jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Diya Pratish Chohan | Bipasa Dey | Arshia Tarkunde | Vaishnavi Vyas | Srijita De Sarkar | K. S. Babitha
Source: Journal of Cluster Science (article, 2024-09-06)
Field: Materials Science — Biomaterials

Advancing Autonomous Nanomedicine: Bridging the Gap from Concept to Potential Clinical Studies Abstract Autonomous nanomedicine, a burgeoning field within nanotechnology and biomedical sciences, is poised to revolutionize healthcare by eliminating the need for external intervention in targeted applications within the body. This article elucidates the promise and challenges of autonomous nanomedicine, emphasizing its ability to overcome the limitations of traditional methods such as chemotherapy and radiotherapy. Central to its efficacy are nano-sized carriers, which autonomously navigate the body to deliver therapeutic agents with precision and control. By integrating automated nanoscale tools into disease detection processes, this technology offers swift and personalized assessments, reshaping disease management paradigms. To advance the clinical translation of autonomous nanomedicine, rigorous preclinical studies are imperative. However, challenges persist in ensuring reproducibility and safety, hindering progress in clinical trials. This article examines current studies with potential clinical translation, shedding light on the regulatory and ethical considerations crucial for its safe implementation. As the field progresses, maintaining a balance between innovation and safety remains paramount for harnessing the full potential of autonomous nanomedicine while safeguarding patient well-being. Graphical Abstract Bridging (networking) ; Nanomedicine ; Nanochemistry ; Nanotechnology ; Management science ; Engineering ethics ; Computer science ; Materials science ; Engineering ; Nanoparticle Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Advanced biosensing and bioanalysis techniques
Authors: Mahdieh Sheikh | Hajar Shekarchizadeh
Source: Frontiers in Sustainable Food Systems (article, 2024-08-29)
Field: Chemistry — Analytical Chemistry

Visual detection of chlorpyrifos pesticide residues in foodstuffs using a colorimetric indicator based on copper nanoparticles Introduction Accurate and simple detection of pesticide residue amounts in food and crops is crucial for human health protection, food safety, environmental impact, trade, and consumer confidence. Methods Consequently, a colorimetric indicator based on copper nanoparticles (CuNPs) was developed for the detection of chlorpyrifos in this study. Results The prepared CuNPs-based colorimetric indicator exhibited color changes from black to green or blue in the presence of chlorpyrifos, visible to the naked eye and without the need for special equipment. An intriguing feature of this indicator was that its color turned blue for chlorpyrifos amounts below the maximum residue limit (MRL) and green for amounts exceeding the MRL. The complex formation of CuNPs with chlorpyrifos via the Cu-S bond, confirmed by FTIR, XPS, and XRD analysis, was responsible for the color change. Subsequently, the structure of CuNPs transitioned from a clustered to an aggregated state, as evident in the TEM images. The selectivity of the synthesized indicator against four other pesticides (glyphosate, malathion, diazinon, and imidacloprid) was investigated, revealing high selectivity against chlorpyrifos. Finally, the performance of the synthesized indicator was validated using real samples, demonstrating its high accuracy and sensitivity. Conclusion In summary, the prepared indicator represents a promising method for simple, cost-effective, accurate, and highly selective detection of chlorpyrifos. Chlorpyrifos ; Copper ; Pesticide ; Pesticide residue ; Environmental chemistry ; Chemistry ; Colorimetry ; Nanoparticle ; Chromatography ; Nanotechnology ; Materials science ; Biology ; Organic chemistry ; Ecology Dye analysis and toxicity ; Pesticide Residue Analysis and Safety
Authors: Vittorio Ferrara | Valeria Vetri | Bruno Pignataro | Delia Francesca Chillura Martino | Giuseppe Sancataldo
Source: International Journal of Biological Macromolecules (article, 2024-01-21)
Field: Materials Science — Biomaterials

