jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Han Jiang | Susu Li | Zhenchao Li | Long Jiang Zhang | Yaohua Xu | Saranarayanan Ramachandran | Shuibao Liang
Source: IEEE Transactions on Semiconductor Manufacturing (article, 2025-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Grain Morphology Effects on Void Formation and Electromigration-Induced Failure in Copper Interconnects Electromigration, driven by increasing current densities in scaled integrated circuits, poses a serious reliability challenge in semiconductor manufacturing, often culminating in void formation and interconnect failure. As interconnect dimensions shrink to the nanoscale, the influence of microstructural features and grain morphology on interconnect reliability and degradation mechanisms becomes increasingly significant. In this work, we develop a physics-based phase field model to investigate the void formation and evolution in copper interconnects subjected to electromigration. Our simulations closely align with experimental trends, revealing that voids predominantly emerge at or initiate from the cathode end, with their evolution strongly influenced by grain morphology. In the surface diffusion-limited scenario (SDLS), grain boundaries significantly promote void formation, resulting in increased void sizes. Under the grain boundary diffusion-limited scenario (GBDLS), void nucleation in bamboo structure (BS) and polycrystalline structure (PS) interconnects occurs relatively early, often initiating near the interface between the copper and the capping layer. The voltage distribution exhibits noticeable inhomogeneity, with high electric field intensities localized at grain boundaries and void surfaces. The electromigration-induced mass flux predominantly flows from the bottom right toward the left. Additionally, an increased number of copper grains leads to larger voids, with a more pronounced effect in the SDLS. Furthermore, electromigration-induced void growth progressively reduces electrical conductivity. These findings elucidate the complex interplay between grain morphology, electromigration-driven void formation, and electrical performance degradation in copper interconnects, and are anticipated to offer valuable insights into microstructure-dependent failure mechanisms while supporting process optimization in advanced interconnect fabrication. Electromigration ; Void (composites) ; Copper ; Materials science ; Copper interconnect ; Grain size ; Morphology (biology) ; Metallurgy ; Composite material ; Geology Copper Interconnects and Reliability ; Integrated Circuits and Semiconductor Failure Analysis ; Electronic Packaging and Soldering Technologies
Authors: Raustin Reyes | Yuhei Sekiguchi | Daisuke Itô | Taichi Fujiwara | Kansei Watanabe | Toshiharu Makino | Hiromitsu Kato | Hideo Kosaka
Source: npj Quantum Information (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Quantum teleportation of a photon via absorption and emission for quantum repeater nodes Absorption and emission, fundamental interactions between light and matter, enable the regeneration of a quantum state of light via matter through concatenated quantum state transfer based on the principle of quantum teleportation. This transfer is enabled by electron spin-orbit entanglement and electron-nuclear spin entanglement inherent within the material. Here, we demonstrate that a photon quantum state imprinted in polarization is transferred to another photon emitted from a nitrogen vacancy (NV) center. This transfer is heralded by the result of the Bell state measurement between the electron and nitrogen nuclear spins. We show that the minimum number of incident photons needed to achieve transfer is, on average, only 0.1 photons, enabling quantum teleportation over 10 km. This demonstration paves the way for a quantum repeater that is robust against phase and intensity errors, unlike the conventional photon interference scheme, thereby facilitating practical quantum networks. Quantum teleportation ; Quantum network ; Quantum entanglement ; Quantum channel ; Photon ; Quantum sensor ; Quantum information science ; Repeater (horology) ; Quantum state Diamond and Carbon-based Materials Research ; Mechanical and Optical Resonators ; Quantum Information and Cryptography
Authors: Tsvetelina Georgieva | Eleonora Nedelcheva | Stanislav Penchev | Albena Ivanova-Vasileva | Plamen Daskalov
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Indirect approach for measurement of main physico-chemical parameters of engine oils using optical methods The article presents an indirect approach for measuring two main physicochemical parameters – kinematic viscosity and total alkalinity of motor oils based on digital analysis of oil images and regression models containing the color features of their images, which were developed based on reference values obtained from laboratory analysis. Two types of oils with viscosity class – XW-30 and XW-40 – were analyzed. Ten regression models were developed, which were estimated using the two errors RMSE and MAE. The smallest average values of RMSE and MAE are obtained when the images are captured under homogeneous illumination without external light. For viscosity measurement with the highest R 2 , the regression models are linear SVM and fine tree, and for total alkalinity – stepwise regression and fine tree. Alkalinity ; Linear regression ; Viscosity ; Mean squared error ; Mathematics ; Regression analysis ; Homogeneous ; Coefficient of determination ; Regression ; Statistics ; Chemistry ; Materials science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Petroleum Processing and Analysis
Authors: Mesay Altaye
Source: International Journal of Fluid Mechanics & Thermal Sciences (article, 2025-06-06)
Field: Materials Science — Materials Chemistry

