jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher A. Piske | Priscilla G. Leite | Mónia A. R. Martins | Olga Ferreira | João A. P. Coutinho | Dinis O. Abranches | Simão P. Pinho
Source: Journal of Solution Chemistry (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Meta-analysis on the Salt Effect on Glycine Solubility Applying Gaussian Processes Abstract In aqueous solutions containing electrolytes, ions influence both the solubility and the stability of biomolecules. However, inconsistencies across published data highlight the need for a critical review. To address this, a database was constructed on the solubility of glycine in electrolyte solutions spanning from 1996 to 2024, and the experimental data were critically evaluated. Gaussian Process (GP) models were implemented to analyze, predict, and validate solubility behavior. The GP model successfully captures salting-in and salting-out trends, along with specific ion effects reported in the literature. It also provides predictive uncertainty estimates that help identify potentially inconsistent data points or sets. This uncertainty-based analysis enables the reconciliation of conflicting datasets and helps prioritize new experimental measurements in regions where data are sparse or less reliable. By applying a data-filtering method that removes experimental points falling outside the uncertainty range of the model, the influence of inconsistent values is reduced. This results in a more robust model fit and improved prediction accuracy. Therefore, the GP establishes a quantitative foundation for consolidating the current knowledge on the solubility of glycine in saline solutions, identifying methodological inconsistencies in the literature. Solubility ; Aqueous solution ; Salt (chemistry) ; Experimental data ; Gaussian ; Stability (learning theory) ; Electrolyte ; Glycine Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions ; Spectroscopy and Quantum Chemical Studies
Authors: Xintong Chen | Laura Schmitz | Mark Geppert | Sabine Hofer | Norbert Hofstätter | Martin Himly | Litty Johnson
Source: Nanomedicine (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Lessons learned from allergen adsorption on engineered nanomaterials: bridging experimental insights and computational technologies Allergic disease has increased over past decades, coinciding with the use of nanomaterials in medicine, industry, and environmental applications. This trend introduces new opportunities for human exposure to allergen-nanomaterial complexes through different routes like inhalation, ingestion, and dermal exposure. Such interactions have emerged as a determinant influencing immune response. Recent studies reveal that allergen adsorption onto nanomaterial surfaces varies with shape, particle size, surface chemistry, morphology, and agglomeration state. These surface interactions can modify allergen structure, epitope accessibility, and proteolytic stability, thereby reshaping the immune outcomes. Key considerations including dose, exposure route, and kinetics play an important role in determining physiological effects and pathological consequences. This special report provides a comprehensive overview of current research on allergen-nanomaterial interactions, summarizing known mechanisms, structural and immunological consequences, and critically assessing the challenges, gaps, and controversies. Understanding the impact of allergen-nanomaterial interactions on the human immune response demands a cross-disciplinary approach that integrates the complexities in nanotechnology and immunology. Advances in molecular toxicology employing new approach methodologies provide mechanistic insight which can support regulatory science and improve the acceptance of innovative therapeutic strategies in the field of nanomedicine. Allergen-nanomaterial interactions may serve as a model for best practice in this context. Bridging (networking) ; Immune system ; Allergen ; Nanotoxicology ; Human disease Nanoparticles: synthesis and applications ; Inhalation and Respiratory Drug Delivery ; Air Quality and Health Impacts
Authors: Koyuru Nakayama | Keita Sakakibara
Source: Oleoscience (article, 2026-01-01)
Field: Materials Science — Biomaterials

Advancing Digital Transformation in Cellulose Nanofiber (CNF) Research: Acquisition of CNF Morphological Information and Its Application to Composite Material Design セルロースナノファイバー（CNF）は形態分布の多様性が大きく，平均量のみでは複合材料物性を十分に説明できない。とくに機械解繊CNFでは，粗大画分と微細画分の混在が物性ばらつきの要因となる。本稿では，透過光沈降プロファイリングと機械学習を組み合わせ，沈降挙動から比表面積（SSA）および形態分布情報を非破壊・迅速に推定するデータ駆動型解析手法を概説する。沈降プロファイルを速度指標および時空間ヒートマップとして表現し，勾配ブースティング回帰や畳み込みニューラルネットワークによりSSAを高精度に予測した。さらに沈降ヒートマップを用いてアスペクト比分布を推定し，粗大／微細画分に区分した形態指標とIRスペクトル由来の化学情報を統合することで，ポリプロピレン（PP）/CNF複合材料の衝撃特性を定量的に説明可能であることを示した。本アプローチは，形態分布を扱う複合材料設計を可能にするとともに，品質管理やプロセス開発に資するマテリアルDX基盤として有効である。 Nanofiber ; Cellulose ; Composite number ; Transformation (genetics) ; Material Design Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Electrospun Nanofibers in Biomedical Applications
Authors: Christopher P. Anderson | Stefania Castelletto
Source: MRS Bulletin (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Engineering and materials design for robust optically active spin qubits Abstract Optically active spin qubits are promising platforms for applications such as quantum sensing, quantum computing, and quantum communication. However, in many cases, materials challenges remain major hurdles for realizing these quantum technologies. These challenges include the effects of the host material, surfaces, and device integration, which can both undermine intrinsic ideal quantum properties, but also provide necessary engineering controls. In this issue, materials opportunities and challenges that impact the properties of spin qubits interfaced with light are discussed. The material platforms considered are diamond, silicon, silicon carbide, 2D materials and molecular systems. Both fundamental, engineering, and computational design aspects are considered. Such multifaceted, exploratory, and materials-centric advances are required to unlock the potential of these quantum systems. Graphical abstract Qubit ; Quantum technology ; Quantum ; Quantum computer ; Spin (aerodynamics) ; Quantum sensor ; Spin engineering ; Quantum network Diamond and Carbon-based Materials Research ; Graphene research and applications ; Quantum and electron transport phenomena
Authors: Nor Afiqah Nor Asri | Y W Fen | Nurul Illya Muhamad Fauzi | Nur Aqilah Kamaruzzaman | Rahayu Emilia Mohamed Khaidir | Hazwani Suhaila Hashim | Muhammad Fahmi Anuar | Muhammad Amir Zakwan Mohd Zailani | Ahmad Danish Iskandar Mohd Fadzil | Nur Nadia Amira Mahamad Basari | Mazliana Ahmad Kamarudin | H Abdullah
Source: Scientific Reports (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Mango peels-assisted synthesis of carbon quantum dots for potential optical sensing of diazinon Recently, carbon quantum dots (CQDs) have received widespread attention for their attractive properties and potential in sensing applications; however, their production often uses harmful materials and high energy. In this study, CQDs were acquired from mango peels using green hydrothermal method at 200 °C for 3, 6, 9, 12, and 15 h, using water as the solvent. The optical behavior of CQDs with different time synthesis, indicating photoluminescence (PL) emissions wavelength (441–447 nm), varying absorbance (0.80–0.99), and slight changes in optical bandgap (3.935–3.825 eV), showing synthesis time influences optical behavior. The CQDs with 3 h synthesis time were chosen to undergo structural characterization due to the most left shifted in PL emission, indicating the smallest particle size. Transmission electron microscopy analyzed that the CQDs were monodispersed with the average particle size was 3.54 nm, while energy dispersive X-ray results exhibited high carbon content of 97%. Fourier transform infrared analysis proves the formation of CQDs nanoparticles by the existence of hydroxyl, carbonyl, and carboxyl functional groups. Atomic force microscopy confirmed a root mean square roughness increased from 0.71 to 1.02 nm, indicating CQDs attachment, and the gold-CQDs thin film was later used as the surface plasmon resonance (SPR) sensing layer. The develop gold-CQDs thin film-based SPR sensor was successfully tested in diazinon concentration range from 0 to 100 nM, with a limit of detection as low as 0.01 nM and sensitivity of 0.0153º nM-1. These results indicate that the potential of mango peels-derived CQDs as sustainable nanomaterials for optical sensing applications, particularly in environmental monitoring using SPR-based technology. Absorbance ; Photoluminescence ; Surface plasmon resonance ; Transmission electron microscopy ; Quantum dot ; Nanomaterials ; Band gap ; Surface roughness ; Fourier transform infrared spectroscopy Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Supercapacitor Materials and Fabrication
