jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Selin Gökçe | S. Sevinç Şengör
Source: Water (article, 2025-02-11)
Field: Chemistry — Inorganic Chemistry

Reactive Transport Modeling of Uranium in Subsurface: Impact of Field-Scale Heterogeneity and Biogeochemical Dynamics Understanding the fate of contaminants in heterogeneous aquifer systems is crucial to explain their transport behavior. Although it has been proven that heterogeneity has a significant control over the quantification of these processes, the extent of this impact is yet to be studied. The unique contribution of this work lies in the assessment of field-scale physical and chemical heterogeneity in modeling reactive transport processes in the subsurface. The main objective of this study is to investigate the impact of physical and chemical heterogeneity in understanding biogeochemical processes of contaminants in the subsurface environment, coupled with advective and dispersive transport in situ with mixing limitations. This study is particularly focused on an example of uranium, where especially coupled bioreduction and reoxidation processes in the presence of Fe (III) hydroxides are considered. For this purpose, 2D numerical biogeochemical reactive transport models are developed to simulate the fate and transport of uranium in a heterogeneously distributed subsurface. Results have shown that neglecting spatial heterogeneity might lead to an overestimation of uranium bioreduction, where physical heterogeneity has been observed to have a greater impact than chemical heterogeneity in the absence of adsorption reactions. On the other hand, when adsorption of uranium is included, the significance of chemical heterogeneity is more pronounced. Thus, when potential adsorption of contaminants is ignored or if chemical heterogeneity is ignored in the presence of adsorption reactions, the contaminant concentrations might be underestimated. The underestimation is more pronounced in low hydraulic conductivity zones due to the mixing limitations for soluble compounds, whereas for immobile phase interactions, high hydraulic conductivity regions became significant. The impact of U(IV) reoxidation process is also more pronounced in the presence of chemical heterogeneity and particularly enhanced in the zones with the highest mixing. The findings of this study can shed light on identifying the conditions that necessitate the accurate characterizations of physical and chemical heterogeneity in predicting contaminant transport with mixing limitations subject to competing biogeochemical reactions in the natural subsurface. Biogeochemical cycle ; Environmental science ; Scale (ratio) ; Field (mathematics) ; Uranium ; Atmospheric sciences ; Hydrology (agriculture) ; Soil science ; Environmental chemistry ; Geology ; Chemistry ; Physics ; Geotechnical engineering ; Nuclear physics Radioactive element chemistry and processing ; Groundwater flow and contamination studies ; Atmospheric and Environmental Gas Dynamics
Authors: Shreeniket Pawar | Hayden Carlton | Yash Sharad Lad | Lyndsey Werhane | Ma’moun Abu-Ayyad | Preethi Korangath | Robert Ivkov | Anilchandra Attaluri
Source: Journal of Medical Devices (article, 2025-11-10)
Field: Materials Science — Biomaterials

Verification and Validation of an Autotuning Proportional–Integral–Derivative Controller for Spatially Confined Magnetic Particle Hyperthermia We have previously verified the capabilities of a prototype magnetic nanoparticle (MNP) heater (called HYPER) that can perform spatially confined heating; however, the design lacked temperature control capabilities. In this work, we designed, verified, and validated a relay-based autotuning proportional-integral-derivative (PID) controller to be used with the HYPER during in vivo experiments. The PID controller is an autotuning, relay-based controller with several design constraints: The controller must: (1) maintain tumor temperature within hyperthermic range of 41-46 °C; (2) rise time ≤ 5 min; (3) steady-state temperature must be within ±0.5 °C of the setpoint; (4) standard deviation of steady-state temperature within ±0.5 °C; and (5) temperature overshoot within 5%. The relay-based autotuning PID controller was designed in LabVIEW<sup>®</sup> with real-time thermal dose monitoring. Verification experiments were performed by heating aqueous suspensions of high-performance iron oxide MNPs. For validation, we injected the MNPs into tumor-bearing mice and analyzed the ability of the controller to maintain in vivo temperature. The results of the study show that controller was able to maintain the temperature within the hyperthermic range with a rise time ∼4 min and steady-state error ∼0.1 °C. Validation was performed on six mice, where four mice showed the temperature was maintained within design criteria and two mice partially met the design criteria. The autotuning controller can maintain the temperature within the design criteria and monitor thermal dose in real-time. PID controller ; Overshoot (microwave communication) ; Temperature control ; Controller (irrigation) ; Control theory (sociology) ; Open-loop controller ; Temperature measurement ; Thermal ; Range (aeronautics) Nanoparticle-Based Drug Delivery ; Ultrasound and Hyperthermia Applications ; Characterization and Applications of Magnetic Nanoparticles
Authors: M. Gebran | Ian Bentley | Rose Brienza | F. Paletou
Source: Open Astronomy (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Deep learning application for stellar parameter determination: III-denoising procedure Abstract In this third article in a series, we investigate the need of spectra denoising for the derivation of stellar parameters. We have used two distinct datasets for this work. The first one contains spectra in the range of 4,450–5,400 Å at a resolution of 42,000, and the second in the range of 8,400–8,800 Å at a resolution of 11,500. We constructed two denoising techniques, an autoencoder, and a principal component analysis. Using random Gaussian noise added to synthetic spectra, we have trained a neural network to derive the stellar parameters <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>T</m:mi> </m:mrow> <m:mrow> <m:mi mathvariant="normal">eff</m:mi> </m:mrow> </m:msub> </m:math> {T}_{{\rm{eff}}} , <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>log</m:mi> <m:mi>g</m:mi> </m:math> \log g , <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>v</m:mi> </m:mrow> <m:mrow> <m:mi mathvariant="normal">e</m:mi> </m:mrow> </m:msub> <m:mi>sin</m:mi> <m:mi>i</m:mi> </m:math> {v}_{{\rm{e}}}\sin i , <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi>ξ</m:mi> </m:mrow> <m:mrow> <m:mi mathvariant="normal">t</m:mi> </m:mrow> </m:msub> </m:math> {\xi }_{{\rm{t}}} , and [M/H] of the denoised spectra. We find that, independently of the denoising technique, the accuracy values of stellar parameters do not improve once we denoise the synthetic spectra. This is true with and without applying data augmentation to the stellar parameters neural network. Physics ; Combinatorics ; Analytical Chemistry (journal) ; Algorithm ; Mathematics ; Chemistry Spectroscopy and Chemometric Analyses ; Optical Polarization and Ellipsometry ; Blind Source Separation Techniques
Authors: Suresh Erannagari | D. Mallikarjuna Reddy
Source: Periodica Polytechnica Mechanical Engineering (article, 2025-05-09)
Field: Materials Science — Polymers and Plastics

An Experimental Investigation of Natural-fibre/Rubber Reinforced Bio-composites under Low-velocity Impact Analysis Natural fibers are renewable, inexpensive, and environmentally friendly; they have become a viable material for a wide range of uses. These composites consist of a natural fibre matrix modified with flax, hemp, sisal, jute, and bamboo. The aim of this present investigation is to explore the existing research, which focuses on examining the influence of natural fibre's absorbed energy and peak force on hemp rubber hemp (HRH), jute rubber jute (JRJ), hemp rubber jute (HRJ), and glass rubber glass (GRG) composite laminates under Low-velocity impact (LVI) analysis. The LVI test results affirmed that the HRH laminates have more energy absorption and elastic energy by 44.34%, 10.6%, and 80% as compared to other configurations due to their stiffness and robustness. The free vibrational analysis shows that the HRH samples have the highest natural frequency of 526.9 Hz compared to JRJ, HRJ, and GRG. The Field Emission Scanning Electron Microscopy (FESEM) discloses the failure mechanisms of the tested samples, including interlaminar failure, delamination, and matrix cracking due to the stress concentration in the impacted region. Composite material ; Materials science ; Natural rubber Natural Fiber Reinforced Composites ; High-Velocity Impact and Material Behavior
Authors: Minglei Lyu | Jiaqian Yu | Qing Yang | Yi Shen | Haoling Liu | Xuanjie Wang | Xiaolin Liu | Fang Shi | Xi Zou | Jinmiao Zha | Guangyu Li | Xufa Ma
Source: Toxics (article, 2025-11-28)
Field: Materials Science — Materials Chemistry

Titanium Dioxide Nanoparticles Aggravated the Developmental Neurotoxicity of Ammonia Nitrogen on Zebrafish Embryos Total ammonia nitrogen (TAN) is a common and potent neurotoxic pollutant in aquatic environments. Due to their strong adsorption capacity, titanium dioxide nanoparticles (n-TiO<sub>2</sub>), a widely used engineered material, can induce combined toxicity with multiple pollutants. However, the combined neurotoxicity of n-TiO<sub>2</sub> and TAN and its underlying mechanisms remain unclear. In this study, zebrafish embryos were exposed to TAN (0, 0.1, 1, 10 mg/L) and n-TiO<sub>2</sub> (100 µg/L) individually or in combination for 120 h. The results indicated that co-exposure to n-TiO<sub>2</sub> and TAN significantly increased the bioaccumulation of TAN in zebrafish embryos compared to TAN alone. Consequently, this led to exacerbated neurotoxicity, manifested as developmental impairments and abnormal motor behavior. Mechanistic investigations revealed that the co-exposure aggravated developmental neurotoxicity by triggering neuronal apoptosis and oxidative stress, disrupting the cholinergic and dopaminergic systems, and impairing neural and retinal development. Transcriptomic analysis further indicated that the co-exposure predominantly perturbed neurodevelopment, oxidative stress, and apoptosis. In conclusion, this study confirms that n-TiO<sub>2</sub> significantly amplifies TAN-induced neurodevelopmental toxicity by promoting its bioaccumulation and synergistically disrupting multiple neurophysiological processes. These findings provide crucial scientific evidence for assessing the combined ecological risks of nanomaterials and conventional pollutants. Neurotoxicity ; Zebrafish ; Bioaccumulation ; Toxicity ; Nanotoxicology ; Developmental toxicity ; Embryo ; Oxidative stress Nanoparticles: synthesis and applications ; Marine Biology and Environmental Chemistry ; Environmental Toxicology and Ecotoxicology
Authors: Dalton Boutwell | Axiu Cao | Allison S. Walker | Lauren E. Buchanan
Source: ChemRxiv (preprint, 2025-05-23)
Field: Materials Science — Materials Chemistry

Refining Structural Analysis of Proteins: Automated Methods to Measure Transition Dipole Strength of Single Residues Transition dipole strength (TDS) analysis enhances two-dimensional infrared (2D IR) spectroscopy by probing protein structural differences that frequency alone cannot resolve. However, its application has been limited to strong signals due to challenges with low signal-to-noise and large backgrounds in the linear optical density. Manual baseline correction can suffer from user error and produce large artifacts that obscure signals of interest. Here, we introduce a new approach incorporating automated baseline correction via the airPLS algorithm to improve the accuracy and precision of TDS calculations across broad spectral windows. Using human islet amyloid polypeptide, we demonstrate TDS analysis of a single 13C=18O-labeled residue, enabling a more precise measure of protein structure at the single-residue level. Further, airPLS-corrected TDS spectra can be calculated throughout amyloid aggregation to resolve potential intermediate structures. This work establishes TDS as a robust tool for investigating the structural dynamics of proteins and other complex macromolecular assemblies. Measure (data warehouse) ; Refining (metallurgy) ; Dipole ; Transition (genetics) ; Chemistry ; Computer science ; Data mining ; Biochemistry ; Physical chemistry ; Organic chemistry Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Gizem Simge Kılınç | Oğuz Uncu | İ. Eren | Neriman Bağdatlıoğlu
Source: Microchemical Journal (article, 2025-06-24)
Field: Chemistry — Analytical Chemistry

Adulteration of pomegranate molasses with sugar syrups: application of FTIR-ATR spectroscopy and chemometrics Chemometrics ; Sugar ; Fourier transform infrared spectroscopy ; Chemistry ; Food science ; Chromatography ; Chemical engineering Spectroscopy and Chemometric Analyses ; Pomegranate: compositions and health benefits ; Phytochemicals and Antioxidant Activities
Authors: Marco Caredda | Alessio Silvio Dedola | Massimo Pes | Margherita Addis
Source: Foods (article, 2025-06-27)
Field: Chemistry — Analytical Chemistry

The Use of NIR Spectroscopy and Chemometrics to Identify the Thermal Treatment of Milk in Fiore Sardo PDO Cheese to Detect Fraud The production of Fiore Sardo cheese is regulated by the specification of the Protected Designation of Origin (PDO), which aims to guarantee the specific area of production, the know-how of local producers, and the specific use of raw milk from Sarda sheep. The thermization of milk is a sub-pasteurization process that is commonly used in cheese-making to lower the bacterial load and increase the shelf life of the product; it is therefore a cause of non-compliance with the PDO specification of Fiore Sardo cheese, allowing producers to gain practical and economic advantages. In this work, NIR spectroscopy coupled with multivariate discriminant analysis was used to identify the thermal treatment of milk in Fiore Sardo cheese samples. Cheeses were produced using raw milk (38 °C), low-thermized milk (57 °C for 30 s), and high-thermized milk (68 °C for 30 s). The NIR spectra of the cheeses were used to build discriminant models for individuating the thermal treatment of the processed milk. The obtained discriminant models were able to correctly classify about 90% of the Fiore Sardo cheese samples. This method could be suitable as a screening technique to authenticate Fiore Sardo PDO cheese. Pasteurization ; Chemometrics ; Raw milk ; Linear discriminant analysis ; Food science ; Chemistry ; Mathematics ; Statistics ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: John R. Helliwell | C. Nave | Doletha M. E. Szebenyi
Source: Journal of Synchrotron Radiation (article, 2025-11-06)
Field: Materials Science — Materials Chemistry

50th anniversary of the Stanford SSRL synchrotron radiation and protein crystallography initiative We provide a historical introduction spanning the past 50 years of synchrotron radiation protein crystallography. We then provide a resume of current trends. These help us to celebrate the huge influence that synchrotron radiation, and now X-ray lasers, has had on the scope of protein crystallography. It has also accelerated the development of closely allied methods such as neutron protein crystallography, which has adopted the synchrotron Laue method as its own as well as developing monochromatic and time-of-flight methods. Also, the democratic access to central synchrotron facility beamlines has prompted similarly operated centres of electron cryo-microscopy and micro-electron diffraction. We offer our thoughts on the current trends across this scientific landscape. Synchrotron ; Synchrotron radiation ; Monochromatic color ; Scope (computer science) ; Protein crystallization Enzyme Structure and Function ; Protein Structure and Dynamics ; Advanced X-ray Imaging Techniques
Authors: Yuhai Yao | Jun Gu | Tianle Li | Ying Zhang | Zihao Jia | Qiao Zhao | Jingrui Zhang
Source: Processes (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Improved Firefly Algorithm-Optimized ResNet18 for Partial Discharge Pattern Recognition Within Small-Sample Scenarios The growing complexity of electrical infrastructure has elevated partial discharge (PD) detection to a crucial methodology for ensuring power system safety. Current PD pattern recognition approaches encounter persistent challenges in low-data scenarios, particularly regarding classification accuracy and model generalizability. This study develops a Firefly Algorithm with a Black Hole Mechanism-ResNet18 (FBH-ResNet18) framework that synergistically integrates the Firefly Algorithm with the Black Hole Mechanism (FBH algorithm) optimization with residual neural networks for PD signal classification using phase-resolved partial discharge (PRPD) mappings. A dedicated experimental platform first acquires PD signals through UHF sensors, which are subsequently converted into two-dimensional PRPD representations. The FBH algorithm systematically optimizes four key hyperparameters within the ResNet18 architecture during network training. The Black Hole Mechanism and improved population dynamics enhance optimization efficiency, resulting in more accurate hyperparameter tuning and improved model performance. Comparative evaluations demonstrate the enhanced performance of this parameter-optimized model against alternative configurations. Experimental results indicate that the improved ResNet18 achieves fast convergence and strong generalization on small-sample datasets, significantly enhancing recognition accuracy. During the first 80 generations of training, the classification accuracy reaches 89.11%, and in the final iteration, the model’s recognition accuracy increases to 92.55%, outperforming other models with accuracies generally below 90%. Additionally, the model shows excellent performance on the test set, with a loss function value of 0.250785, significantly lower than that of other models, indicating superior performance on small sample datasets. This research provides an effective solution for power cable fault diagnosis, offering high practical value. Hyperparameter ; Algorithm ; Computer science ; Firefly algorithm ; Convergence (economics) ; Artificial intelligence ; Machine learning ; Population ; Sample (material) ; Pattern recognition (psychology) ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Non-Destructive Testing Techniques
Authors: Hao Shi | Rui Yuan Wu | Shi He | Xue Yun Guo | Xiao Qing Chen | Yi Zheng | Bing Bing Zhu | Lei Zhang | Tie Jun Cui
Source: Advanced Science (article, 2025-12-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Programmable Transmissive Metasurface with Phase and Amplitude Manipulation Capability Digital and programmable metasurfaces provide novel approaches to connect electromagnetic (EM) physics with information science for flexible EM wave manipulation and information modulation. However, most recent studies on programmable metasurfaces have been focused on reflective phase coding and only provide limited regulation capability. To overcome these limitations, we propose a transmissive programmable metasurface with both amplitude and phase coding. The function of the metasurface can vary according to different control voltages. Typically, the metasurface achieves multiple-bit phase control and phase-amplitude joint control. To prove the unique features, we design and fabricate a prototype of the metasurface to verify the functions of vortex beam generation, hologram imaging, and beam forming. The measured results show good performance of the proposed metasurface, implying powerful EM manipulation capability and indicating a wide range of application potentials in imaging, data storage, wireless communications, and so on. Holography ; Amplitude ; Phase (matter) ; Beam steering ; Reconfigurability ; Phase control ; Phase response ; Field-programmable gate array Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Orbital Angular Momentum in Optics
Authors: Xuesong Zhu | Zezhong Ding | Mei Wang | Shanjian Liu | Yulong Chen | Chunwang Dong
Source: npj Science of Food (article, 2025-08-08)
Field: Chemistry — Analytical Chemistry

Convolutional neural network based on transfer learning for discriminating the fermentation degree of black tea Black tea is among the most widely consumed tea. The fermentation process is crucial for developing the flavor of black tea. Currently, many producers rely on personal experience to gauge fermentation, which can be inconsistent and subjective. Additionally, large models are impractical for use in production. Based on this, this paper introduces a lightweight convolutional neural network utilizing transfer learning to assess the fermentation level of black tea. Initially, we applied a model-based transfer learning strategy and conducted pre-training weight experiments to compare and select the student model and the teacher model. Next, we modified the loss function with PolyLoss and optimizer with AdamW for the student model. Finally, we performed a knowledge distillation experiment on the student model. Results indicated that the improved model's accuracy, precision, recall, and F1 improved by 0.0415, 0.0215, 0.0902, and 0.0645, respectively. This research offers technical assistance for digital production of black tea. Black tea ; Convolutional neural network ; Artificial intelligence ; Computer science ; Black box ; Transfer of learning ; Production (economics) ; Artificial neural network ; Process (computing) ; Machine learning ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Traditional Chinese Medicine Studies