Impact of Nerium-Water Emulsion with Diethyl Ether on Performance and Emission Reduction in a Direct Injection Diesel Engine This study investigated the impact of a Nerium-water emulsion, enhanced with diethyl ether (DEE) addition at 5%, 10%, and 15% concentrations, on the performance, combustion, and emissions of a direct injection (DI) diesel engine. Motivated by the need for sustainable fuels and reduced pollutant output, this research explored the potential of a Nerium-derived emulsion, leveraging DEE’s oxygenating properties. Experiments were conducted on a single-cylinder, water-cooled diesel engine under steady-state conditions, analyzing in-cylinder pressure, heat release rate (HRR), nitrogen oxides (NOx), and smoke opacity. Brake thermal efficiency and specific energy consumption were also evaluated. The finding revealed that incorporating the Nerium-water emulsion DEE effectively decreased NOx and smoke opacity while largely maintaining brake thermal efficiency. Notably, higher DEE concentrations are generally correlated with greater emission reductions. The 15% DEE blend demonstrated the most promising results, achieving the lowest NOx and smoke opacity with minimal compromise to engine efficiency compared to the 5% and 10% blends. These results unequivocally highlight the synergistic advantages of employing a Nerium-water emulsion enriched with DEE as a fuel for a DI diesel engine. This approach offers a viable strategy for cleaner engine operation by utilizing a non-food biomass resource and significantly mitigating harmful emissions, thereby contributing to more sustainable transportation practices. The optimized blend with 15% DEE presents a particularly beneficial pathway for balancing performance and emission reduction in DI diesel engine applications. Diethyl ether ; Emulsion ; Diesel engine ; Diesel fuel ; Reduction (mathematics) ; Materials science ; Ether ; Pulp and paper industry ; Waste management ; Environmental science ; Chemistry ; Organic chemistry ; Automotive engineering ; Mathematics ; Engineering Catalytic Processes in Materials Science ; Biodiesel Production and Applications ; Advanced Combustion Engine Technologies
Authors: Aisha Y Al-Mansoori | Md. Razzab Ali
Source: International Journal of Advanced Academic Studies (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Innovative approaches to non-destructive testing in fruit quality assessment enhancing agricultural productivity Non-destructive testing (NDT) has become a vital tool in the modern agricultural sector, particularly in fruit quality assessment. With increasing global demand for high-quality fruits and the rising challenges of post-harvest losses, it is crucial to adopt innovative and efficient methods for quality evaluation. Traditional testing techniques, often destructive in nature, have been slowly replaced by non-invasive approaches that allow for real-time, accurate assessment of fruit attributes such as ripeness, texture, and internal quality. This review paper explores the current state of non-destructive testing methods, including near-infrared spectroscopy (NIR), ultrasonic testing, and hyperspectral imaging, highlighting their applications in fruit quality control. These techniques offer several advantages, including reducing waste, enhancing productivity, and improving marketability of fresh produce. Furthermore, the paper discusses the integration of artificial intelligence (AI) and machine learning with NDT, which enhances the precision and efficiency of these techniques by enabling automated analysis of large datasets. The review also covers advancements in sensor technology, software tools, and the integration of multiple NDT methods to provide more comprehensive quality assessments. While significant progress has been made, challenges such as high equipment costs and complexity remain barriers to widespread adoption, particularly in developing countries. This paper concludes by suggesting potential areas for future research, emphasizing the need for cost-effective solutions and further refinement of hybrid systems that combine multiple NDT techniques. Ultimately, the goal is to enhance agricultural productivity and reduce post-harvest losses by adopting cutting-edge, non-destructive technologies in fruit quality assessment. Quality (philosophy) ; Productivity ; Agriculture ; Agricultural productivity ; Emerging technologies ; Quality assessment Spectroscopy and Chemometric Analyses
Authors: Zhaoyuan Hou | Yaxuan Wang | Feng Tan | Jiadong Gao | Feng Jiao | Chunjie Su | Xin Zheng
Source: Plants (article, 2025-04-12)
Field: Chemistry — Analytical Chemistry

Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra Accurate diagnosis of crop nutritional status is critical for optimizing yield and quality in modern agriculture. This study enhances the accuracy of Raman spectroscopy-based nutrient diagnosis, improving its application in precision agriculture. We propose a method to identify optimal diagnostic positions on cucumber leaves for early detection of nitrogen (N), phosphorus (P), and potassium (K) deficiencies, thereby providing a robust scientific basis for high-throughput phenotyping using Raman spectroscopy (RS). Using a dot-matrix approach, we collected RS data across different leaf positions and explored the selection of diagnostic positions through spectral cosine similarity analysis. These results provide critical insights for developing rapid, non-destructive methods for nutrient stress monitoring in crops. Results show that spectral similarity across positions exhibits higher instability during the early developmental stages of leaves or under short-term (24 h) nutrient stress, with significant differences in the stability of spectral data among treatment groups. However, visual analysis of the spatial distribution of positions with lower similarity values reveals consistent spectral similarity distribution patterns across different treatment groups, with the lower similarity values predominantly observed at the leaf margins, near the main veins, and at the leaf base. Excluding low-similarity data significantly improved model performance for early (24 h) nutrient deficiency diagnosis, resulting in higher precision, recall, and F1 scores. Based on these results, the efficacy of the proposed method for selecting diagnostic positions has been validated. It is recommended to avoid collecting RS data from areas near the leaf margins, main veins, and the leaf base when diagnosing early nutrient deficiencies in plants to enhance diagnostic accuracy. Similarity (geometry) ; Nutrient ; Raman spectroscopy ; Mathematics ; Pattern recognition (psychology) ; Computer science ; Biological system ; Artificial intelligence ; Biology ; Ecology ; Physics ; Image (mathematics) ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Faruk Alpay | Hamdi Alakkad
Source: ArXiv.org (preprint, 2025-08-19)
Field: Materials Science — Materials Chemistry

Calibrated Semantic Diffusion: A p-Laplacian Synthesis with Learnable Dissipation, Quantified Constants, and Graph-Aware Calibration We develop a calibrated diffusion framework by synthesizing three established concepts: linear Laplacian smoothing, nonlinear graph p-Laplacian flows, and a learnable dissipation term derived from a strongly convex potential. This synthesis provides a general model for graph-based diffusion with controllable dynamics. Our key theoretical results include a quantified two-regime decay analysis for $p>2$, which provides stronger, p-dependent transient bounds not captured by standard ISS templates, and the first formalization of a "non-synonymy" impossibility principle, which proves that fixed-parameter models cannot meet universal performance targets across graphs with varying spectral properties. To address this, we propose a constructive calibration algorithm (SGPS) with formal guarantees for achieving target rates and mass. We derive explicit, closed-form lower bounds for the graph p-gap on canonical graphs a notable improvement over prior implicit estimates and provide sharp constants for discrete-time and stochastic stability, including a contextualized restatement of the necessary and sufficient Euler step-size and a strengthened analysis of the stochastic noise floor. Illustrative, small-scale empirical validations confirm the tightness of key theoretical bounds. Calibration ; Laplacian matrix ; Nonlinear system ; Graph ; Constructive ; Laplace operator ; Impossibility ; Noise (video) ; Term (time) Machine Learning in Materials Science
Authors: Leandro Chaves Fonseca | Fernando Cunha Peixoto | Jakler Nichele
Source: Research Square (preprint, 2025-10-17)
Field: Materials Science — Materials Chemistry