Authors: Wei Gao | Jiangshan Zhao | Wei Qi | Zhaohui Huang | Guofeng Liu | Chuanqi Feng | Chao Sang | Xiujuan Wang | Xiaolei Zhang
Source: Polymers (article, 2026-01-28)
Field: Materials Science — Polymers and Plastics

Preparation of Low-Surface-Energy SSBR@FA Hybrid Fillers via Solution Mechanochemical Approach and Its Enhancement in Mechanical Strength on the Modified FA/SBR Composites Owing to the substantial polarity difference and weak interfacial interaction, the large-scale application of fly ash (FA) in rubber materials still faces substantial challenges. To solve this issue, this study prepared a modified hybrid SSBR@FA filler through a solution mechanochemical reaction between solution-polymerized styrene-butadiene rubber (SSBR) and FA in a lab planetary ball mill. Fourier transform infrared spectroscopy (FTIR) and energy-dispersive spectroscopy (EDS) analyses demonstrated the in situ grafting-neutralization between the carboxyl in the SSBR chains and metal oxides in FA. Transmission electron microscopy (TEM) showed that surface-grafted SSBR formed a rubber-constrained layer on FA particle surfaces, which can reduce their surface energy and improve the wettability between FA and SBR matrix. Compared with the SBR vulcanizate, the mechanical properties, thermal conductivity, and flame-retardant properties of the SBR/SSBR@FA vulcanizates were obviously improved. This was because of the uniform distribution of FA and the improved interfacial interaction between FA and the rubber matrix. For example, the tensile strength, tear strength, and elongation at break increased by 66.3%, 52.9%, and 17.7%, respectively. This easy, efficient, and environmentally modified method for FA was expected offer a practical and creative solution for its application in rubber manufacturing. Natural rubber ; Ultimate tensile strength ; Fourier transform infrared spectroscopy ; Elongation ; Vulcanization ; Wetting ; Tear resistance ; Thermal stability ; Mechanochemistry Polymer Nanocomposites and Properties ; Flame retardant materials and properties ; Tribology and Wear Analysis
Authors: Sushindh Anandan | Jayasudha M
Source: Frontiers in Physics (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Transient search driven random forest model for predicting diluted heavy crude oil viscosity Viscosity is a basic thermophysical property that determines the flow characteristics of single phase oil such as crude oil. Accurate estimation of diluted heavy crude oil viscosity is important for laboratory characterization and operational handling of heavy oils. In this research, a ML based approach is presented for estimating the viscosity of diluted heavy crude oil samples measured in laboratory conditions. A total of 245 experimental datasets were collected using a Brookfield DV2T viscometer. In this experiment, heavy crude oil was mixed with lighter oil at different dilution rates and temperatures. The major input parameters considered are heavy crude oil viscosity, lighter oil viscosity, dilution rate and temperature. To improve the performance of the model, Min-Max normalization was used for data scaling and Kernel Principal Component Analysis (KPCA) was used for nonlinear feature extraction. A Transient Search driven Random Forest Regression (TS-RFR) model was introduced to optimize hyperparameters and enhance predictive performance. The performance of the proposed model was assessed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination ( R 2 ) and Percentage of Accuracy-Precision (PAP). The comparative analysis with the existing ML models, such as MLP-ANN, SVR and LightGBM, reveals that the proposed TS-RFR model performs better with RMSE = 0.2976, MAE = 0.1005, MSE = 0.1015, R 2 = 97.35% and PAP = 92.35%. The analysis clearly shows that the proposed model is a reliable and efficient tool for estimation of diluted crude oil viscosity. Mean squared error ; Normalization (sociology) ; Dilution ; Correlation coefficient ; Approximation error ; Principal component analysis ; Crude oil ; Coefficient of determination Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Oil Spill Detection and Mitigation
Authors: Adithya Garimella | Firoz Alam Faroque | S. S. Ghosh | Samarendra Nath Ghosh
Source: International Journal of Biomaterials (article, 2026-01-01)
Field: Materials Science — Biomaterials

Nanocomposite Bone Scaffolds Based on Magnesium Alloy: A Detailed Investigation of Their In‐Vitro Biodegradation Performance Polymeric biomaterials and their composites have been extensively explored for orthopaedic applications; however, their inadequate mechanical performance significantly restricts their use in load-bearing environments. Metallic biomaterials, by contrast, offer superior mechanical strength and structural stability. Among them, magnesium (Mg) has emerged as a particularly attractive candidate for temporary orthopaedic implants owing to its elastic modulus and density being close to those of natural bone, thereby minimising stress shielding. In addition, Mg inherently fulfils two critical requirements for orthopaedic implants-biocompatibility and biodegradability. In this study, a bioactive magnesium-alloy-based nanocomposite scaffold was engineered to overcome the limitations of conventional biomaterials while closely replicating the porous microarchitecture of human bone. A novel bioactive glass-ceramic, nano-fluorcanasite (n-FC), was incorporated into the Mg-alloy matrix to enhance osteogenic activity and accelerate bone tissue regeneration. The introduction of an interconnected porous structure was designed to promote efficient nutrient diffusion, facilitate metabolic waste removal and reduce implant density. Furthermore, the controlled addition of selected alloying elements in specific weight fractions effectively moderated the degradation kinetics of the Mg-based scaffold. The nanocomposite scaffolds were fabricated using a powder metallurgy route followed by sintering. Tailored porosity was achieved through the controlled incorporation of carbamide particles as a space-holding agent. The in vitro degradation behaviour of the scaffolds was systematically evaluated using a weight-change method after immersion in phosphate-buffered saline (PBS) for predetermined durations. The results demonstrate that, compared with unalloyed magnesium, the degradation rate of the nanocomposite scaffolds can be precisely and consistently regulated, highlighting their potential as mechanically competent, bioactive and biodegradable candidates for orthopaedic implant applications. Nanocomposite ; Porosity ; Scaffold ; Biodegradation ; Magnesium ; Biocompatibility ; Degradation (telecommunications) ; Modulus ; Elastic modulus Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Magnesium Oxide Properties and Applications
Authors: Usman Tariq | Bilal Shabbir | Fahad Saeed
Source: Scientific Reports (article, 2026-03-13)
Field: Chemistry — Spectroscopy

End-to-end deep attention-based multitask pipeline for predicting uncertainty-quantified peptide properties from mass spectrometry data Mass-based filtering significantly reduces the peptide candidate pool for subsequent scoring in database search algorithms. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides – potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra 77.8% of the time. Integrating ProteoRift into an end-to-end pipeline significantly reduces the search space compared to mass-only filtering. This delivers 8x to 12x speedups while maintaining peptide deduction accuracy comparable to established algorithmic techniques. We also developed uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against the correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end pipeline available at https://github.com/pcdslab/ProteoRift. Pipeline (software) ; Peptide ; Mass spectrometry ; Proteomics ; Isobaric labeling Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Mason Hooten | Meenakshi Dutt
Source: Molecular Systems Design & Engineering (article, 2026-01-01)
Field: Materials Science — Biomaterials

Self-assembly of phenylalanine oligopeptides: development of transferable bottom-up coarse-grained potentials Self-assembled nanostructures comprising phenylalanine-based oligopeptides are widely utilized in various applications across the domains of electronics, personal care, and biomedicine. Phenylalanine ; Oligopeptide ; Peptide ; Amino acid Supramolecular Self-Assembly in Materials ; Chemical Synthesis and Analysis ; Polydiacetylene-based materials and applications
Authors: Sumeet Malik | Waqas Ahmad | Adnan A. Khan | Gul Rahman | Sabir Khan | Maria Del Pilar Taboada Sotomayor | Thiago Machado da Silva Acioly | Mohd Abul Hasan | Alibek Ydyrys | Muhammad Ilyas