Phasor-FLIM analysis of cellulose paper ageing mechanism with carbotrace 680 dye Ageing of paper is a complex process of great relevance for application purposes because of its widespread use as support for information storage in books and documents, and as common low-cost and green packaging material, to name a few. A key factor in paper ageing is the oxidation of cellulose, a macromolecule of natural origin that constitutes the main chemical component of paper. Such a complex process results in changes in the cellulose polymeric chains in chemical and structural properties. The scope of this work is to explore the effects of oxidation of cellulose as one of the principal mechanisms of ageing of paper using a fluorescence-based approach. To this aim, fluorescence-lifetime imaging microscopy (FLIM) measurements on pure cellulose samples stained using Carbotrace 680 dye were performed, and data were analyzed by phasor approach. The comparison with results from conventional techniques allowed to map paper microstructure as a function of the sample oxidation degree correlating the fluorescence-lifetime changes to cellulose oxidation. A two-step oxidation kinetics that produced specific modification in paper organization was highlighted indicating that FLIM measurements using Carbotrace 680 dye may provide a simple tool to obtain information on the oxidation process also adding spatial information at sub-micrometric scale. Cellulose ; Fluorescence ; Macromolecule ; Materials science ; Process (computing) ; Chemical engineering ; Chemistry ; Computer science ; Organic chemistry ; Biochemistry Advanced Cellulose Research Studies ; Cultural Heritage Materials Analysis ; Biosensors and Analytical Detection
Authors: Jandi Kim | Ji Hye Seo | Sang Heun Lee | Myunghee Cho | Hun Kwak | Ran Sae Cheon | Seungchan Cho | Sung Beom Cho | Minkee Kim | Yoon‐Seok Lee | Young Do Kim | Moonhee Choi
Source: Journal of the Korean Ceramic Society (article, 2024-01-19)
Field: Materials Science — Materials Chemistry

Systematic determination of the optimized Zr content of Ba(ZrxTi1-x)O3 with high dielectric constant at room temperature for high-voltage system application Abstract In this study, by replacing the B-site element in BaTiO 3 , a ferroelectric material, with an element with a larger ionic radius, a ferroelectric material with high permittivity at room temperature was synthesized. The powders were prepared by solid-state reaction to perform lattice substitution with Zr 4+ (0.72 Å), which has a larger ionic radius than Ti 4+ (0.605 Å). To perform effective solid-state reaction and better understand the correlation between variables, this study introduced a design of experiment (DOE) based on the orthogonal array (OA) method included in the PIAno software. By substituting 0.222 mol of Zr, which has a large ionic radius, the crystal structure was deformed through an effective diffuse phase transition (DPT), and this resulted in the largest improvement in permittivity at room temperature. In addition, the powder, which underwent solid-state reaction at 1300 °C, formed the densest structure during sintering, which established the conditions for realizing the best dielectric properties. These results can be utilized as a key material for improving the properties of passive devices used in high-voltage industrial systems in societies undergoing the fourth industrial revolution. Materials science ; Ionic radius ; Dielectric ; Permittivity ; Ferroelectricity ; Sintering ; Crystal structure ; Lattice constant ; Phase transition ; Relative permittivity ; Analytical Chemistry (journal) ; Composite material ; Optoelectronics ; Thermodynamics ; Ion ; Crystallography ; Optics ; Diffraction ; Organic chemistry Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; Multiferroics and related materials
Authors: Daning Zhang | Xinnan Zhai | Siyu Wang | Xuan Li | Pengjiang Xu | Haoxiang Zhao | Guanjun Zhang
Source: Journal of Applied Physics (article, 2024-09-26)
Field: Materials Science — Biomaterials

