jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ramya Sharma
Source: Aeronautical and Aerospace Engineering (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

A Decision-Support System for Cryogenic Storage Tank Material Selection Based on WASPAS Material selection problem of cryogenic storage tank the material selection for cryogenic storage tanks is a critical and complex problem due to the extreme operating conditions and specific requirements of cryogenic applications. Cryogenic storage tanks are designed to store liquefied gases at very low temperatures, typically below -150°C (-238°F) Common materials used for cryogenic storage tanks include stainless steels, aluminum alloys, and certain low-temperature carbon steels. Additionally, specialized materials such as nickel alloys or composite materials may be utilized for specific applications where enhanced properties are required. The material selection process for cryogenic storage tanks involves a comprehensive evaluation of the aforementioned factors, often utilizing material databases, testing, and simulation tools. It is essential to consult with experts in cryogenic engineering and consider the specific requirements of the intended application to ensure the optimal material choice for cryogenic storage tanks. he Weighted Aggregated Sum Product Assessment (WASPAS) methodology is a multi-criteria decision-making (MCDM) technique used to solve problems where multiple criteria need to be considered for decision-making. The WASPAS methodology allows decision-makers to systematically evaluate alternatives based on multiple criteria and their relative importance. It provides a structured approach for decision-making that takes into account the preferences and priorities of the decision-maker. The weights assigned to the criteria play a crucial role in determining the final ranking of the alternatives. 2024 aluminium in the T6 temper, 2024 aluminium in the O temper, 301 Full Hard Tempered Stainless Steel, Stainless Steel 310, TC4, Ti64, or ASTM Grade 5, nickel-chromium-molybdenum super alloy, Brass. Yield Strength, Elastic Modulus, Toughness Index, Density, Specific Heat, Thermal Expansion. From the result we can see that ss301-FH got 1st rank and AL5052-0 got last rank. by using waspas method we obtained that ss301-FH got 1st rank and AL5052-0 got last rank . Storage tank ; Material selection ; Process (computing) ; Selection (genetic algorithm) ; Ranking (information retrieval) ; Cryogenics ; Yield (engineering) Material Selection and Properties ; Metallurgical and Alloy Processes ; Machine Learning in Materials Science
Authors: Irem O. Alp
Source: Photonics (article, 2026-03-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compact EM Wave Trapping in Low-Index-Contrast Structures with Al2O3-Based Experimental Validation This study presents the design of a compact low-index-contrast structure capable of trapping electromagnetic waves within a highly confined region. The energy storage performance of the geometries was enhanced using a genetic algorithm that employed a binary (present/absent) assignment of insulating cylinders. Time-domain simulations performed with MEEP were experimentally validated using precisely positioned θ-phase alumina rods, resulting in a 13 dB increase in receiver-antenna power within the microwave regime. The measurements showed a correlation exceeding 99% with the computational results over a dielectric constant range of 4.5 to 5.0. A quality factor of Q =3.6×103 was measured at 5.08 GHz in the experiment. Subsequently, machine learning techniques were applied, further increasing the Q value to approximately 104, even within such a small configuration. Furthermore, the proposed structure does not require a complete photonic bandgap; instead, relatively high-Q factors were achieved by suppressing radiation losses through a pattern of low-index dielectric rods. Dielectric ; Electromagnetic radiation ; Microwave ; Range (aeronautics) ; Trapping ; Binary number ; Energy (signal processing) ; Photonics ; Radiation Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Simulation and Numerical Methods
Authors: Woodruff, Simon
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

A costing framework for fusion power plants This paper summarizes and consolidates fusion power-plant costing work performed in support of ARPA-E from 2017 through 2024, and documents the evolution of the associated analysis framework from early capital-cost-focused studies to a standards-aligned, auditable costing capability. Early efforts applied ARIES-style cost-scaling relations to generate Nth-of-a-kind (NOAK) estimates and were calibrated through a pilot study with Bechtel and Decysive Systems to benchmark balance-of-plant (BOP) costs and validate plant-level reasonableness from an engineering, procurement, and construction (EPC) perspective. Subsequent work, informed by Lucid Catalyst studies of nuclear cost drivers, expanded the methodology to treat indirect costs explicitly and to evaluate cost-reduction pathways for non-fusion-island systems through design-for-cost practices, modularization, centralized manufacturing, and learning. As ARPA-E's fusion portfolio expanded, these methods were applied across BETHE and GAMOW concepts (and select ALPHA revisits), including enhanced treatment of tritium handling and plant integration supported by Princeton/PPPL expertise. In 2023 the capability was refactored to align with the IAEA-GEN-IV EMWG-EPRI code-of-accounts lineage, while key ARIES-derived scaling relations were replaced by bottom-up subsystem models for dominant fusion cost drivers (e.g., magnets, lasers, power supplies, and power-core components) coupled to physics-informed power balances and engineering-constrained radial builds. These developments were implemented in the spreadsheet-based Fusion Economics code (FECONs) and released as an open-source Python framework (pyFECONs), providing a transparent mapping from subsystem estimates to standardized accounts and a consistent computation of LCOE. Activity-based costing ; Computation ; Cost driver ; Portfolio ; Key (lock) ; Benchmark (surveying) ; Cost estimate ; Fusion power Fusion materials and technologies ; Fusion and Plasma Physics Studies ; Magnetic confinement fusion research
Authors: R Selvaprakash | N S Rabi | K Aundy | R Rajeev | C Viswanathan
Source: Plant Science Today (article, 2026-01-07)
Field: Chemistry — Analytical Chemistry

Early detection of rice blast disease using hyperspectral remote sensing We presented an integrative hyperspectral approach for the rapid and non-invasive detection of rice blast (Magnaporthe oryzae) that moves beyond traditional index-based methods. Leaf and canopy-level reflectance data (350 nm- 2500 nm) were smoothed using Savitzky - Golay polynomials, standardised with Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) and then differentiated to highlight subtle infection signals. Dimensionality reduction methods including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) revealed clear separations between healthy and diseased spectra, while cosine similarity and the Spectral Angle Mapper (SAM) measured illumination-invariant spectral differences. A Random Forest impurity analysis identified the ten most informative wavelengths, enabling the evaluation of over one million band combinations. From this, we developed the Rice Blast Index (RBI = (R1068 - R1560) / (R1068 + R1560)), which outperformed Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI), achieving an F1-score of 0.95 and Cohen’s κ of 0.93 across independent growing seasons. New structural diagnostics, including lagged correlation, spectral autocorrelation and feature persistence, were introduced to quantify redundancy and identify stable biochemical absorption zones, notably a 38 nm region around 680 nm and a 1470 nm region linked with chlorophyll - protein features. Outlier spectra were removed with an Isolation Forest algorithm, improving robustness by 4.7 %. The average processing time was 18 ms per spectrum, enabling real-time scouting. Together, these elements deliver a unified, end-to-end framework that combines advanced pre-processing, dimensionality reduction, anomaly rejection, machine-learning-based band selection and new structural metrics. This framework improves early rice blast surveillance and offers a transferable template for hyperspectral phenotyping of diverse crop stresses, effectively bridging fine-scale sensitivity with field-scale applicability in precision agriculture. Hyperspectral imaging ; Principal component analysis ; Dimensionality reduction ; Random forest ; Spectral bands ; Normalized Difference Vegetation Index ; Pattern recognition (psychology) ; Spectral index ; Outlier Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Yulia Rachmawati | Amellia Dwi Rizkyana | Lulus Mualimin | Fawait Afnani | Deyla Prajna
Source: BIO Web of Conferences (article, 2026-01-01)
Field: Materials Science — Biomaterials

Optimization of extraction and characterization of cellulose from edamame pod waste for active biofilm applications Edamame pod waste, a by-product of soybean processing, represents a promising lignocellulosic biomass source for biopolymer development. This study aimed to optimize cellulose extraction from edamame pod waste using Response Surface Methodology (RSM) with a Box–Behnken Design (BBD) and to characterize the extracted cellulose for potential biofilm applications. Four independent factors—particle size (40– 80 mesh), extraction temperature (60–90 °C), extraction time (1–3 h), and NaOH concentration (5–10%, w/v)—were investigated, with cellulose yield (%) and whiteness index (WI) as responses. The optimized conditions (80 mesh, 75°C, 1 h, 7.5% NaOH) produced cellulose with a yield of 54.5% and a WI of 70%. Subsequent alkaline hydrogen peroxide (AHP) washing enhanced cellulose purity, increasing cellulose content from 41.25% to 65.14% and reducing hemicellulose and lignin to 19.07% and 9.21%, respectively. FTIR analysis confirmed successful delignification through the disappearance of lignin-associated peaks (1508–1512 cm⁻¹) and the presence of characteristic β-1,4-glycosidic linkages. Thermal characterization using Thermogravimetric Analysis (TGA) and Derivative Thermogravimetry (DTG) showed a single-step degradation pattern with Tonset ≈ 275 °C, Tmax ≈ 350 °C, and ~13% residue at 600 °C, indicating high thermal stability and purity comparable to commercial cellulose. These findings demonstrate that edamame pod waste can be efficiently converted into high-purity, thermally stable cellulose through an eco-friendly alkaline extraction process, providing a sustainable raw material for the development of biodegradable biopolymers and active biofilms as alternatives to synthetic plastics. Cellulose ; Hemicellulose ; Extraction (chemistry) ; Lignin ; Biopolymer ; Thermogravimetric analysis ; Thermal stability ; Yield (engineering) ; Thermogravimetry Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Polysaccharides and Plant Cell Walls
Authors: Jiayi Liu | Xiaolin Guo | Xiaoyu Mu
