jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carlota Ucha | A. Val del Río | Alba Pedrouso | A. Mosquera-Corral | D.P. Mesquita
Source: Journal of environmental chemical engineering (article, 2026-02-05)
Field: Materials Science — Biomaterials

Comparative assessment of two bioproducts using quantitative image analysis Polyhydroxyalkanoates (PHA) and triacylglycerides (TAG) could be used as bioplastic and precursors of biodiesel, respectively. To ensure a more controlled and efficient operation of the reactor, rapid monitoring of bioproducts accumulation is essential for enabling precise process adjustments and efficiency. Usually, gas chromatography (GC) or high-performance liquid chromatography (HPLC) are the methods used for bioproduct quantification, but these are hazardous and labor-intensive techniques. In this context, quantitative image analysis (QIA) emerges as a rapid and non-hazardous alternative for bioproducts quantification. The present work investigates the application of QIA for the simultaneous quantification of PHA and TAG in a mixed microbial culture fed with used cooking oil (UCO). Three stains were evaluated: Sudan Black B (SBB), Nile Blue A (NBA), and BODIPY. The data obtained from the images acquired were processed using multivariate statistical methods, specifically Partial Least Squares (PLS) and Multilinear Regression (MLR). Overall, SBB showed comparatively better results for PHA prediction, and the PLS models generally demonstrated higher reliability than MLR. However, none of the tested stains were effective in quantifying TAG, likely due to the concentration of data around a limited range, which hindered the model´s ability to predict this bioproduct. Simultaneous quantification of both products (PHA+TAG) resulted in intermediate model performance (R 2 ≈ 0.62-0.89), without improving the predictive accuracy of PHA quantification alone. These findings support the potential of image-based techniques for intracellular bioproduct quantification, while also highlighting the need for a larger and more diverse dataset to enhance model prediction accuracy and robustness. • Quantitative image analysis was used for bioproducts quantification. • Sudan Black B showed better results for polyhydroxyalkanoates quantification. • Partial least squares generally showed better results than multilinear regression. • Challenges in predicting triacylglycerides arise with datasets of low variability. Bioproducts ; Polyhydroxyalkanoates ; Partial least squares regression ; Multilinear map ; Process (computing) ; Repeatability ; Chemometrics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Spectroscopy and Chemometric Analyses
Authors: Shengjun Li | Yi Su | Teng Shen | Ke Li | Yunhua Zhang
Source: Sustainability (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Study on the Effect of Catalyst Loading on the DOC + SCR Coupled System of a Light-Duty Diesel Engine DOC coupled with SCR represents a key technological approach for reducing gaseous pollutant emissions from diesel engines. Based on engine bench testing using a light-duty diesel engine as a prototype, this study investigates the impact of DOC coupled with SCR at different catalyst loadings on diesel engine emission characteristics. Results indicate that higher DOC loadings lead to greater exhaust backpressure losses, with a maximum pressure difference reaching 4.3 kPa. The temperature difference across the DOC was minimally affected by catalyst loading. Higher DOC loading enhanced catalytic activity toward CO and THC. At medium-to-low loads, this effect was pronounced, while at high loads, the influence of catalyst loading diminished. Higher DOC loading enhances NO oxidation capacity. Under external characteristic conditions, elevated engine exhaust temperatures maximize post-DOC NO2 formation, increasing post-DOC NO2 production by over 100%. These findings provide useful guidance for optimizing diesel aftertreatment systems to achieve a better balance between pollutant reduction, energy consumption, and environmental sustainability, thereby supporting the sustainable development of cleaner diesel engine technologies. Diesel engine ; Diesel fuel ; Pollutant ; Diesel particulate filter ; Catalysis ; Diesel exhaust ; Exhaust gas recirculation ; Diesel exhaust fluid Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Biodiesel Production and Applications
Authors: Yun‐Seok Choi | Tae Gwan Park | Antoinette J. Taylor | Hou‐Tong Chen | Abul K. Azad
Source: Advanced Materials Technologies (article, 2026-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Terahertz Chiral Metamaterials Enabling Broadband Polarization Conversion Using Polarization Guiding Effect ABSTRACT Polarization control plays a vital role in terahertz (THz) photonics, enabling a wide range of applications from imaging and spectroscopy to sensing and wireless communication. However, conventional polarization control methods at THz frequencies are limited by narrow operational bandwidths and excessive absorption losses. In this work, we present a broadband THz polarization control based on the polarization guiding effect. We design and fabricate a THz polarization rotator capable of rotating incident linear polarization by 90°. The device consists of 24 twistingly stacked silicon–air metagrating layers that introduce a large degree of form birefringence, with each metagrating