Yaw-induced transitions in ballistic limit and fracture mechanisms in steel plate perforation Ballistic limit ; Projectile ; Fracture (geology) ; Perforation ; Limit (mathematics) ; Oblique case High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Laser-Plasma Interactions and Diagnostics
Authors: Tong Xia | Zicheng Wang | Teja Lakamraju | Danielle E. Haslam | Saravanan Thangarajan | David T. Wong | Liming Liang | Kaumudi Joshipura | Meir J. Stampfer | Frank B. Hu | Kyu Ha Lee | Shilpa N Bhupathiraju
Source: Frontiers in Endocrinology (article, 2025-12-04)
Field: Chemistry — Spectroscopy

Multi-fluid, multi-omics signatures of insulin resistance and incident type 2 diabetes among Puerto Rican adults Plasma proteomic signatures provided the strongest T2D prediction. Adding saliva data did not improve predictive performance of plasma data. Type 2 diabetes ; Insulin resistance ; Puerto rican ; Saliva ; Diabetes mellitus ; Type 2 Diabetes Mellitus Advanced Proteomics Techniques and Applications ; Genetic Associations and Epidemiology ; Diabetes and associated disorders
Authors: Ellis Capp | Marco Pontin | Peter Walters | Perla Maiolino
Source: ArXiv.org (preprint, 2025-02-06)
Field: Materials Science — Polymers and Plastics

Soft and Highly-Integrated Optical Fiber Bending Sensors for Proprioception in Multi-Material 3D Printed Fingers Accurate shape sensing, only achievable through distributed proprioception, is a key requirement for closed-loop control of soft robots. Low-cost power efficient optoelectronic sensors manufactured from flexible materials represent a natural choice as they can cope with the large deformations of soft robots without loss of performance. However, existing integration approaches are cumbersome and require manual steps and complex assembly. We propose a semi-automated printing process where plastic optical fibers are embedded with readout electronics in 3D printed flexures. The fibers become locked in place and the readout electronics remain optically coupled to them while the flexures undergo large bending deformations, creating a repeatable, monolithically manufactured bending transducer with only 10 minutes required in total for the manual embedding steps. We demonstrate the process by manufacturing multi-material 3D printed fingers and extensively evaluating the performance of each proprioceptive joint. The sensors achieve 70% linearity and 4.81° RMS error on average. Furthermore, the distributed architecture allows for maintaining an average fingertip position estimation accuracy of 12 mm in the presence of external static forces. To demonstrate the potential of the distributed sensor architecture in robotics applications, we build a data-driven model independent of actuation feedback to detect contact with objects in the environment. 3d printed ; Proprioception ; Bending ; Materials science ; Optical fiber ; Fiber ; Composite material ; Computer science ; Biomedical engineering ; Engineering ; Telecommunications ; Physical medicine and rehabilitation ; Medicine Textile materials and evaluations
Authors: Zhang, Hongliang | Chen,  Mei | Chen,  Xiangrong | Ma,  Na | Wei,   Xiang
Source: Journal of Combinatorial Mathematics and Combinatorial Computing (article, 2025-03-30)
Field: Chemistry — Analytical Chemistry

