalized permutation matrix Machine Learning in Materials Science ; Advanced Fluorescence Microscopy Techniques ; Surface Chemistry and Catalysis
Authors: Robinson Ferrer | Damla Turgut | Zhongzhou Chen | Shashank Sonkar
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

When Can We Trust LLM Graders? Calibrating Confidence for Automated Assessment Large Language Models (LLMs) show promise for automated grading, but their outputs can be unreliable. Rather than improving grading accuracy directly, we address a complementary problem: \textit{predicting when an LLM grader is likely to be correct}. This enables selective automation where high-confidence predictions are processed automatically while uncertain cases are flagged for human review. We compare three confidence estimation methods (self-reported confidence, self-consistency voting, and token probability) across seven LLMs of varying scale (4B to 120B parameters) on three educational datasets: RiceChem (long-answer chemistry), SciEntsBank, and Beetle (short-answer science). Our experiments reveal that self-reported confidence consistently achieves the best calibration across all conditions (avg ECE 0.166 vs 0.229 for self-consistency). Surprisingly, self-consistency remains 38\% worse despite requiring 5$\times$ the inference cost. Larger models exhibit substantially better calibration though gains vary by dataset and method (e.g., a 28\% ECE reduction for self-reported), with GPT-OSS-120B achieving the best calibration (avg ECE 0.100) and strong discrimination (avg AUC 0.668). We also observe that confidence is strongly top-skewed across methods, creating a ``confidence floor'' that practitioners must account for when setting thresholds. These findings suggest that simply asking LLMs to report their confidence provides a practical approach for identifying reliable grading predictions. Code is available \href{https://github.com/sonkar-lab/llm_grading_calibration}{here}. Grading (engineering) ; Inference ; Calibration ; Confidence interval ; Automation ; Low Confidence Machine Learning in Materials Science ; Computational and Text Analysis Methods ; Topic Modeling
Authors: M. Zucca | Melika Hassanzadeh | Ornella Conti | Valter Giusio | U. Pogliano
Source: Energies (article, 2026-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Extensive Validation of the Three-Branch Model in Simulating the Static and the Dynamic Behavior of EDLC Supercapacitors This study demonstrates the response of the three-branch equivalent circuit model in reproducing the terminal voltage of a supercapacitor that undergoes dynamic galvanostatic pulse trains for frequencies from 0.1 Hz to 100 Hz and signal amplitudes of 1 A, significantly wider than the sinusoidal signals used in impedance spectroscopy. Clean and accurate pulse trains were generated at different frequencies using a transconductance power amplifier, and the voltages at the supercapacitor terminals of three different sizes of supercapacitors were successfully compared with the model. The latter was also utilized to check its ability to accurately reproduce charging and self-discharging processes and the ability to simulate voltammetric cycles. This was verified for six supercapacitor sizes from 1 F (rated 1 mWh, 10 W) up to a large 130 F module (rated 70 Wh, 144 kW). In all cases, the relative difference, with respect to the rated voltage, between the terminal voltage provided by the measurements and that yielded by the model simulations did not exceed 3.5%. This extensive multi-level validation demonstrates how the identified three-branch model based on state equations can provide an accurate electrical design tool and a reference for standards. Supercapacitor ; Voltage ; Electrical impedance ; SIGNAL (programming language) ; Power (physics) ; Equivalent circuit ; Amplitude ; Pulse (music) Supercapacitor Materials and Fabrication ; Fuel Cells and Related Materials ; Smart Materials for Construction
Authors: Guangkui Fan | Xinyu Ji | Hunyue Liao | Bo Meng | Duotao Pan | Jinze Huang | Yang Zhao
Source: International Journal of Molecular Sciences (article, 2026-02-25)
Field: Chemistry — Spectroscopy

MFF-AE: Enhanced Quality Control for Proteomics Mass Spectrometry Data via Multi-Scale Feature Fusion Mass spectrometry (MS) is a core analytical tool in proteomics, and the quality of the generated data directly determines the effectiveness of downstream analyses and the reliability of final research conclusions. While MS is also widely used in other omics applications, this study focuses on label-free quantitative proteomics, where samples are represented as protein-abundance matrices derived from MaxQuant. However, MS data are typically characterized by high dimensionality and substantial noise, posing serious challenges for quality control (QC). Existing QC methods have limited feature extraction capabilities and struggled to capture the key information embedded in the data, resulting in poor performance in identifying anomalous samples. Here, we propose the Multi-Scale Feature Fusion-based Autoencoder (MFF-AE). This deep learning-based anomaly detection model achieves precise identification of anomalous samples by integrating both global and local data features. The model consists of three modules: an autoencoder-based backbone network that efficiently embeds raw data into a low-dimensional semantic space, a local feature extraction and fusion module designed to capture and integrate multi-scale features within MS data, and a sample identification module that enhances discriminative representations to enable accurate anomaly detection. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on a benchmark dataset with synthesized anomalies. Quantitative results on the benchmark dataset show that, compared with 15 baseline models from statistical learning, deep learning, and ensemble learning, our model consistently achieves the best performance across key metrics. Furthermore, through linear relationship analysis on real-world clinical datasets, the exclusion of outlier samples significantly increased the statistical significance and fold change in the identified differential proteins. Overall, the proposed model establishes a solid data foundation, paving the way for downstream mechanistic studies and target discovery. Benchmark (surveying) ; Discriminative model ; Pattern recognition (psychology) ; Anomaly detection ; Outlier ; Feature (linguistics) ; Autoencoder ; Feature extraction ; Identification (biology) Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: P. McPherson | Bernadette McKay | Deborah Lowry | Paul A. McCarron
Source: Journal of Chemical Education (article, 2026-02-26)
Field: Chemistry — Physical and Theoretical Chemistry

Exploring Tablet Dissolution with a Conductivity Probe Dissolution testing of tablets is an important aspect of quality assurance and control in the pharmaceutical industry. Students pursing courses in pharmaceutical chemistry and related areas can be exposed to (inter alia) the Noyes–Whitney model in isolation without accompanying laboratory work. We developed a simple, inquiry-based laboratory activity that uses a conductivity sensor to continuously monitor the dissolution of sodium chloride tablets. Learning goals were written to develop both knowledge and cognitive development in students, with purposeful inclusion of transferable and transversal skills. The laboratory work could be completed within two hours, and students submitted a publication-style short communication for summative assessment of the activity. Student feedback was gathered by a questionnaire, which yielded positive responses. Overall, this simple, low-cost activity incorporates important themes from physical chemistry and pharmaceutics and fosters critical and divergent thinking in students. Summative assessment ; Dissolution ; Pharmaceutics ; Inclusion (mineral) ; General chemistry ; Conductivity ; Work (physics) ; Formative assessment Various Chemistry Research Topics ; Innovative Teaching Methods ; Chemical Safety and Risk Management
Authors: Chengdong Tao | Chuanbao Liu | Yongliang Li | Siwen Qian | Wenmin Han | Feng Wang | Song Zhao | Feifei Ren | Yang Bai | Bo Li | Ji Zhou
Source: Microstructures (article, 2026-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tailoring the optical transfer function of nonlocal metasurfaces for targeted image processing via an automated inverse design framework Nonlocal metasurfaces exhibit significant potential for advanced all-optical image processing by leveraging their exceptional capability to regulate spatial dispersion through precise tailoring of optical transfer functions (OTFs). However, the inverse design of specific OTFs remains challenging due to the inherently complex and highly nonlinear relationship between metasurface structural parameters and angular-dependent optical responses, which conventional empirical trial-and-error approaches struggle to address. To overcome this limitation, we propose an automated inverse design framework integrating a deep neural network acting as a forward predictor with Bayesian optimization. This framework enables automated OTF tailoring by optimizing metasurface structural parameters for targeted image processing operations at desired wavelengths within the 1,200-1,400 nm range. We validate the framework by designing nine dedicated silicon hollow brick metasurfaces: for each operational wavelength (1,250, 1,300, and 1,350 nm), three distinct devices are engineered to separately execute 2D second-order differentiation, 2D fourth-order differentiation, and 2D Gaussian high-pass filtering in transmission mode through targeted OTF engineering. These inversely designed nonlocal metasurfaces achieve a numerical aperture close to 0.4 and serve as fundamental components for edge detection and image sharpening. This intelligent, automated design paradigm dramatically accelerates the design process and significantly expands the scope of achievable functionalities for optical computing metasurfaces, paving the way for more sophisticated all-optical information processing systems. Image processing ; Process (computing) ; Point spread function ; Aperture (computer memory) ; Inverse ; Function (biology) ; Enhanced Data Rates for GSM Evolution ; Transmission (telecommunications) ; Artificial neural network Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: M. M. Hammad | Aasma Riaz | Laraib Chouhdary | Muhammad Kabir | P. R. De los Rios-Escalante
Source: Frontiers in Plant Science (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Genotypic modulation of nanoparticles-induced tolerance in heavy metals-stressed crops; a literature review To address the limited understanding of genotypic responses mediated by nanoparticles under heavy metal stress, this review summarizes research on "Genotypic Modulation of Nanoparticles-Induced Tolerance in Heavy Metals-Stressed Crops". Topics covered include heavy metal toxicity in agriculture, including global prevalence and impacts on crop yield, soil health, and food safety; current mitigation strategies and their limitations; nanoparticles as a novel solution with unique properties and examples; research gaps focusing on physiological effects versus genotypic diversity; and review objectives focusing on NP-induced genotypic modulation and proposing a nano-genomic approach integrating omics tools. The review's objectives were to compare physiological and genotypic crop responses, assess the effects of heavy metal toxicity, benchmark mitigation techniques, describe the functions of nanoparticles, and suggest an integrated nano-genomic framework. Relevant research from around the world was examined, with a focus on multi-omics techniques and the use of nanoparticles in a variety of crops. The results show that while genotypic variation affects tolerance mechanisms mediated by differential gene expression and epigenetic regulation, nanoparticles outperform conventional methods in improving physiological traits, reducing metal uptake, and enhancing antioxidant defenses. Although multi-omics integration clarifies the intricate molecular networks that underlie the reduction of stress caused by nanoparticles, it is still constrained by methodological heterogeneity and insufficient data synthesis. Even though nanoparticle properties and application methods have a significant impact on efficacy, further study is required to ascertain long-term impacts and environmental safety. Despite significant properties and application methods, still it required more study and research for long-term impacts and environmental safety and sustainability. Altogether, it will contribute to novel nano-genomic strategies for constraining sustainable crop resilience against heavy metal contamination. Heavy metals ; Genotype ; Agriculture ; Gene–environment interaction ; Benchmark (surveying) ; Crop Nanoparticles: synthesis and applications ; Plant Stress Responses and Tolerance ; Heavy metals in environment
Authors: Sengsavang Aphayvong | Meika Takagi | Kira Fujihara | Yohane Fujibayashi | Norifumi Fujimura | Hidemasa Yamane | Shuichi Murakami | Takeshi Yoshimura
Source: Microsystems & Nanoengineering (article, 2026-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced electromechanical coupling in piezoelectric MEMS vibration energy harvesters via strain-induced phase transition in Mn-doped bismuth ferrite epitaxial films Mn-doped BiFeO<sub>3</sub> (BFMO) epitaxial films grown on (100) Si wafers delivered enhanced electrical and piezoelectric properties under systematically optimized growth conditions, realized through a biaxial combinatorial sputtering method. The dielectric constant and dielectric loss of the resulting BFMO films were approximately 140 and 1%, respectively, considerably lower than those of undoped BiFeO<sub>3</sub>. Most notably, the effective transverse piezoelectric coefficient was -6.0 C/m<sup>2</sup>, the highest yet reported for this material system. According to detailed structural and electrical characterizations, the improved piezoelectric performance stems from a strain-induced phase transition from the rhombohedral to the monoclinic structure. To demonstrate this enhancement beyond the material level, the optimized films were successfully integrated into piezoelectric MEMS vibration-energy harvesters. The films demonstrated device-level performance improvements with a generalized electromechanical coupling factor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo> <mml:msup><mml:mrow><mml:mi>K</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msup> <mml:mo>)</mml:mo></mml:mrow> </mml:math> of 0.5%, fivefold that of (100) oriented BFO films. Piezoelectricity ; Electromechanical coupling coefficient ; Dielectric ; Piezoelectric coefficient ; Bismuth ferrite ; Wafer ; Microelectromechanical systems ; Sputtering ; Epitaxy ; Monoclinic crystal system Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials