jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mais Khadur | В. Ф. Иванов | Artem Gusenkov | А. А. Гулин | М. Е. Соловьева | Yulia Diakonova | Yulian Khalturin | В. А. Надточенко
Source: Preprints.org (preprint, 2025-09-03)
Field: Chemistry — Inorganic Chemistry

New Insight into the Mechanism of Oxidative Degradation of Artistic White Acrylic Paint Based on Zinc Oxide: Uneven Distribution of Damage in the Paint Layer Under Artificial Aging Conditions Accelerated artificial aging of ZnO PW4 acrylic artist’s paints was carried out for a total of 1963 hours (~8 107 lux.h) with aging assessment at specific intervals. Color change ΔE* &amp;lt; 2 (CIELab-76 system) over 1725 hours of aging, while the human eye notices color change at ΔE* &amp;gt; 2. Oxidative degradation of organic components in the paint to form volatile products was revealed by ATR-FTIR, Raman spectroscopy-microscopy and scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDS). It appears that deep oxidation of organic intermediates and volatilization of organic matter may be responsible for the relatively small value of ΔE* color difference during aging of the samples. To elucidate the degradation pathways, principal component analysis (PCA) was applied to the spectral data, revealing: 1) the catalytic role of ZnO in accelerating photodegradation, 2) the Kolbe photoreaction, 3) the decomposition of the binder to form volatile degradation products, 4) the relative photoinactivity of CaCO3 compared to ZnO, showing slower degradation in areas with higher CaCO3 content compared to those dominated by ZnO. These results provide fundamental insights into formulation-specific degradation processes, offering practical guidance for the development of more durable artist paints and conservation strategies for acrylic artworks. Zinc ; Mechanism (biology) ; Degradation (telecommunications) ; Layer (electronics) ; Distribution (mathematics) ; Oxide ; Materials science ; White (mutation) ; Forensic engineering ; Metallurgy ; Composite material ; Engineering ; Chemistry ; Electrical engineering ; Physics ; Mathematics Pigment Synthesis and Properties ; Color Science and Applications
Authors: E. Luévano-Hipólito | Emireth A Mellado-Lira | Luz I. Ibarra-Rodríguez | Leticia M. Torres-Martínez
Source: Coatings (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Harnessing an Invasive Seaweed: Mechanical Reinforcement of Simonkolleite Coatings Using Brown Algae as a Sustainable Filler Simonkolleite (Zn5(OH)8Cl2·H2O), a layered double hydroxide, is used as a fast dry coating that can be applied onto different surfaces. Due to its rapid crystallization, some problems remain during its application, e.g., crack formation, low hardness, and limited compressive strength. To solve these challenges, we propose the harnessing of brown algae, a natural plague of the Caribbean, as a filler for Simonkolleite coatings. The influence of the addition of brown algae on the structural and morphological properties of the coatings was studied, with particular emphasis on their potential for improved durability and functional performance. The addition of the algae to the coatings favored microstructural compaction, resulting in a denser and mechanically more stable coating that exhibited higher hardness and compressive strength. Also, the presence of chlorophyll in the algae could promote light utilization for other emerging applications. Filler (materials) ; Durability ; Coating ; Compressive strength ; Brown algae ; Algae ; Brown seaweed Layered Double Hydroxides Synthesis and Applications ; Magnesium Oxide Properties and Applications ; Materials Engineering and Processing
Authors: Dr. Anita Sagar
Source: International Journal of Latest Technology in Engineering Management & Applied Science (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Quantum Dot–Based Solar Cells: Advancements, Challenges, and Future Prospects Quantum dot–based solar cells have rapidly grown into one of the most promising candidates in next-generation photovoltaics, largely due to their quantum confinement–driven tunability, strong absorption coefficients, and compatibility with solution-processed fabrication. Their unique optical behaviour, particularly the ability to tailor bandgaps simply by adjusting nanocrystal size, offers a conceptual advantage over conventional bulk semiconductors. Over the past decade, improvements in surface passivation, ligand