Design of a real-time monitoring system for blending ratio and purity of tobacco for hyperspectral imaging In recent years, hyperspectral imaging technology has a large application prospect in quality inspection in the tobacco industry.The study is based on near infrared spectroscopy technology and partial least squares regression method to establish mathematical analysis model of tobacco adulteration ratio of four components, such as expanded tobacco, stalked tobacco, large threaded tobacco and small threaded tobacco, and carry out internal and external inspection.At the same time, TLBO algorithm is used in the optimization of ELM tobacco purity grade determination model to realize the design of tobacco purity monitoring method, and then build the real-time monitoring system of tobacco blending ratio and purity.Tobacco with different purity grades were selected for experimental testing and model comparison analysis.The results show that the constructed PLS model can accurately predict the adulteration content of the four components in tobacco, and the correlation coefficients between the predicted and actual values are above 0.95 (p < 0.01), and the relative deviation of the prediction is below 3%.The accuracy of the TLBO-ELM model for identifying tobacco with different grades of purity is 88%, and the classification accuracy in the validation set is improved by 9.32% compared with the ELM model, which is within the acceptable range.It shows a better classification effect than PLS-DA in an acceptable range, which proves that the proposed method can be used for discriminating and monitoring the purity of tobacco.The monitoring system in this paper can be used in the analysis of tobacco blending ratio and purity detection. Hyperspectral imaging ; Process engineering ; Environmental science ; Artificial intelligence ; Computer science ; Computer vision ; Remote sensing ; Engineering ; Geology Spectroscopy and Chemometric Analyses
Authors: Washington Duarte Silva da Silva | Joielan Xipaia dos Santos | Tawani Lorena Naide Acosta | Deivison Venício Souza | Ana Paula Souza Ferreira | Pamella Carolline Marques dos Reis Reis | Leonardo Pequeno Reis | Helena Cristina Vieira | Graciela Inês Bolzón de Muñiz | Silvana Nisgoski
Source: Forests (article, 2025-06-11)
Field: Chemistry — Organic Chemistry

Near-Infrared Spectroscopy and Machine Learning for Wood Species Discrimination in an Amazon Floodplain Forest Management Area This study analyzes near-infrared (NIR) spectral characteristics of the wood of Hevea spruceana (Benth.) Müll. Arg., Hura crepitans L., Ocotea cymbarum Kunth, and Pseudobombax munguba (Mart.) Dugand from an Amazon floodplain forest area located in the Mamirauá Sustainable Development Reserve, aiming at their discrimination using artificial intelligence. The samples were collected as increment cores, from which NIR spectra were randomly collected in the transversal anatomical surface and compared. Principal component analysis (PCA) was applied to explore variation patterns in the data. Additionally, the classifier support vector machine algorithm, partial least squares–discriminant analysis (PLS-DA), and k-nearest neighbors regression were used to evaluate the accuracy in distinguishing the woods based on the NIR data. The results indicate similar spectral behavior among the species, with differences in absorbance intensities. PCA revealed a greater tendency for samples of the same species to cluster together, with Ocotea cymbarum showing the highest tendency for grouping. Among the classifiers, PLS-DA achieved the highest accuracy (98%). We can conclude that NIR spectroscopy combined with artificial intelligence classifiers has the potential to distinct wood species from the Amazon floodplain forest analyzed. Amazon rainforest ; Floodplain ; Environmental science ; Agroforestry ; Remote sensing ; Geography ; Ecology ; Biology ; Cartography Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Ashok Kumar Jha | Alan Tin‐Lun Lam
Source:  (preprint, 2025-05-28)
Field: Materials Science — Materials Chemistry

Mathematical Modeling of CO₂ Emissions from HPC: Novel Insights into the Environmental Impact of AI Growth in Future Decades This research was conducted as part of the HiMCM Mathematical Modeling challenge and hence includes elements from the competition deviating from the traditional structure of a research paper.We, Arsh and Anderson, Environmental science ; Biochemical engineering ; Engineering Catalytic Processes in Materials Science