layer being 280 µm thick and sequentially twisted by 3.75°. Numerical simulations using the Berreman 4 × 4 method predict broadband operation of the device. Experimental validation using THz time‐domain spectroscopy confirms efficient and broadband 90° polarization rotation from 0.2 to 1.25 THz. Based on this concept, we further propose THz Q‐plates for the generation of broadband structured vector beams with radial and azimuthal polarization states. This work offers a scalable, material‐agnostic platform for advanced THz polarization manipulation and vector beam engineering, opening new pathways for next‐generation THz photonic devices. Terahertz radiation ; Broadband ; Polarization (electrochemistry) ; Polarization rotator ; Metamaterial ; Linear polarization ; Azimuth ; Polarizer Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Chao‐Hui Feng
Source: Processes (article, 2026-03-19)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Combined with Chemometrics Technique for Monitoring the Quality of Strawberries Under Various Pre-Cooling Treatments Hyperspectral imaging (HSI) combined with chemometrics has emerged as a rapid and non-destructive technique for fruit quality evaluation, enabling efficient monitoring of biochemical changes during postharvest storage. Among quality indicators, antioxidant activity is closely associated with nutritional value and physiological stability. This study aimed to develop an HSI-based approach for assessing the antioxidant capacity of strawberries subjected to different pre-cooling treatments during storage. Strawberries were treated with five pre-cooling methods and stored for up to 41 days. Antioxidant activity was measured using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay. Hyperspectral data were collected and preprocessed using multiplicative scatter correction (MSC), followed by partial least squares regression (PLSR) to construct predictive models. Among the treatments, immersion vacuum cooling combined with one-cycle pulsing (IVCWP1) exhibited significantly higher DPPH scavenging activity (61.17 ± 12.31%) than immersion vacuum cooling with water (IVCW, 52.89 ± 18.30%) (p &lt; 0.05). The PLSR model developed using MSC-corrected average reflectance spectra showed superior predictive performance and a higher coefficient of determination (R2) than models based on raw spectra. The results demonstrate that HSI coupled with chemometrics is an effective and practical tool for non-destructive evaluation of antioxidant activity and comparison of pre-cooling strategies in strawberries. Chemometrics ; Hyperspectral imaging ; Partial least squares regression ; Coefficient of determination ; Postharvest ; Predictive modelling ; Correlation coefficient ; Titratable acid Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Food Drying and Modeling
Authors: Jungkap Park | KyungGon Kim | Un-Beom Kang | Sangtae Kim
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-07)
Field: Chemistry — Spectroscopy

DelPi Learns Generalizable Peptide–Signal Correspondence for Mass Spectrometry-Based Proteomics Abstract Peptide identification in mass spectrometry–based proteomics has traditionally relied on handcrafted features or simplified probabilistic approaches that limit the interpretation of structured peptide evidence. We present DelPi, an open-source peptide identification framework that learns generalizable peptide–signal correspondence from raw spectra through self-supervised pre-training followed by task-specific fine-tuning. With model distillation enabling practical deployment, DelPi expands the interpretation of peptide evidence across data-independent and data-dependent acquisition while maintaining robust false discovery control. Probabilistic logic ; Identification (biology) ; Interpretation (philosophy) ; Limit (mathematics) ; Proteomics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Adam J. Rish | Samuel R. Henson | Owen G. Rehrauer
Source: Journal of Chemometrics (article, 2026-01-27)
Field: Chemistry — Analytical Chemistry

Lean Chemometrics in Spectroscopic Process Analytical Technology ABSTRACT The adoption of spectroscopy as a process analytical technology (PAT) modality in the pharmaceutical industry and related sectors has enabled advanced monitoring and control of manufacturing processes. Most applications of spectroscopic PAT instruments are dependent on chemometric multivariate data analysis (MVDA) methods to extract the relevant process data from the spectral measurements. However, calibrating and maintaining conventional MVDA methods is often burdensome, as it requires extensive time, material, and financial costs to generate the necessary representative samples and corresponding reference data. This calibration burden can be a barrier to the adoption of spectroscopic PAT in the pharmaceutical industry. Within this article, a classification of MVDA methods referred to as “lean chemometrics” is proposed and formalized. Lean chemometrics are time‐saving, material‐sparing, and cost‐cutting MVDA methods that reduce the calibration burden relative to conventional chemometric methods of choice for spectroscopic PAT. Categories of various MVDA methods that are classifiable as lean chemometric techniques and practical considerations for integration of these techniques with PAT in common pharmaceutical PAT applications are discussed. The intention of lean chemometrics is to raise awareness of solutions that minimize the challenge of calibration burden toward improving spectroscopic PAT adoption. Chemometrics ; Process analytical technology ; Calibration ; Process (computing) ; Multivariate statistics ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Carlos Cardona-Giraldo | C. Fanelli | James Giroux | Cole G. Granger | Benjamin Nachman | Gerald Sabin