Study on mechanical and electrical properties improvement of insulating paper modified by cellulose nanocrystals The mechanical and dielectric properties of transformer insulating paper are key factors that require close attention to ensure optimal operation. In this study, a novel approach of enhancing properties by nanocellulose modification was proposed. To investigate the effect of doping, insulating paper with different doping mass fractions of cellulose nanocrystals (CNCs) was prepared, and multiple properties of the sample were characterized. By applying a doping concentration gradient, the non-monotonic trend in performance with doping levels was revealed, and the impact of doping concentration on the data variability was investigated. The effects of CNC doping on the surface pore size of the samples were analyzed by SEM (scanning electron microscopy) and a mercury intrusion method, and the regularity of CNC dispersion uniformity with doping concentration was investigated. The Brunauer, Emmett, and Teller model was employed to fit the water vapor adsorption isotherm data, providing an analysis of the impact of doping on hydrogen bond connections and the moisture adsorption properties. Tensile strength and Young's modulus were measured and analyzed using the Page model and the Halpin–Kardos model. The tensile strength of the 6 wt. % CNC-modified paper was increased by 6.91%. 10 wt. % CNC-modified paper had a Young's modulus increase of 11.98%. 3 wt. % CNC-modified paper has a 28.53% increase in the AC breakdown field strength and an 8.72% increase in the DC breakdown field strength. The influence of CNC on the dielectric properties of the insulating paper was discussed using the Havriliak–Negami (H–N) model. The results indicate that the introduction of CNC can effectively enhance the comprehensive performance of oil-immersed insulating paper. Nanocrystal ; Cellulose ; Materials science ; Nanotechnology ; Composite material ; Chemical engineering ; Engineering Advanced Cellulose Research Studies ; High voltage insulation and dielectric phenomena ; Material Properties and Processing
Authors: M Aparna | Geetha Lekshmi P. R.
Source: Journal of Advances in Biology & Biotechnology (article, 2024-11-02)
Field: Materials Science — Biomaterials

Chitosan Based Edible Coatings: Enhancing Shelf Life and Quality in Fruits and Vegetables Chitosan-based edible coatings have gained considerable attention as a sustainable solution for harvested fruits and vegetables which are highly prone to post harvest decay and quality deterioration during storage. Chitosan, derived from chitin exhibits biocompatibility, biodegradability and antimicrobial properties making it an ideal material for edible coatings. Edible coating act as effective barriers to moisture loss, gas exchange and microbial contamination thereby enhancing the quality and extending the shelf life of produce. Research has shown that chitosan coatings can significantly reduce respiration rates, inhibit enzymatic browning and preserve the sensory qualities of fruits and vegetables, contributing to a reduction in food waste and improved food security. The effectiveness of chitosan based coating is influenced by factors such as chitosan concentration, the addition of other natural additives and the methods of application. Studies have demonstrated that chitosan coatings alone or in combination with essential oils or plant extracts effectively reduce weight loss, maintain firmness and prevent spoilage in horticultural perishables. Additionally, nano-encapsulated chitosan formulations have been explored for their enhanced protective effects offering more uniform coatings with superior barrier properties. The application of chitosan coatings is expected to evolve with the integration of nanotechnology, the development of functionalized chitosan molecules and the use of biocompatible additives. Chitosan coatings have been successfully applied commercially, such as in the preservation of perishables illustrating their potential as a sustainable solution with practical benefits for both scientific advancement and industrial application These innovations will further improve the efficacy of coatings by enhancing their moisture and gas barrier properties along with antimicrobial and antioxidant properties. Additionally, sustainability concerns are driving research into eco-friendly sources of edible coating and chitosan is a promising alternative to synthetic or chemical ingredients or fungicides. As the technology advances, chitosan based edible coatings are likely to have increased commercial adoption due to its sustainable and biodegradable nature. However, Chitosan-based coatings face challenges with solubility, application consistency, scalability, cost, allergen concerns, and varying regulatory standards. Shelf life ; Chitosan ; Food science ; Quality (philosophy) ; Business ; Biotechnology ; Biology ; Physics Nanocomposite Films for Food Packaging
Authors: Liting Wang | Changjie Xu | Libo Feng | Wenjie Wang
Source: Sensors (review, 2024-11-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Survey of the Magnetic Anisotropy Detection Technology of Ferromagnetic Materials Based on Magnetic Barkhausen Noise Magnetic Barkhausen noise (MBN) is one of the most effective methods for determining the easy axis of ferromagnetic materials and for evaluating texture and residual stress in a nondestructive manner. MBN signals from multiple angles and different magnetization sections can be used to characterize magnetic anisotropy caused by various magnetization mechanisms. This paper reviews the development and application of magnetic anisotropy detection technology, and the MBN anisotropy models that take into account domain wall motion and magnetic domain rotation are analyzed thoroughly. Subsequently, the MBN anisotropy detection devices and detection methods are discussed, and the application of magnetic anisotropy detection technology in stress measurement and texture evaluation is reviewed. From the perspective of improving detection accuracy, the influence of composite mechanisms on magnetic anisotropy is analyzed. Finally, the opportunities and challenges faced by current magnetic anisotropy detection technology are summarized. The relevant conclusions obtained in this paper can be used to guide the MBN evaluation of magnetic anisotropy in ferromagnetic materials. Barkhausen effect ; Anisotropy ; Magnetic anisotropy ; Magnetic domain ; Materials science ; Condensed matter physics ; Ferromagnetism ; Magnetization ; Single domain ; Rotation (mathematics) ; Nuclear magnetic resonance ; Magnetic field ; Physics ; Computer science ; Optics ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Microstructure and Mechanical Properties of Steels
Authors: Jakub Nalepa | Łukasz Tulczyjew | Bertrand Le Saux | Nicolas Longépé | Bogdan Ruszczak | Agata M. Wijata | Krzysztof Smykała | Michał Myller | Michał Kawulok | Rıdvan Salih Kuzu | Frauke Albrecht | Caroline Arnold | Mohammad Hussein Alasawedah | Suzanne Angeli | D. Nobileau | Achille Ballabeni | Alessandro Lotti | Alfredo Locarini | Dario Modenini | Paolo Tortora | Michał Gumiela
Source: IEEE Geoscience and Remote Sensing Magazine (article, 2024-05-09)
Field: Chemistry — Analytical Chemistry

Estimating Soil Parameters From Hyperspectral Images: A benchmark dataset and the outcome of the HYPERVIEW challenge Enhancing agricultural methods through the utilization of Earth observation and artificial intelligence (AI) has emerged as a significant concern. The ability to quantify soil parameters on a large scale can play a pivotal role in optimizing the fertilization process. While techniques for noninvasive estimation of soil parameters from hyperspectral images (HSIs) exist, their validation typically occurs across different datasets and employs varying validation protocols. This diversity renders them inherently challenging (or even impossible) to compare objectively. Hyperspectral imaging ; Benchmark (surveying) ; Outcome (game theory) ; Remote sensing ; Computer science ; Artificial intelligence ; Environmental science ; Mathematics ; Geology ; Geography ; Cartography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Dela Quarme Gbadago | Gyuyeong Hwang | Kihwan Lee | Sungwon Hwang
Source: Korean Journal of Chemical Engineering (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery Computer science ; Artificial intelligence ; Deep learning ; Convolutional neural network ; Reinforcement learning ; Artificial neural network ; Machine learning ; Biochemical engineering ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Text and Document Classification Technologies
Authors: Shengxi Jiang | Peiji Yang | Yujia Zheng | Xiong Lu | Chaoming Xie
Source: Smart Materials in Medicine (article, 2024-02-11)
Field: Chemistry — Analytical Chemistry

