jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yizhi Luo | Qingting Jin | Huazhong Lu | Peng Li | Guangjun Qiu | QI Hai-jun | Bin Li | Xingxing Zhou
Source: Agriculture (article, 2025-01-28)
Field: Chemistry — Analytical Chemistry

Advancing Loquat Total Soluble Solids Content Determination by Near-Infrared Spectroscopy and Explainable AI TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination. Infrared spectroscopy ; Spectroscopy ; Infrared ; Content (measure theory) ; Analytical Chemistry (journal) ; Chemistry ; Near-infrared spectroscopy ; Food science ; Environmental science ; Environmental chemistry ; Mathematics ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Phytochemicals and Antioxidant Activities
Authors: Tsega Y. Melesse
Source: Food and Bioprocess Technology (article, 2025-12-19)
Field: Chemistry — Analytical Chemistry

Intelligent Postharvest Sorting of Bananas Using Thermal Imaging and Deep Neural Network Models Abstract The assessment of postharvest fruit quality under non-invasive and accurate methodologies plays a significant role in enhancing sorting efficiency and loss minimization under precision agriculture. In this study, a deep learning framework is introduced in which a light weight CNN (LW-CNN) and thermal imaging are combined for banana classification under four distinct quality groups: fresh, ripe, overripe, and rotten. A specific set of 4336 thermal images was captured using a FLIR ONE Gen 3 infrared camera operating under thermal-only mode under controlled ambient setups. The set was subjected to balancing and augmentation procedures for better generalization capabilities, and the CNN was trained under such inputs for the identification of thermal signatures corresponding to ripeness. The proposed model demonstrated a high level of accuracy and robust performance in varied measures, marking its ability to distinguish effectively across all categories of quality. Testing under a confusion matrix and a precision-recall curve also supported the effective performance of the classifier under differing confidence thresholds. These results encourage the combination of deep learning and thermal imaging as a feasible, economically viable, and non-invasive real-time postharvest quality evaluation methodology. The proposed methodology forms the basis for smart sorting infrastructure and decision-support systems under data-informed sustainable postharvest management. Postharvest ; Convolutional neural network ; Sorting ; Classifier (UML) ; Artificial neural network ; Confusion matrix ; Hyperspectral imaging ; Thermography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Drying and Modeling
Authors: Abid Zaman | Salhah Hamed Alrefaee | Shirin Shomurotova | Anvar Nurmuhammedov | Salah Knani | Vineet Tirth | Ali Algahtani | Noureddine Elboughdiri
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Unveiling ferroelectric and altermagnetic coexistence in multiferroic HfMnO<sub>3</sub> perovskite Multiferroic materials, which simultaneously exhibit electric and magnetic ordering, have garnered increasing attention due to their potential to revolutionize next-generation spintronic, memory, and multifunctional devices. Their unique ability to couple electric polarization and magnetic states offers low-power operation, electric field control of magnetism, and enhanced device scalability. Among these, altermagnetic multiferroics are particularly promising for enabling spin-polarized transport without stray fields. In this context, we have systematically investigated the structural, electronic, ferroelectric, and optical properties of the trigonal <i>R</i>3<i>c</i>-phase HfMnO<sub>3</sub> using first-principles density functional theory. The compound is found to be thermodynamically and dynamically stable, with a Goldschmidt tolerance factor of 0.88 supporting a distorted perovskite framework. The spin-resolved band structure revealed energy-dependent spin splitting without net magnetization, which is characteristic of altermagnetic behavior. Furthermore, our Berry phase calculations predict a robust spontaneous polarization of approximately 104 μC cm<sup>-2</sup> along the [111] direction, positioning HfMnO<sub>3</sub> as a strong ferroelectric candidate. Remarkably, the polarization-switched state retains both dynamic and electronic stability while preserving the altermagnetic signatures, confirming the multiferroic nature of the material. Optical analyses show high UV absorption, a notable refractive index, and plasmonic features above 7.5 eV, suggesting potential applications in optoelectronics and UV photodetectors. These findings establish HfMnO<sub>3</sub> as a promising altermagnetic multiferroic oxide for multifunctional applications in spintronic and optoelectronic technologies. Multiferroics ; Ferroelectricity ; Spintronics ; Perovskite (structure) ; Electric field ; Polarization (electrochemistry) ; Polarization density Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Magnetic and transport properties of perovskites and related materials
Authors: Jelena Milinković Budinčić | Željana Radonić | Danka Dragojlović | Tea Sedlar | Matija Milković | Marija Polić Pasković | Igor Pasković
Source: Applied Sciences (article, 2025-08-24)
Field: Materials Science — Biomaterials

Exuviae of Tenebrio molitor Larvae as a Source of Chitosan: Characterisation and Possible Applications Biopolymers have gained significant attention due to their environmental advantages, with insects emerging as a promising but underutilized source of chitin and chitosan. In this study, chitosan was extracted from the larval exuviae of Tenebrio molitor through sequential demineralization, deproteinization, and deacetylation steps. For selected analyses, the extracted chitosan was further purified via reprecipitation from an acid solution using a basic precipitant (1 M NaOH). Chitosan was then characterized using chemical and instrumental methods. The results indicated that the chitosan had a medium degree of deacetylation (72.27%) and viscosity-average molecular weight (612 kDa), along with minimal ash (0.33%) and amino acid (0.14%) content, suggesting high product quality. FTIR analysis identified characteristic functional groups present, and SEM analysis highlighted a fibrous and porous microstructure in the purified chitosan. The prepared films exhibited favorable properties, including low thickness (0.0197 mm), high swelling degree (335.07%), moderate water solubility (46.99%), and moisture content of 32.39%, supporting their practical applicability. T. molitor exuviae thus represents a sustainable and environmentally friendly source of high-quality chitosan, with beneficial structural and functional properties, supporting its use in a wide array of value-added applications. Larva ; Chitosan ; Zoology ; Chemistry ; Biology ; Ecology ; Biochemistry Nanocomposite Films for Food Packaging ; Insect Utilization and Effects ; Pineapple and bromelain studies
Authors: Kenrick Kai-yuen Chan | Jimmy Ka-Wai Cheung | Sai‐Ho Chung | Hang‐Kin Kong | Jingfang Bian | Lei Zhou | Chi Wai | Thomas Chuen Lam
Source: Scientific Data (article, 2025-05-23)
Field: Chemistry — Spectroscopy

A Comprehensive Proteome of Human Corneal Epithelial Cells Constructed by Cross-platform DIA-Mass Spectrometry The corneal epithelium serves as the front barrier against environmental stimuli and pathogens on the ocular surface. A comprehensive protein profile of the corneal epithelium would be crucial for understanding the molecular mechanisms that are related to corneal disease. This work demonstrated a library-free data-independent acquisition (DIA) approach across different mass spectrometers and proteomic software to build a comprehensive proteomic dataset for human corneal epithelial cells (HCECs). With the combinational use of different data-independent acquisition technologies of multiple mass spectrometers, including Sciex ZenoTOF 7600 (DIA-SWATH), Bruker TimsTOF Pro2 (DIA-PASEF), and ThermoFisher Orbitrap Fusion Lumos (DIA-HRMS1), protein identification and quantification were performed with superior sensitivity and resolution. By using a library-free DIA approach, this study constructed a more diverse and unbiased proteomic profile of human corneal epithelial cells (HCECs), comprising 11,954 protein groups (1% FDR). This represents the largest corneal proteome reported to date. All raw proteomic data were deposited to ProteomeXchange Consortium via Proteomics Identifications database (PRIDE) with the dataset identifier accession number PXD059451. Our findings hold the potential to enhance future understanding of corneal pathologies and transformative therapeutics. Proteome ; Mass spectrometry ; Computational biology ; Proteomics ; Chemistry ; Biology ; Chromatography ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Glycation End Products research
Authors: Suresh Choppadandi | Divya Vemula | Nagaraju Kerru | Vasundhra Bhandari | Chilukoti Srilakshmi
Source: ChemistrySelect (article, 2025-04-30)
Field: Chemistry — Organic Chemistry

Design, Synthesis, and Antibacterial Evaluation of Pyrazole Pyrimidine Hybrids: Integrative In Vitro and In Silico Studies Abstract Several novel pyrimidine–pyrazole hybrids were synthesized and evaluated for their antimicrobial potential against diverse bacterial pathogens, focusing on targeting penicillin‐binding protein 3 (PBP3), a key enzyme in bacterial cell wall synthesis. Among the tested derivatives, seven derivatives exhibited significant antibacterial activity against E. faecium at 50–200 µg/mL with the cyclopentane‐substituted compound ( 6d ) showing the highest activity (50 µg/mL). Notably, the 3‐fluoro,5‐trifluoromethyl compound ( 6a ) demonstrated broad‐spectrum activity (200 µg/mL) against S. aureus , E. faecium, A. baumannii , and K. pneumoniae . Docking studies revealed strong binding affinity with HMG‐CoA synthase, an essential enzyme in bacterial metabolism. In silico pharmacokinetic predictions indicated favourable safety profiles and drug‐likeness. These results suggest pyrimidine–pyrazole hybrids as promising antibacterial agents targeting PBP3, warranting further investigation through enzymatic inhibition assays and in vivo studies. In silico ; Pyrazole ; Pyrimidine ; In vitro ; Hybrid ; Combinatorial chemistry ; Chemistry ; Biology ; Computational biology ; Stereochemistry ; Biochemistry ; Botany Synthesis and biological activity ; Cancer therapeutics and mechanisms ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Zhenfa Yang | Xiaoping Lu | Lucheng Chen
Source: Frontiers in Chemistry (article, 2025-02-05)
Field: Chemistry — Analytical Chemistry

Discriminating the adulteration of varieties and misrepresentation of vintages of Pu’er tea based on Fourier transform near infrared diffuse reflectance spectroscopy In the Pu'er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu'er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu'er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu'er tea quality inspection, and offering a robust method for combating the pervasive issues within the market. Misrepresentation ; Vintage ; Diffuse reflectance infrared fourier transform ; Fourier transform infrared spectroscopy ; Fourier transform ; Mathematics ; Biological system ; Computer science ; Pattern recognition (psychology) ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Heavy Metals in Plants
Authors: Yuncan Pan | Xin Liu | Jianyao Yao
Source: Materials (article, 2025-05-05)
Field: Materials Science — Ceramics and Composites

Prediction of Mechanical Properties of Void Defect-Containing Cf/SiBCN Ceramic Matrix Composite Based on a Multiscale Analysis Approach Carbon fiber-reinforced SiBCN ceramic matrix composite (Cf/SiBCN CMC) is emerging as a promising candidate for advanced thermal protection systems, owing to its superior thermal stability and notable ablation resistance. In this study, the mechanical properties of Cf/SiBCN CMC are investigated theoretically and experimentally. Utilizing a multiscale approach, a representative volume element (RVE) is developed to predict mechanical properties based on detailed microstructural characterization. The predictions derived from the RVE demonstrate agreement with the experimental findings. The experimental results show dispersion in the mechanical properties of Cf/SiBCN CMC. To investigate whether the dispersion of mechanical properties is associated with defects, this study examines the impact of the location, content, and size of defects on the mechanical properties of the Cf/SiBCN CMC. The analysis reveals that the location, content, and size of defect all impact the mechanical properties of the Cf/SiBCN CMC, with overall porosity having the most significant effect. When the porosity is constant, variations in defect location and size also contribute to the observed variability in the mechanical performance of the Cf/SiBCN CMC. Materials science ; Composite material ; Porosity ; Ceramic ; Composite number ; Void (composites) ; Ceramic matrix composite ; Dispersion (optics) Advanced ceramic materials synthesis ; Advanced materials and composites ; Thermal properties of materials
Authors: Weixin Wang | Mingzhu Ma | Yuting Song | Yujie Ma | Chong Yan | Zhongliao Wang | Dongwei Ma | Xin Wang | Xuebin Zhu
Source: Small (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

External Electric Field Enhanced Ti<sub>3</sub>C<sub>2</sub> MXene Surface Passivation for Realizing Ultra‐Long Cycling Stability External electric field (EEF), as a stimulating factor, is an effective method for optimizing the surface composition and structure of materials. Ti<sub>3</sub>C<sub>2</sub> MXene surface enriched with negatively charged functional groups (─OH, ─O, etc.) will exhibit high sensitivity to EEF. However, the impact of EEF on the interaction mechanisms between the guest ions and MXene surface remains unclear and requires further investigation. Herein, the density functional theory (DFT) is employed to simulate the adsorption energies between butyl trimethylammonium ion (BTA<sup>+</sup>) and MXene surfaces under different intensities of EEFs (±0.9, ±0.7, ±0.5, ±0.3, ±0.1, and 0 V Å<sup>-1</sup>), indicating EEF can effectively regulate adsorption. It will increase the encapsulation degree of BTA<sup>+</sup> on the MXene surface, thereby enhancing surface passivation. Based on theoretical predictions, quaternary-ammonium ions with different chain-lengths (BTA<sup>+</sup>, DTA<sup>+</sup>, STA<sup>+</sup>) are selected as guest ions to unveil the mechanism of EEF on MXene surface passivation. The applied-EEF promotes the formation of Ti─O─N bonds between ─OH and ammonium groups to construct more-denser protective layer, leading to enhancement of surface passivation and obviously increasing the capacitance retention after 100,000 cycles (50.8% to 97.5%). This work provides a new pathway and theoretical support for the surface passivation of MXene. Passivation ; Materials science ; Adsorption ; Ion ; Density functional theory ; Surface modification ; Electric field ; Chemical engineering ; Nanotechnology ; Layer (electronics) ; Physical chemistry ; Chemistry ; Computational chemistry ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Kyle J. Colston | Santiago C. Monsalve | Severin T. Schneebeli
Source: ChemRxiv (preprint, 2025-03-20)
Field: Materials Science — Materials Chemistry

Structure-Based Modeling of Environment-dependent Protonation States Across LNP Formulations with Atomistic CpHMD The pKa values and associated protonation states of ionizable lipids in lipid nanoparticle (LNP) formulations are strongly dependent on their chemical environment. This phenomenon leads to poorly understood structure-function relationships that influence payload delivery, tissue-selective biodistribution, and manufacturing. For example, the charge- and biodistribution of an mRNA-loaded LNP can vary based on the type of ionizable lipid used, the molar ratio of its components, and its cargo. Yet, the spatial resolution of experimental protonation state measurements is currently limited to the apparent charge of an ionizable lipid averaged over all environments/conformations of an LNP — best represented by its apparent pKa value. Such measurements are too coarse to capture the heterogenous charge distributions of ionizable lipids in LNPs, which influence biocorona formation and interactions with the payload. Similar limitations are inherent to classical fixed protonation-state in silico models that cannot capture the environment-dependent protonation states and pKa values determining local pKa. To address this gap in experimental and computational tools available to accurately determine the local charge distributions in LNPs, this work now incorporates a scalable continuous constant pH molecular dynamics (CpHMD) model to simulate the self-assembly processes of five reported distinct LNP formulations. Parameters for ionizable lipids were generated from fitting fixed lambda-state calculations performed with Hamiltonian replica exchange (HREX) to improve conformational sampling during parameterization. Simulated systems were composed of 100 ionizable lipids (50 mol%), cholesterol (40 mol%), distearoylphosphatidylcholine (10 mol%), and mRNA (20 nucleotides) to model the interior of an LNP. Self-assembly was simulated for 100 ns at different pH values to validate the apparent pKa for each system. The theoretically calculated apparent pKa values matched reasonably well with those measured experimentally (mean absolute error = 0.5 pKa units), and all systems exhibited pH-dependent structures. Overall, this work provides a new computational platform technology to (i) predict the pKa values of ionizable lipids in different chemical environments and (ii) enable a structure-based way to model the heterogeneous, environment-dependent charge distributions of ionizable lipids in LNP systems typically encountered during LNP manufacturing and delivery. Protonation ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Chemical Physics Studies
Authors: Cosmin Nicolescu | Tudor Viorel Țigănescu | Aurora Antoniac | Ovidiu Iorga | Brândușa Ghiban | Alexandru Pascu | Alexandru Streza | Iulian Antoniac
Source: Crystals (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Mechanical Properties and Ballistic Performance for Different Coatings on HARDOX 450 Steel for Defense Applications The aim of the current study is to investigate the mechanical properties and ballistic performance of HARDOX 450 steel for defense applications in different conditions: uncoated, alumina-coated, and LINE X polyurea-coated. Tensile tests and Vickers microhardness measurements were conducted, along with fracture surface analysis using stereomicroscopy, scanning electron microscopy, and computed tomography. Experimental results showed that uncoated HARDOX 450 steel exhibited the highest strength and hardness, with ductile fracture features. Polyurea-coated HARDOX 450 steel samples retained good mechanical properties and demonstrated effective ballistic protection, including the containment of fragments. In contrast, alumina-coated HARDOX 450 steel samples exhibited reduced strength and ballistic resistance, attributed to the microstructural changes in HARDOX 450 steel caused by the high-temperature deposition process of alumina. Numerical simulations performed with the 5.56 × 45 mm bullet used in the simulation, along with its ballistic impact interaction with the Hardox 450 target model, aligned well with experimental ballistic impact results for all the samples. Overall, LINE X polyurea coating on HARDOX 450 steel proved to be the more suitable coating for applications requiring a balance of mechanical strength and ballistic impact resistance. Materials science ; Composite material High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Ion-surface interactions and analysis