chemistry, nanocrystal synthesis, and device architecture have enabled efficiencies exceeding 18%, reflecting an impressive rise from early single-digit values (Ning et al., 2022). Yet, despite these encouraging advances, the field continues to face challenges related to long-term stability, environmental toxicity, and large-scale manufacturability. This paper traces the evolution of quantum dot solar cell research, highlighting key breakthroughs in material design, mechanistic understanding, and interfacial engineering, while also discussing the scientific and technological hurdles that must be confronted before widespread commercial adoption becomes feasible. The analysis suggests that continued collaboration between materials scientists, device engineers, chemists, and computational researchers will be crucial for realising the full potential of quantum dot photovoltaics in a sustainable global energy landscape. Quantum dot ; Photovoltaics ; Nanocrystal ; Solar cell ; Solar energy ; Quantum ; Quantum technology ; Photovoltaic system Quantum Dots Synthesis And Properties ; Carbon and Quantum Dots Applications ; TiO2 Photocatalysis and Solar Cells
Authors: Marco Krummenacher | Martin Sommer-Jörgensen | Moritz Gubler | Jonas A. Finkler | Ehsan Rahmatizad Khajehpasha | Giuseppe Fisicaro | Stefan Goedecker
Source: ArXiv.org (preprint, 2025-01-30)
Field: Chemistry — Organic Chemistry

Implications of the multi-minima character of molecular crystal phases onto the free energy In recent years, significant advancements in computational methods have dramatically enhanced the precision in determining the energetic ranking of different phases of molecular crystals. The developments mainly focused on providing accurate dispersion corrected exchange correlation functionals and methods for describing the vibrational entropy contributions to the free energy at finite temperatures. Several molecular crystals phases were recently found to have of multi-minima character. For our investigations we highlight the multi-minima character in the example of the molecular crystal consisting of N-(4-Methylbenzylidene)-4-methylalanine. We explore its potential energy landscape on the full DFT level or with a machine learned potential that was fitted to DFT data. We calculate not only many local minima but also exact barriers along transformation pathways to demonstrate the multi-minima character of our system. Furthermore, we present a framework, based on the quantum superposition method, that includes both configurational and vibrational entropy. As an example, we show for our system that the transition temperature between two of its phases is afflicted by an error of about 200 K if the multi-minima character is not taken into account. This indicates that it is absolutely essential to consider configurational entropy to obtain reliable finite temperature free energy rankings for complex molecular crystals. Maxima and minima ; Character (mathematics) ; Crystal (programming language) ; Energy (signal processing) ; Crystal structure prediction ; Materials science ; Crystallography ; Chemical physics ; Physics ; Chemistry ; Crystal structure ; Computer science ; Geometry ; Mathematics ; Quantum mechanics ; Mathematical analysis Chemical Thermodynamics and Molecular Structure ; Advanced Physical and Chemical Molecular Interactions
Authors: Daniele Crisafulli | Martina Mazzaferro | Francesca Mancuso | Roberto Cirilli | Marco Pierini | Roberto Zagami | Luigi Monsù Scolaro | Neal Hickey | Silvano Geremia | Anna Notti | Melchiorre F. Parisi | Ilenia Pisagatti | Giuseppe Gattuso
Source: Communications Chemistry (article, 2025-12-14)
Field: Chemistry — Organic Chemistry

Enantioselective sensing driven by the interplay of point, helical and planar chirality in pillar[5]arene Pseudo[1]catenanes Chirality plays a central role in molecular recognition, and systems combining multiple, interdependent chiral elements can offer enhanced enantioselectivity. Here we show that covalent tethering of a dihydroxy-pillar[5]arene with meso-2,11-dibromododecanedioate yields diastereomeric pseudo[1]catenanes that combine three stereogenic features: two point chiral centers within the aliphatic linker, a helical chirality (P/M) induced by macrocycle closure, and a planar chirality of the pillararene ring. This system exhibits dynamic stereochemical behaviour involving in/out conformational equilibria, accompanied by inversion of the planar chirality. Using NMR spectroscopy, X-ray diffraction, DFT calculations and circular dichroism, we characterize the structures and dynamics of these macrobicyclic pillararenes, including guest-induced inclusion-exclusion processes. Hydrolysis affords a dicarboxyl-functionalized receptor capable of recognizing diamines via host-to-guest proton transfer, while enantiopure derivatives act as chiroptical sensors able to discriminate between L- and D-lysine esters. These results demonstrate how coupling multiple types of chirality can be exploited for responsive molecular recognition. Chirality (physics) ; Enantiopure drug ; Stereocenter ; Planar chirality ; Axial chirality ; Enantioselective synthesis ; Diastereomer ; Planar Supramolecular Chemistry and Complexes ; Synthesis and Properties of Aromatic Compounds ; Supramolecular Self-Assembly in Materials
Authors: J. R. Barra | Shayan Shahbazi | Anthony Birri | Rajni Chahal | Ibrahim Isah | Muhammad Anwar | Tyler Starkus | Prasanna Balaprakash | Stephen Lam
Source: ChemPhysChem (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich-Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi-empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force-field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r<sup>2</sup> over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods. Molten salt ; Generalizability theory ; Mixing (physics) ; Measure (data warehouse) ; Transfer of learning ; Range (aeronautics) ; Artificial neural network ; Salt (chemistry) ; Field (mathematics) Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Metallurgical Processes and Thermodynamics
Authors: Minghao Cai | Guangzhao Cui | Ruixiang Sun | Yingjie Zhao | L.L. Li | Haoran Yuan | Guolin Li
Source: Microwave and Optical Technology Letters (article, 2025-04-01)
Field: Chemistry — Spectroscopy

Near‐Infrared CO Sensor Based on Small Multi‐Pass Gas Cell and GCRA‐BiLSTM Model in Underground Parking ABSTRACT Due to the influence of automobile exhaust in underground parking lots all year round, a large amount of carbon monoxide (CO) gas accumulates in underground parking lots, endangering the safety of human lives. Based on tunable diode laser absorption spectroscopy (TDLAS) technology, a CO gas detection system is designed to be applied in underground parking lots. A distributed feedback baser (DFB) laser was used to scan the CO absorption peak at 1.566 μm, avoiding interference from other background gases. A small multi‐pass gas cell (MPGC) was designed with a volume of only 704 cm 3 and an effective optical range of up to 26 m. An improved wavelet thresholding denoising‐complete esemble empirical mode decomposition with adaptive noise (IWTD‐CEEMDAN) denoising algorithm was designed to filter out the effects of background noise, thereby improving the signal‐to‐noise ratio (SNR) and lowering the detection limit of the system. A greater cane rat algorithm‐bidirectional long short‐term memory (GCRA‐BiLSTM) concentration inversion algorithm was designed to improve the accuracy of the system concentration inversion. According to the Allan variance, the limit of detection (LoD) of the sensor is 11.57 ppm at 1 s and 0.124 ppm at 117 s. A series of experiments are carried out on the sensor, and the experimental results show that the sensor has good linearity, stability, and LoD, which can realize the real‐time monitoring of underground garage CO, and it has an important application value. Environmental science ; Remote sensing ; Geography Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Gas Sensing Nanomaterials and Sensors
Authors: Magdalena Majdan | Piotr S. Macia̧g | Aneta Rogalska
Source: Frontiers in Pharmacology (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Assessment of embryotoxic effects of quinoline yellow using attention-based convolutional neural network and machine learning in zebrafish model Our daily diet often includes food additives found in numerous processed foods. Growing concerns about the toxicity and potential health risks of synthetic dyes have drawn increased attention from researchers and regulatory authorities. This study examines the embryotoxic effects of Quinoline Yellow (QY), a synthetic dye commonly used as an additive, using both <i>in silico</i> and <i>in vivo</i> models. Computational studies on QY were conducted using QSAR (Quantitative Structure Activity Relations) analysis to identify the major toxicological endpoints. <i>In silico</i> predictions indicated clastogenic and reproductive toxicities, interaction with androgen and estrogen receptors, and an elevated propensity for skin and respiratory allergies. <i>Danio rerio</i> (zebrafish) embryos were exposed to various concentrations of QY (0.005-2 mg⋅mL<sup>-1</sup>) over 48, 72 and 96-h periods. Lethal effects were observed at concentrations above 0.5 mg mL<sup>-1</sup>, with a median lethal concentration LC50 of 0.64 mg mL<sup>-1</sup>. Exposure to QY (0.5-2 mg⋅mL<sup>-1</sup>) resulted in pericardial edema, swollen and necrosed yolk sac, blood stasis and reduced eye size. The study provides direct evidence for the developmental toxicity and teratogenic potential of QY. To enhance the analysis, attention-based Convolutional Neural Networks (CNN) and Transfer Learning (TL) were employed to discern morphological alterations in zebrafish embryos exposed and not exposed to QY. Automating the analysis and classification of zebrafish embryo images diminishes the workload and time burden on biological experts while simultaneously enhancing the reproducibility and objectivity of the classification. The developed neural network further corroborates the evidence suggesting QY's potential toxicity. Zebrafish ; In silico ; Danio ; Toxicity ; Developmental toxicity ; Quantitative structure–activity relationship ; Biology ; In vivo ; Toxicology ; Teratology ; Pharmacology ; Bioinformatics ; Medicine ; Biotechnology ; Internal medicine ; Biochemistry ; Genetics Dye analysis and toxicity
Authors: J H Lin | Oksana Gerlits | Daniel W. Kneller | Kevin L. Weiss | Leighton Coates | Mark A. Hix | Solomon Yamoah Effah | Andrey Kovalevsky | Alice R. Walker | Mark A. Wilson
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-06)
Field: Chemistry — Physical and Theoretical Chemistry

Environmental Contributions to Proton Sharing in Protein Low-Barrier Hydrogen Bonds Hydrogen bonds (H-bonds) are central to biomolecular structure and dynamics. Although H-bonds are typically characterized by well-defined proton positions, proton delocalization can play a key role in facilitating enzyme catalysis and allostery in some systems. Experimentally locating protons is difficult, hampering the study of the proton mobility in H-bonds. We used neutron crystallography and large quantum mechanics/molecular mechanics-Born Oppenheimer molecular dynamics (QM/MM-BOMD) simulations of human DJ-1 and its bacterial homolog YajL to validate atomic resolution X-ray crystallographic bond length analysis, directly visualizing the shared deuteron in a low-barrier hydrogen bond (LBHB) between Glu14-Asp23 in YajL that is a conventional H-bond in DJ-1. In addition, X-ray bond length analysis of protiated and perdeuterated DJ-1 and YajL shows no significant effect of deuteron substitution on these carboxylic acid-carboxylate H-bonds but does reveal an effect at the active site glutamic acid. Residues in the vicinity of Glu14-Asp23 that might favor LBHB formation in YajL were interrogated by mutagenesis of homologous residues in DJ-1. X-ray bond length analysis and QM/MM-BOMD simulations demonstrate that a distal I21T DJ-1 substitution increases proton delocalization in the Glu-Asp H-bond. In addition, simulations show that the extent of proton mobility in the H-bond influences correlated dimer-spanning motions in YajL and DJ-1. In total, we show that mutations within extended H-bonding networks can modulate proton transfer barriers in carboxylic acid-carboxylate H-bonds, allowing proton delocalization to be engineered using combined bioinformatic, structural, and computational information. Proton ; Delocalized electron ; Hydrogen bond ; Deuterium ; Molecular dynamics ; Hydride ; Active site ; Protein dynamics ; Mutagenesis Crystallography and molecular interactions ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Andres C. Arana-Linares | Álvaro Barrera-Ocampo | Arley Camilo Patiño | Yhors Ciro | Constaín H. Salamanca
Source: Polymers (article, 2025-12-17)
Field: Materials Science — Biomaterials

Active Packaging Based on Chitosan, Fish Gelatin, Zein, and Kafirin Biopolymers: A Promising Strategy for Innovation in the Cosmetic Sector Active packaging systems based on biopolymers-either alone or combined with natural bioactive ingredients-offer a viable innovation pathway for the cosmetics industry. These systems support clean-label claims and ecological positioning, representing a strategic opportunity to adapt validated technologies from the food sector to meet emerging cosmetic market demands. Active packaging ; Cosmetics ; Fish <Actinopterygii> ; Food packaging ; Food products ; Food sector Nanocomposite Films for Food Packaging ; Polysaccharides Composition and Applications ; Consumer Packaging Perceptions and Trends
Authors: Zhihong Lu | Kangkang Jia | Haoyang Zhang | Lei Tan | Saritporn Vittayapadung | Lie Deng | Qiang Lyu
Source: Agriculture (article, 2025-11-12)
Field: Chemistry — Analytical Chemistry

Geographical Origin Identification of Citrus Fruits Based on Near-Infrared Spectroscopy Combined with Convolutional Neural Network and Data Augmentation Accurately determining citrus origin is essential for establishing and maintaining regional brands with distinctive qualities while safeguarding the rights and interests of both farmers and consumers. In this study, 2693 navel orange samples were collected from 13 major producing regions in China to establish a comprehensive near-infrared spectroscopy (NIRS) dataset. To address the challenge of citrus origin authentication, this study proposes a novel six-layer one-dimensional convolutional neural network (1D-CNN). The classification accuracy of this model reaches 96.16%. Compared with the support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three-layer 1D-CNNs with kernel sizes of 3 and 16, the accuracy of the proposed six-layer model is improved by 9.65%, 3.21%, 3.84%, and 1.98%, respectively. Furthermore, the dataset is augmented using a Wasserstein Generative Adversarial Network (WGAN) and Noise Addition. The results indicate that data augmentation can effectively improve the accuracy of various algorithm models. Among them, the 1D-CNN proposed in this study achieves the best performance on the Noise Addition-augmented dataset, with its accuracy, precision, recall, and F1-score reaching 98.39%, 0.9843, 0.9839, and 0.9840, respectively. Compared with the other four comparative models, the accuracy of this model is increased by 1.48%, 1.36%, 1.48%, and 2.85%, respectively. Finally, a visual analysis of the 1D-CNN’s feature-extraction process was conducted. The results demonstrate that the 1D-CNN can effectively extract discriminative NIR spectral features to accurately distinguish citrus from different origins and that data augmentation markedly improves model performance by increasing data diversity. This work provides a robust tool for citrus origin tracing and offers a new perspective for the origin authentication of other agricultural products. Discriminative model ; Pattern recognition (psychology) ; Convolutional neural network ; Kernel (algebra) ; Support vector machine ; Citrus × sinensis ; Noise (video) ; Generative adversarial network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Nafees Ibne Alam | Maisha Chowdhury Karabi | Sabiqune Nahar Niti
Source:  (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Review on MXene–Starch Based Packaging Systems for Sustainable and Biodegradable Electronics Review on MXene–Starch Based Packaging Systems for Sustainable and Biodegradable Electronics Abstract To address the growing demand for sustainable and high-performance materials in electronics packaging, starch-based MXene bio-composite films emerge as a promising alternative to conventional synthetic polymers. This work provides a comprehensive review of the integration of MXenes with starch matrices for enhancing functional properties critical to electronics packaging. Improvements in mechanical strength, electrical conductivity, thermal stability, barrier performance, and environmental sustainability are critically analysed. Also, different MXene compositions, and incorporation strategies are compared to identify the most effective approaches. It is observed that starch–MXene films hold significant potential for replacing petroleum-based materials in electronic device packaging. Besides, the interactive effect of MXenes can modify starch films for advanced applications by balancing biodegradability with performance, making them a strong candidate for next-generation sustainable electronics packaging. Electronics ; Electronic systems ; Work (physics) ; Electronic packaging ; Electronic equipment MXene and MAX Phase Materials ; Nanocomposite Films for Food Packaging ; Electromagnetic wave absorption materials