Source: Scientific Reports (article, 2026-02-11)
Field: Chemistry — Analytical Chemistry

Fabrication of an electrochemical sensor based on magnetic molecularly imprinted polymer for detection of sunset yellow dye Sunset Yellow (SY) is a widely used synthetic azo dye in the food, pharmaceutical, and cosmetic industries, but its excessive release poses serious health and environmental risks. In this study, a magnetic molecularly imprinted polymer (MMIP)-based electrochemical sensor was developed for the selective detection of SY using 1-vinylpyridine as the functional monomer. Scanning electron microscopy (SEM) revealed irregular particle morphologies with an average diameter of approximately 69 nm, attributed to surface cavities formed during the imprinting process. Batch sorption experiments confirmed the high specificity of the MMIPs, with a maximum sorption capacity of 80 mg g− 1 under optimal conditions (pH 2, sorbent dosage 2 mg, contact time 18 min). Sorption kinetics followed a pseudo-second-order model, and adsorption behavior was best described by the Langmuir isotherm, indicating monolayer adsorption. Electrochemical measurements demonstrated that the fabricated sensor exhibited high sensitivity, selectivity, and stability for SY detection. The sensor performed optimally at pH 7, an accumulation time of 60 s, and a concentration of 1.5 × 10⁻³ M, with recovery values of 72.9–99.3% in real water and beverage samples, highlighting its practical applicability for environmental and food analysis. The LOD and LOQ values were found to be 5.82 × 10− 5 M and 1.76 × 10− 4 M, respectively. These results confirm the effectiveness of MMIP-based platforms for rapid, accurate, and selective monitoring of synthetic dyes in complex matrices. Sorption ; Monolayer ; Molecularly imprinted polymer ; Electrochemical gas sensor ; Sorbent ; Adsorption ; Molecular imprinting ; Electrochemistry Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Enzyme-mediated dye degradation
Authors: Hao Pan | Pengju Zhang | Chen Xue | Dongdong Liu
Source: Processes (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Interpretable Data-Driven Crystal Diameter Prediction in CZ Silicon Single-Crystal Growth via MIC-Guided and GWO-Optimized TCN–LSTM This study proposes a data-driven framework with post hoc interpretability analysis for one-step-ahead crystal diameter prediction in the Czochralski (CZ) silicon single-crystal growth process. To address the strong multivariable coupling, nonlinear dynamics, variable-specific delays, and difficulty of online measurement in CZ growth, the maximal information coefficient (MIC) was first used to screen key auxiliary variables from industrial process data. The Grey Wolf Optimizer (GWO) was then employed for multi-variable delay estimation and feature alignment, and a hybrid temporal convolutional network (TCN)–long short-term memory (LSTM) model was constructed to combine local temporal feature extraction with long-term dependency learning. Four input configurations were designed according to whether lag alignment and diameter history were included, and the proposed TCN-LSTM was systematically compared with standalone TCN and LSTM models. The results show that both diameter history and delay alignment improve prediction performance. Under the current single-run evaluation protocol, the TCN-LSTM configurations yielded lower prediction errors than the corresponding TCN and LSTM models under the same input settings. Under the withlag-withY configuration, the TCN-LSTM model achieved MSE = 0.00259, RMSE = 0.05087, MAE = 0.03949, and R2 = 0.96982. After GWO-based hyperparameter optimization, the best TCN-LSTM configuration further improved to MSE = 0.00239, RMSE = 0.04894, MAE = 0.03651, and R2 = 0.97207. SHAP-based analysis was further used to provide a post hoc interpretation of the relative contributions of key process variables to diameter variation. Overall, the proposed framework provides a data-driven prediction approach and may support subsequent process analysis and optimization in industrial CZ growth. Interpretability ; Mean squared error ; Feature (linguistics) ; Hyperparameter ; Process (computing) ; Pattern recognition (psychology) ; Key (lock) Machine Learning in Materials Science ; Fault Detection and Control Systems ; Iron and Steelmaking Processes