Source: Journal of intelligent medicine. (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Two‐dimensional materials for next‐generation biosensors Abstract Since the revolutionary breakthrough in the successful isolation and characterization of graphene in 2004, the field of two‐dimensional materials has undergone a major transformation. This pivotal advancement not only confirmed the unique ability of atomically thin layers to confine charge carriers within a two‐dimensional plane but also sparked a wave of research into transition metal dichalcogenides and Xene derivatives such as silicene, boronene, and germanene. These materials exhibit exceptional biophysical properties, electrical performance, mechanical flexibility, and optical responsiveness, providing a novel platform for developing bioelectronic devices. Current research centers on functional device innovation: electrodes, transistors, and p‐n junctions based on two‐dimensional materials are advancing biosensor upgrades for seamless skin interaction; emerging neural interface systems can collaborate with brain and ocular tissues; and smart tattoo sensors demonstrate self‐powered detection potential. These breakthrough technologies collectively point toward the convergent development of wearable devices, implantable systems, and bioelectronic interfaces, laying the foundation for next‐generation intelligent sensing technologies. Graphene ; Biosensor ; Characterization (materials science) ; Wearable computer ; Interface (matter) ; Isolation (microbiology) ; Carbon nanotube ; Biomimetic materials 2D Materials and Applications ; Graphene research and applications ; Polydiacetylene-based materials and applications
Authors: Jian Wu | Xianhuan Zeng | Aiguo Ouyang | Bin Li | Nan Chen | Yande Liu
Source: Journal of Food Science (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Assessment of Optical Properties and Monte Carlo Multilayered Simulation of Light Propagation in Cherries for SSC and MC Detection Nondestructive detection technology for cherry quality is a critical demand in the field of agricultural product processing, whereas existing studies lack a systematic exploration of the optical properties of cherries. This study investigated the optical properties (OPs) of cherry tissues and the light propagation using Monte Carlo multilayered (MCML) simulations. Moreover, the relationships between the OPs with internal qualities were explored. The results showed that skin tissues had significant absorption in 400-600 nm, while decreasing in 650-1050 nm. The reduced scattering coefficient of flesh tissues has significant differences in 400-1050 nm. Microscopic images revealed that the differences in OPs were caused by the broken cellular structure, and the bruising had no significant effect on soluble solids content (SSC) and moisture content (MC). MCML simulations indicated that photon energy distribution was dominated by diffuse reflectance due to the high reflectance of the kernel. Therefore, the reflectance model is more effective for detecting internal qualities. The PLSR models for the prediction of SSC and MC built on the absorption coefficient spectra had the best prediction performance, with a coefficient of determination of 0.935 and 0.839, and root mean square error of 0.254 and 0.008, respectively. The results indicate that the absorption characteristics of cherry tissue are primarily influenced by SSC and MC, and the use of the absorption coefficient enables more accurate prediction of its internal quality. This work offers guidance for nondestructive assessment methods targeting cherry internal quality. PRACTICAL APPLICATIONS: The reflectance model is more effective for detecting internal qualities, and the internal quality can be predicted better based on the absorption coefficient. This work offers guidance for nondestructive assessment methods targeting cherry internal quality. Monte Carlo method ; Attenuation coefficient ; Absorption (acoustics) ; Scattering ; Coefficient of determination ; Photon ; Reflectivity ; Diffuse reflection ; Opacity Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Food Drying and Modeling
Authors: Ragini Bose | Shubham Ghosh | Moutoshi Singh | Sarthak Singh
Source: Journal of Drug Delivery and Therapeutics (article, 2026-01-15)
Field: Chemistry — Inorganic Chemistry

Beyond the Beverage: The Versatility and Health Benefits of Coconut Water Young coconuts contain a clear liquid called coconut water, which has attracted a lot of interest due to its inherent qualities and potential health benefits. Coconut water, which has long been used as a hydration beverage, is now valued for its diverse nutritional composition, which includes vital sugars, proteins, amino acids, vitamins, minerals, and other substances that promote growth. This review provides an overview of recent studies on the physicochemical traits, medicinal qualities, and practical uses of coconut water. Research has emphasized its cardioprotective, anti-inflammatory, and antioxidant properties as well as its possible application in the treatment of kidney disease, diabetes, and hypertension. The review also looks at how coconut water has been used historically and traditionally in medicine across cultures as a natural cure for a range of illnesses. In addition to its nutritional and therapeutic value, coconut water is being investigated for use in pharmaceutical and cosmetic formulations, as well as for food preservation and biotechnological applications. The results highlight that coconut water is a multipurpose biofluid with potential uses in the health and wellness sectors, making it more than just a cool beverage. Its therapeutic potential could be further established and its commercial use expanded with further research into its bioactive compounds and mechanisms of action. To sum up, coconut water is a naturally occurring, sustainable, and biologically active resource with a variety of advantages that warrant further research and incorporation into contemporary medical procedures. Keywords: Coconut water, Hydration, Therapeutic potential, Biofluid, Cardioprotective, Anti-inflammatory, Anti-oxidant. Health benefits ; Cocos nucifera ; Resource (disambiguation) ; Coconut oil ; Health problems ; Complementary medicine Coconut Research and Applications ; Herbal Medicine and Trade Cooperation ; Medicinal Plant Research