Machine learning for polyphenol-based materials Polyphenol-based materials, primarily composed of polyphenolic compounds, have attracted considerable attention due to their unique chemical structures and biological activities. However, there are many derivatives of polyphenols, resulting in the complexity and diversity of polyphenol-based materials. Traditional methods are difficult to meet the rapid development of polyphenol-based materials. Machine learning, known for its proficiency in predicting performance, optimizing synthesis processes, and designing novel materials, offers significant potential in the intelligent design and applications of polyphenol-based materials. In this review, we summarize the recent advancements in the research and development of polyphenol-based materials and machine learning. The intersection of polyphenol-based materials and machine learning is also discussed, including their applications in biomedical, environmental, and energy fields. The challenges and prospects for the future development of polyphenol-based materials based on machine learning are highlighted. The role of machine learning in the development and application of polyphenol-based materials. • Potential pathways for exploring new functionalities of polyphenol-based materials using machine learning are proposed. Polyphenol ; Computer science ; Artificial intelligence ; Chemistry ; Organic chemistry ; Antioxidant Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Jawayria Najeeb | Syed Shoaib Ahmad Shah | Mudassir Hussain Tahir | Ahmed I. Hanafy | Salah M. El‐Bahy | Zeinhom M. El‐Bahy
Source: Materials Chemistry and Physics (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Machine learning assisted designing of polymers and refractive index prediction: Easy and fast screening of polymers from chemical space Polymer ; Chemical space ; Refractive index ; Space (punctuation) ; Materials science ; Index (typography) ; Polymer science ; Nanotechnology ; Computer science ; Chemical engineering ; Chemistry ; Optoelectronics ; Engineering ; Composite material Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Various Chemistry Research Topics
Authors: Chengyu Wang | Wei Wang | Yanji Sun | Yanqiu Pan | Siyao Feng
Source: Industrial & Engineering Chemistry Research (article, 2024-03-13)
Field: Chemistry — Inorganic Chemistry

Hybrid Modeling of Methanol to Olefin Reaction Kinetics Based on the Artificial Neural Network Methanol to olefin (MTO) is an important coal-based process to ensure olefin yield. The current reaction kinetics model as a crucial tool of process regulation lacks real-time, accuracy, and simplicity in describing the catalyst deactivation. To fill in the gap, a reaction kinetics hybrid model of MTO was developed by a machine learning approach. Specifically, a catalyst deactivation model was first built by an artificial neural network (ANN) and then integrated into a lump-based reaction kinetics model over a fresh SAPO-34 catalyst. Reaction kinetics parameters were estimated by the genetic algorithm. A priori knowledge was extracted by analyzing the deactivation experimental data and used in the training of ANN to enhance its extrapolability. The developed model of MTO agreed well with the kinetics experimental data considering the catalyst deactivation. The model provided a satisfactory predictive performance. Results show that a higher reaction temperature, longer reaction space time, and shorter catalyst residence time are beneficial to the yield of ethylene and propylene. The hybrid model developed in this work is expected to apply to the process control of MTO. The proposed hybrid modeling approach could be used as theoretical guidance for other catalytic reaction kinetics modeling. Catalysis ; Kinetics ; Chemical kinetics ; Olefin fiber ; Artificial neural network ; Methanol ; Chemistry ; Yield (engineering) ; Process (computing) ; Biological system ; Computer science ; Thermodynamics ; Organic chemistry ; Artificial intelligence ; Physics Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Takashi Toyao
Source: Chemistry Letters (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Toward accelerated discovery of solid catalysts using extrapolative machine learning approach Abstract Designing novel catalysts is pivotal for overcoming numerous energy and environmental challenges. Although data science approaches, particularly machine learning (ML) approaches, hold promise for accelerating catalyst development, discovering truly novel catalysts through ML remains rare. This is largely due to the perceived inability of the ML models to extrapolate and identify exceptional materials. In this Review, I present our approach taken to tackle this limitation. Specifically, we employed an advanced ML methodology that could make extrapolative predictions. This approach led to the discovery of multielemental solid catalysts for CO2 hydrogenation to CO. The results not only demonstrate the immense potential of ML in catalysis research but also set a new standard for the rapid development of high-performance catalysts. Chemistry ; Catalysis ; Artificial intelligence ; Chemical engineering ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation