jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fen Wang | Hao Pan | Wei Mao | Dan Wang
Source: Heliyon (review, 2024-07-01)
Field: Materials Science — Materials Chemistry

Optimizations of luminescent materials for white light emitting diodes toward healthy lighting White light emitting diodes (wLEDs) have been widely used as the green lighting sources. The commercial wLEDs devices are mainly achieved through the combination of blue emission chips and yellow phosphors, which offer advantages of high efficiency and long lifetime. However, the color rendering index (CRI) of traditional wLEDs is low due to the lack of red components. In recent years, with the improvement of the quality of life, a lot of efforts have been paid to improve the performance of wLEDs devices related to CRI, correlated color temperature, light uniformity, luminous flux, etc. In this article, we summarize the recent advances on the optimization of wLEDs toward healthy lighting. Brief introductions on the fundamentals of healthy effect of lighting are presented, followed by discussions of current methods to realize wLEDs devices. Special overviews on strategies for luminescent materials of wLEDs in recent years are presented. The opportunities and challenges in the future development of wLEDs lighting devices are also discussed. White light ; Light-emitting diode ; Optoelectronics ; Luminescence ; Diode ; White (mutation) ; Materials science ; Engineering physics ; Optics ; Environmental science ; Physics ; Biology Luminescence Properties of Advanced Materials ; Organic Light-Emitting Diodes Research ; Optical Imaging and Spectroscopy Techniques
Authors: Richard A. Sykes | Natalie T. Johnson | Christopher J. Kingsbury | J. A. Harter | Andrew G. P. Maloney | Isaac J. Sugden | Suzanna C. Ward | Ian Bruno | Stewart A. Adcock | P.A. Wood | Patrick McCabe | Alexandru A. Moldovan | Francis Atkinson | Ilenia Giangreco | Jason C. Cole
Source: Journal of Applied Crystallography (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

What has scripting ever done for us? The CSD Python application programming interface (API) Since its first release in 2016, the Cambridge Structural Database Python application programming interface (CSD Python API) has seen steady uptake within the community that the Cambridge Crystallographic Data Centre serves. This article reviews the history of scripting interfaces, demonstrating the need, and then briefly outlines the technical structure of the API. It describes the reach of the CSD Python API, provides a selected review of its impact and gives some illustrative examples of what scientists can do with it. The article concludes with speculation as to how such endeavours will evolve over the next decade. Python (programming language) ; Scripting language ; Computer science ; Programming language ; Application programming interface ; Interface (matter) ; Software engineering ; World Wide Web ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Enzyme Structure and Function
Authors: Florian Sammüller | Sophie Hermann | Matthias Schmidt
Source: Journal of Physics Condensed Matter (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Why neural functionals suit statistical mechanics We describe recent progress in the statistical mechanical description of many-body systems via machine learning combined with concepts from density functional theory and many-body simulations. We argue that the neural functional theory by Sammüller<i>et al</i>(2023<i>Proc. Natl Acad. Sci.</i><b>120</b>e2312484120) gives a functional representation of direct correlations and of thermodynamics that allows for thorough quality control and consistency checking of the involved methods of artificial intelligence. Addressing a prototypical system we here present a pedagogical application to hard core particle in one spatial dimension, where Percus' exact solution for the free energy functional provides an unambiguous reference. A corresponding standalone numerical tutorial that demonstrates the neural functional concepts together with the underlying fundamentals of Monte Carlo simulations, classical density functional theory, machine learning, and differential programming is available online athttps://github.com/sfalmo/NeuralDFT-Tutorial. Statistical mechanics ; Statistical physics ; Artificial neural network ; Physics ; Classical mechanics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Neural Networks and Applications ; Protein Structure and Dynamics
Authors: Haobo Guo | Pooria Lesani | Hala Zreiqat | Elizabeth J. New
Source: Sensors & Diagnostics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A fluorescent sensor array based on carbon dots for the accurate determination of pH The combination of machine learning techniques with a set of carbon dot (CD) based pH sensors enables the identification of two CDs that can accurately determine pH values. Fluorescence ; Carbon fibers ; Nanotechnology ; Analytical Chemistry (journal) ; Materials science ; Optoelectronics ; Chemistry ; Chromatography ; Optics ; Physics Carbon and Quantum Dots Applications ; Advanced biosensing and bioanalysis techniques ; Electrochemical sensors and biosensors
Authors: Qi Wang | Zhang Longfei | Zhen-Ya Zhou | Hai‐Bin Yu
Source: Science Advances (article, 2024-05-23)
Field: Materials Science — Materials Chemistry

Predicting the pathways of string-like motions in metallic glasses via path-featurizing graph neural networks String-like motions (SLMs)-cooperative, "snake"-like movements of particles-are crucial for dynamics in diverse glass formers. Despite their ubiquity, questions persist: Do SLMs prefer specific paths? If so, can we predict these paths? Here, in Al-Sm glasses, our isoconfigurational ensemble simulations reveal that SLMs do follow certain paths. By designing a graph neural network (GNN) to featurize the environment around directional paths, we achieve a high-fidelity prediction of likely SLM pathways, solely based on the static structure. GNN gauges a structural measure to assess each path's propensity to engage in SLMs, akin to a "softness" metric, but for paths rather than for atoms. Our GNN interpretation reveals the critical role of the bottleneck zone along a path in steering SLMs. By monitoring "path softness," we elucidate that SLM-favored paths transit from fragmented to interconnected upon glass transition. Our findings reveal that, beyond atoms or clusters, glasses have another dimension of structural heterogeneity: "paths." Bottleneck ; String (physics) ; Path (computing) ; Computer science ; Graph ; Statistical physics ; Biological system ; Theoretical computer science ; Physics ; Theoretical physics ; Biology Material Dynamics and Properties ; Metallic Glasses and Amorphous Alloys ; Glass properties and applications
Authors: Kausik Hira | Mohd Zaki | Dhruvil Sheth | Mausam Mausam | N. M. Anoop Krishnan
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Reconstructing the materials tetrahedron: challenges in materials information extraction Quantifying challenges towards information extraction from scientific articles to complete the materials science tetrahedron. Tetrahedron ; Extraction (chemistry) ; Information extraction ; Data science ; Computer science ; Nanotechnology ; Materials science ; Information retrieval ; Crystallography ; Chemistry ; Chromatography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Material Selection and Properties
Authors: Wendimu Fanta Gemechu | W. Sitek | Gilmar Ferreira Batalha
Source: Applied Sciences (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Improving Hardenability Modeling: A Bayesian Optimization Approach to Tuning Hyperparameters for Neural Network Regression This study investigates the application of regression neural networks, particularly the fitrnet model, in predicting the hardness of steels. The experiments involve extensive tuning of hyperparameters using Bayesian optimization and employ 5-fold and 10-fold cross-validation schemes. The trained models are rigorously evaluated, and their performances are compared using various metrics, such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results provide valuable insights into the models’ effectiveness and their ability to generalize to unseen data. In particular, Model 4208 (8-85-141-1) emerges as the top performer with an impressive RMSE of 1.0790 and an R2 of 0.9900. The model, which was trained with different datasets for nearly 40 steel grades, enables the prediction of hardenability curves, but is limited to the range of the training dataset. The research paper contains an illustrative example that demonstrates the practical application of the developed model in determining the hardenability band for a specific steel grade and shows the effectiveness of the model in predicting and optimizing heat treatment results. Hyperparameter ; Computer science ; Artificial neural network ; Machine learning ; Hardenability ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Machine Learning and Data Classification ; Industrial Vision Systems and Defect Detection
Authors: Yuxuan Xu | Guo Chen | Du Fei | Ming Li | Liangfei Wu | Deyuan Yao | Xiaodong Liu | Junfeng Ding | Zhi Zeng | Ruibin Liu | Hai‐Qing Lin | Xianlong Wang
Source: Science Advances (article, 2024-09-27)
Field: Materials Science — Materials Chemistry

Free-standing cubic gauche nitrogen stable at 760 K under ambient pressure Cubic gauche nitrogen (cg-N) has received wide attention for its exceptionally high energy density and environmental friendliness. However, traditional synthesis methods for cg-N predominantly rely on high-pressure techniques or the utilization of nanoconfined effects using highly toxic and sensitive sodium azide as precursor, which substantially restrict its practical application. On the basis of the first-principles simulations, we found that adsorption of potassium on the cg-N surface exhibits superior stabilization compared to sodium. Then, we chose safer potassium azide as precursor for synthesizing cg-N. Through plasma-enhanced chemical vapor deposition treatment, the free-standing cg-N was successfully synthesized without the need for high-pressure and nanoconfined effects. It demonstrated excellent thermal stability up to 760 K, and then rapid and intense thermal decomposition occurred, exhibiting typical thermal decomposition behaviors of high-energy-density materials. The explosion parameters were also measured using laser-induced plasma spectroscopy. Our work has substantially promoted the practical application of cg-N as HEDMs. Sodium azide ; Nitrogen ; Azide ; Thermal stability ; Materials science ; Thermal decomposition ; Potassium ; Decomposition ; Sodium ; Ambient pressure ; Chemical engineering ; Chemistry ; Organic chemistry ; Thermodynamics Diamond and Carbon-based Materials Research ; Catalytic Processes in Materials Science ; Energetic Materials and Combustion
Authors: Sourav Nath | Abhijit Datta | Arijit Das | Suman Adhikari
Source: International Journal of experimental research and review (article, 2024-03-30)
Field: Materials Science — Biomaterials

Metal-Based Drugs in Cancer Therapy Metal-based drugs have emerged as pivotal therapeutics in cancer therapy, enlightening a path toward innovative and effective treatment strategies. Platinum-based therapeutics, notably cisplatin, carboplatin, and oxaliplatin, have transformed the landscape of cancer treatment, setting the stage for the development of next-generation metal-based compounds. Delving into the principles governing the design of metal-based drugs, this article navigates through their intricate coordination chemistry, targeted delivery approaches, and mechanisms of action. Notably, metal-based compounds form covalent bonds with DNA, disrupting vital cellular processes and inducing apoptosis in cancerous tissues. Even though contemporary chemotherapy as well as radiotherapeutic methods have greatly increased patient survival rates, disease recurrence still represents a fatal danger. The probability of metastasis and the drug resistance are increased by the incomplete clearance of neoplastic tissues from the body. This review explores the compelling journey of metal-based compounds, from their historical significance in ancient remedies to their pivotal role in modern oncology, and also discusses the recent advancements and emerging trends that promise to shape the future of metallodrugs. The shining promise of metal-based drugs in cancer remedies holds the potential to revolutionize treatment paradigms, offering hope and resilience in the ongoing battle against one of humanity's most relentless adversaries. Cancer therapy ; Medicine ; Cancer ; Internal medicine Nanoparticle-Based Drug Delivery
Authors: Carla Ivonne La Fuente Arias | Chelo González‐Martínez | Amparo Chiralt
Source: Polymers (article, 2024-06-02)
Field: Materials Science — Biomaterials

Active Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) Films Containing Phenolic Compounds with Different Molecular Structures To obtain more sustainable and active food packaging materials, PHBV films containing 5% wt. of phenolic compounds with different molecular structures (ferulic acid, vanillin, and catechin) and proved antioxidant and antimicrobial properties were obtained by melt blending and compression molding. These were characterized by their structural, mechanical, barrier, and optical properties, as well as the polymer crystallization, thermal stability, and component migration in different food simulants. Phenolic compounds were homogenously integrated within the polymer matrix, affecting the film properties differently. Ferulic acid, and mainly catechin, had an anti-plasticizing effect (increasing the polymer glass transition temperature), decreasing the film extensibility and the resistance to breaking, with slight changes in the elastic modulus. In contrast, vanillin provoked a plasticizing effect, decreasing the elastic modulus without notable changes in the film extensibility while increasing the water vapor permeability. All phenolic compounds, mainly catechin, improved the oxygen barrier capacity of PHBV films and interfered with the polymer crystallization, reducing the melting point and crystallinity degree. The thermal stability of the material was little affected by the incorporation of phenols. The migration of passive components of the different PHBV films was lower than the overall migration limit in every simulant. Phenolic compounds were released to a different extent depending on their thermo-sensitivity, which affected their final content in the film, their bonding forces in the polymer matrix, and the simulant polarity. Their effective release in real foods will determine their active action for food preservation. Catechin was the best preserved, while ferulic acid was the most released. Ferulic acid ; Materials science ; Polymer ; Glass transition ; Vanillin ; Thermal stability ; Chemical engineering ; Crystallinity ; Crystallization ; Active packaging ; Food packaging ; Organic chemistry ; Composite material ; Chemistry ; Chromatography ; Food science biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Polymer composites and self-healing
Authors: C. Vairavan | B. M. Kamble | A. G. Durgude | Snehal R Ingle | K. Pugazenthi
Source: Journal of Experimental Agriculture International (review, 2024-01-17)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging of Soil and Crop: A Review The remote sensing is one of the precision technologies, can be used to monitor and assess the target area or object such as soil, crop, and water. Hyperspectral imaging (HSI), also known as imaging spectrometry or hyperspectral remote sensing, is a combined technique of spectroscopy and imaging system for sensing spectral information of an area or object. It involves capturing images of an object using multiple distinct optical bands that cover a wide range of the electromagnetic spectrum (350-2500 nm). The hyperspectral bands are continuous, narrow, and contagious and contain hundreds and thousands of numbers. Hyperspectral remote sensing is particularly valuable for gathering precise and up-to-date information necessary for agricultural planning and precision farming. HSI technology is the employment of hyperspectral sensors aids in analyzing soil physical (bulk density, texture, water content), chemical (pH, EC, SOC, and macro and micro nutrients), biological (SOM) properties and helps to categorize different crop varieties, identify pests and diseases, and assess crop yield and water stress in plants. The spectral reflectance of soil is affected by its properties such as mineral composition (Fe oxides), organic matter, soil moisture, and texture. For example, the spectral reflectance will be more if soil has less organic matter. The chemical bonds of soil molecules interact with the electromagnetic spectrum, and produce distinct pattern of reflectance. But the data collected from hyperspectral imaging are required big storage due to its large amount of data and finding the most appropriate hyperspectral image classification algorithm is a challenging task. So, these problems should be solved in future and national soil spectral library is needed for calibration of models which helps for efficient use of hyperspectral imaging technology. Hyperspectral imaging ; Remote sensing ; Environmental science ; Precision agriculture ; Soil texture ; Spectral signature ; Electromagnetic spectrum ; Imaging spectrometer ; Computer science ; Spectrometer ; Soil science ; Soil water ; Agriculture ; Geology ; Geography Spectroscopy and Chemometric Analyses
Authors: Tuba Ayça Tunca Arın | Ondřej Sedláček
Source: Biomacromolecules (review, 2024-08-16)
Field: Materials Science — Materials Chemistry

Stimuli-Responsive Polymers for Advanced <sup>19</sup>F Magnetic Resonance Imaging: From Chemical Design to Biomedical Applications Fluorine magnetic resonance imaging (<sup>19</sup>F MRI) is a rapidly evolving research area with a high potential to advance the field of clinical diagnostics. In this review, we provide an overview of the recent progress in the field of fluorinated stimuli-responsive polymers applied as <sup>19</sup>F MRI tracers. These polymers respond to internal or external stimuli (e.g., temperature, pH, oxidative stress, and specific molecules) by altering their physicochemical properties, such as self-assembly, drug release, and polymer degradation. Incorporating noninvasive <sup>19</sup>F labels enables us to track the biodistribution of such polymers. Furthermore, by triggering polymer transformation, we can induce changes in <sup>19</sup>F MRI signals, including attenuation, amplification, and chemical shift changes, to monitor alterations in the environment of the tracer. Ultimately, this review highlights the emerging potential of stimuli-responsive fluoropolymer <sup>19</sup>F MRI tracers in the current context of polymer diagnostics research. Polymer ; Context (archaeology) ; Biodistribution ; Magnetic resonance imaging ; Nanotechnology ; Materials science ; Chemistry ; Nuclear magnetic resonance ; Organic chemistry ; Physics Lanthanide and Transition Metal Complexes ; Luminescence and Fluorescent Materials ; Advanced NMR Techniques and Applications
Authors: Suvi‐Tuuli Akkanen | Juan Arias Muñoz | Aleksei V. Emelianov | Kamila K. Mentel | Juhani V. Tammela | Mikko Partanen | Susobhan Das | Faisal Ahmed | Mika Pettersson | Zhipei Sun
Source: Advanced Functional Materials (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Enhanced Nonlinear Optical Responses in MoS<sub>2</sub> via Femtosecond Laser‐Induced Defect‐Engineering Abstract 2D materials are a promising platform for applications in many fields as they possess a plethora of useful properties that can be further optimized by careful engineering, for example, by defect introduction. While reliable high‐yield defect engineering methods are in demand, most current technologies are expensive, harsh, or non‐deterministic. Optical modification methods offer a cost‐effective and fast mechanism to engineer the properties of 2D materials at any step of the device fabrication process. In this paper, the nonlinear optical responses of mono‐, bi‐, and trilayer molybdenum disulfide (MoS 2 ) flakes are enhanced by deterministic defect‐engineering with a femtosecond laser. A 50‐fold enhancement in the third harmonic generation (THG) and a 3.3‐fold increase in the second harmonic generation (SHG) in the optically modified areas is observed. The enhancement is attributed to resonant SHG and THG processes arising from optically introduced mid‐band gap defect states. These results demonstrate a highly controllable, sub‐micrometer resolution tool for enhancing the nonlinear optical responses in 2D materials, paving the way for prospective future applications in optoelectronics, quantum technologies, and energy solutions. Materials science ; Femtosecond ; Nonlinear optical ; Laser ; Optoelectronics ; Nonlinear optics ; Nonlinear system ; Nanotechnology ; Engineering physics ; Optics 2D Materials and Applications ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Samira Shokri | Nabi Shariatifar | Ebrahim Molaee-Aghaee | Gholamreza Jahed Khaniki | Parisa Sadighara | Mohammad Ali Faramarzi
Source: Scientific Reports (article, 2024-01-02)
Field: Chemistry — Analytical Chemistry

Modeling sunset yellow removal from fruit juice samples by a novel chitosan-nickel ferrite nano sorbent Analysis of food additives is highly significant in the food industry and directly related to human health. This investigation into the removal efficiency of sunset yellow as an azo dye in fruit juices using Chitosan-nickel ferrite nanoparticles (Cs@NiFe<sub>2</sub>O<sub>4</sub> NPs). The nanoparticles were synthesized and characterized using various techniques. The effective parameters for removing sunset yellow were optimized using the response surface methodology (RSM) based on the central composite design (CCD). Under the optimum conditions, the highest removal efficiency (94.90%) was obtained for the initial dye concentration of 26.48 mg L<sup>-1</sup> at a pH of 3.87, a reaction time of 67.62 min, and a nanoparticle dose of 0.038 g L<sup>-1</sup>. The pseudo-second-order kinetic model had a better fit for experimental data (R<sup>2</sup> = 0.98) than the other kinetic models. The equilibrium adsorption process followed the Freundlich isotherm model with a maximum adsorption capacity of 212.766 mg g<sup>-1</sup>. The dye removal efficiency achieved for industrial and traditional fruit juice samples (91.75% and 93.24%), respectively, confirmed the method's performance, feasibility, and efficiency. The dye adsorption efficiency showed no significant decrease after five recycling, indicating that the sorbent has suitable stability in practical applications. variousThe synthesized nanoparticles can be suggested as an efficient sorbent to remove the sunset yellow dye from food products. Chitosan ; Sorbent ; Nickel ; Fruit juice ; Sunset ; Ferrite (magnet) ; Nano- ; Food science ; Materials science ; Chemistry ; Chemical engineering ; Metallurgy ; Biochemistry ; Composite material ; Physics ; Adsorption ; Engineering ; Organic chemistry ; Optics Dye analysis and toxicity ; Biochemical Analysis and Sensing Techniques