Source: arXiv (Cornell University) (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning Modern particle physics experiments face an increasing demand for high-fidelity detector simulation as luminosities rise and computational requirements approach the limits of available resources. Deep generative models have emerged as promising surrogates for traditional Monte Carlo simulation, with recent advances drawing inspiration from large language models (LLM) and next-token prediction paradigms. In this work, we introduce a generalizable foundation model for calorimetry built on next-token transformer backbones, designed to support modular adaptation across materials, particle species, and detector configurations. Our approach combines Mixture-of-Experts pre-training with parameter-efficient fine-tuning strategies to enable controlled, additive model expansion without catastrophic forgetting. A pre-trained backbone is trained to generate electromagnetic showers across multiple absorber materials, while new materials are incorporated through the addition and tuning of lightweight expert modules. Extensions to new particle types are achieved via parameter-efficient fine-tuning and modular vocabularies, preserving the integrity of the base model. This design enables efficient, incremental knowledge integration as new simulation datasets become available, a critical requirement in realistic detector-development workflows. In addition, we demonstrate that next-token calorimeter models are computationally competitive with standard generative approaches under established LLM optimization procedures. These results establish next-token architectures as a viable path toward extensible, physics-aware foundation models for calorimetry and future high-energy physics experiments. Modular design ; Monte Carlo method ; Detector ; Transformer ; Particle filter ; Knowledge base Machine Learning in Materials Science ; Particle physics theoretical and experimental studies ; Nuclear physics research studies
Authors: Waqas Khalid | Dmitry I. Ignatov | Radu Timofte
Source: ArXiv.org (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design--balancing syntactic reliability, performance, and structural novelty--remains underexplored. We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles. The model synthesizes PyTorch convolutional networks which are validated, evaluated via low-fidelity performance signals (single-epoch accuracy), and filtered using a MinHash-Jaccard criterion to prevent structural redundancy. High-performing, novel architectures are converted into prompt-code pairs for iterative fine-tuning via parameter-efficient LoRA adaptation, initialized from the LEMUR dataset. Across cycles, the LLM internalizes empirical architectural priors, becoming a robust generator. The valid generation rate stabilizes at 50.6 percent (peaking at 74.5 percent), while mean first-epoch accuracy rises from 28.06 percent to 50.99 percent, and the fraction of candidates exceeding 40 percent accuracy grows from 2.04 percent to 96.81 percent. Analyses confirm the model moves beyond replicating existing motifs, synthesizing 455 high-performing architectures absent from the original corpus. By grounding code synthesis in execution feedback, this work provides a scalable blueprint for transforming stochastic generators into autonomous, performance-driven neural designers, establishing that LLMs can internalize empirical, non-textual rewards to transcend their training data. Scalability ; Blueprint ; Code (set theory) ; Memorization ; Language model ; Emulation ; LOOM ; Artificial neural network ; Code generation Machine Learning in Materials Science ; Software Engineering Research ; Topic Modeling
Authors: Pratiksha S. Karwande | Bhakti P. Gunjal | Shashank S. Morkile | Vijaya Barge
Source: International Journal of Pharmacy and Pharmaceutical Research (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

A Critical Review of Derivative Ultraviolet Spectrophotometry in Pharmaceutical Analysis Derivative UV spectrophotometry is an advanced analytical technique widely used in pharmaceutical analysis for qualitative and quantitative determination of drugs.The method enhances spectral resolution and improves analytical sensitivity compared to conventional UV spectroscopy.By transforming the normal absorbance spectrum into its derivative form, the method allows the precise estimation of analytes in complex mixtures without prior separation.Derivative spectrophotometry has gained significant importance in pharmaceutical quality control due to its simplicity, cost-effectiveness, and rapid analysis capability.This review focuses on the theoretical principles, instrumentation, mathematical background, and pharmaceutical applications of derivative UV spectroscopy.Additionally, advantages, limitations, and recent developments in derivative spectroscopic techniques are discussed.The technique is particularly useful for the simultaneous estimation of drugs in multicomponent formulations, improving accuracy and selectivity.Thus, derivative UV spectrophotometry serves as an effective analytical tool in pharmaceutical research, method development, and quality control. Spectrophotometry ; Derivative (finance) ; Analyte ; Absorbance ; Second derivative ; Pharmaceutical formulation ; Resolution (logic) ; Ultraviolet Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Leon Browne | Stephen R. Power