Authors: Soniya Sriram
Source: Journal on Applied and Chemical Physics (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Applying The WASPAS Method For Optimal Material Selection in Cryogenic Storage Tank Design Cryogenic storage tanks are designed to store liquefied gases at very low temperatures, typically below -150°C (-238°F) Common materials used for cryogenic storage tanks include stainless steels, aluminium alloys, and certain low-temperature carbon steels. Additionally, specialized materials such as nickel alloys or composite materials may be utilized for specific applications where enhanced properties are required. The material selection process for cryogenic storage tanks involves a comprehensive evaluation of the aforementioned factors, often utilizing material databases, testing, and simulation tools. It is essential to consult with experts in cryogenic engineering and consider the specific requirements of the intended application to ensure the optimal material choice for cryogenic storage tanks. the Weighted Aggregated Sum Product Assessment (WASPAS) methodology is a multi-criteria decision-making (MCDM) technique used to solve problems where multiple criteria need to be considered for decision-making. It provides a structured approach for decision-making that takes into account the preferences and priorities of the decision-maker. The weights assigned to the criteria play a crucial role in determining the final ranking of the alternatives. 2024 aluminium in the T6 temper, 2024 aluminium in the O temper, 301 Full Hard Tempered Stainless Steel, Stainless Steel 310, TC4, Ti64, or ASTM Grade 5, nickel-chromium-molybdenum superalloy, Brass. Yield Strength, Elastic Modulus, Toughness Index, Density, Specific Heat, Thermal Expansion. from the result we can see that ss301-FH got 1st rank and AL5052-0 got last rank .by using waspas method we obtained that ss301-FH got 1st rank and AL5052-0 got last rank Material selection ; Ranking (information retrieval) ; Process (computing) ; Storage tank ; Rank (graph theory) ; Selection (genetic algorithm) ; Yield (engineering) Material Selection and Properties ; Heat Transfer and Numerical Methods ; Modeling, Simulation, and Optimization
Authors: Dennis Kuznetsov | Ivan O. Muzyka | Oleksiy R. Ivashchenko
Source: Applied Aspects of Information Technology (article, 2026-02-24)
Field: Chemistry — Analytical Chemistry

A machine vision system for real-time grain quality classification using machine learning Grain quality control directly affects the efficiency of grain processing, the stability of product parameters, and the economic outcomes of storage and cleaning operations.In industrial practice, visual inspection and manual sampling remain widespread, yet these approaches are time-consuming and sensitive to human subjectivity, especially when large grain volumes must be assessed continuously.This study addresses the need for an automated, objective, and scalable solution for grain quality classification that can operate in real time under conveyor-based conditions.The purpose of the research is to develop and experimentally validate a machine vision system that classifies grain by quality and supports operational decisions for fractional separation and cleaning.An experimental test stand was built to simulate conveyor transportation of grain, enabling controlled variation of belt speed and illumination conditions.A dataset of wheat, corn, and barley was formed using laboratory image capture and manual labeling into three quality classes based on visible defects and damage severity.Image preprocessing and augmentation were applied to increase variability and improve robustness.Two supervised approaches were implemented for comparative evaluation: a deep learning image classifier and a kernel-based classifier using handcrafted visual descriptors.The experimental results demonstrate that the deep learning approach achieves higher classification accuracy, while the kernel-based classifier provides faster inference with a moderate reduction in accuracy.The most frequent misclassifications occur between adjacent quality categories, indicating the importance of borderline-class coverage and labeling consistency.Processing time measurements confirm the feasibility of real-time operation for moderate grain flow rates, with performance degradation at higher conveyor speeds due to motion-related image distortions.The scientific novelty lies in the integrated experimental assessment of classification accuracy and throughput under controlled conveyor conditions.The practical significance is the feasibility of deploying the system as a component of automated grain cleaning and separation lines. Machine vision ; Quality (philosophy) ; Pattern recognition (psychology) ; Support vector machine ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Image Processing Techniques and Applications