Source: arXiv (Cornell University) (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

Optimizing quantum transport in multi-barrier graphene systems using differential evolution Potential and mass barriers in graphene introduce electron scattering, modulating transmission probabilities. Complex multi-barrier setups allow electron transmission to be controlled with high precision, but have a huge design space of possible barrier geometries. This work presents a framework to optimize electronic transport in such systems using differential evolution algorithms. First, transfer matrix methods are employed to efficiently compute quantum transport through multi-barrier structures, before optimization is applied to find barrier configurations whose transmission profiles closely match a predefined target profile. To address the trade-off between the accuracy and complexity of resulting barrier configurations, regularization techniques are incorporated into the optimization process. Our approach demonstrates the potential for highly tunable electronic transport in graphene-based systems by exploiting evolution-inspired optimization techniques. Quantum ; Graphene ; Differential evolution ; Work (physics) ; Electron transport chain ; Transmission (telecommunications) ; Electron ; Rectangular potential barrier ; Optimization problem Graphene research and applications ; Molecular Junctions and Nanostructures ; Quantum and electron transport phenomena
Authors: G. Giuliano | Wilma Polini
Source: Applied Sciences (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Thickness Uniformity Improvement in Superplastic Hemispherical Shell Using Truncated Conical Blanks: Numerical and Experimental Analysis Achieving thickness uniformity is a critical challenge in superplastic forming (SPF) of hemispherical shells, as standard constant-thickness blanks suffer from excessive thinning at the pole. While the literature suggests using variable thickness blanks to mitigate this issue, existing solutions often rely on complex, non-linear profiles that are expensive and difficult to manufacture. This work proposes a cost-effective, truncated conical blank design (linearly variable thickness) to optimize material distribution. The approach combines Finite Element Method (FEM) analysis and experimental validation on AZ31 magnesium alloy. The study demonstrates that the optimized truncated conical profile (α = 0.2) yields superior structural quality, drastically reducing the thinning factor to 9%. This represents a significant improvement compared to the ~14% thinning observed with conical profile (α = 0) blanks and outperforms constant-thickness blanks (30%). These results demonstrate that a simplified, easily machinable blank geometry can effectively address the thinning problem, providing a practical solution for industrial SPF applications. Blank ; Conical surface ; Superplasticity ; Thinning ; Finite element method ; Shell (structure) Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Magnesium Alloys: Properties and Applications
Authors: Yasin Pathan | Pawan Kumar Singotia | K. Manoz Kumar Reddy | Shradhesh R. Marve | Subbarama Kousik Suraparaju | Ch Polayya | Nupur Ojha
Source: Scientific Reports (article, 2026-02-05)
Field: Materials Science — Polymers and Plastics

Potential of jute seeds crop waste for textile applications: a novel machine-based approach for sustainable resource utilization Seed-producing jute plants are commonly harvested solely for their seeds, leaving the fibre-rich stems unutilized and often burned, which contributes to environmental pollution and resource loss. This study explores the valorization of these stems by extracting fibres suitable for textile applications while maintaining seed yield. A novel Circular Scissor machine was designed and fabricated to separate seed-bearing branches from fibre-rich stems with an operational efficiency of 65–100% and minimal seed damage. The separated stems were processed using two retting methods: biological (water) retting and a mixed biological–chemical retting process employing 5% urea. A side-by-side comparison showed that the urea-assisted process reduced the total retting time (from 33 to 29 days) and produced finer fibres (2.90 tex vs. 3.27 tex for water-retted samples), with improved surface cleanliness and better strand separation, as confirmed by SEM and FTIR analyses. Both methods yielded fibres that met Bureau of Indian Standards (BIS) grading standards for coarse textile applications such as rugs, mats, and upholstery. This combined approach preserving seeds while recovering fibre reduces open-field burning, enhances farmers’ income streams, and demonstrates the potential of jute seed crop stems as a sustainable textile fibre resource. Retting ; Textile ; Coir ; Effluent ; Crop ; Green waste ; Textile industry ; Puddling Natural Fiber Reinforced Composites ; Agricultural Engineering and Mechanization ; Hygrothermal properties of building materials