jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng Chen | Yonghong Song | Xu Yan | Liang Dong | Yunjun Xu | Shouhu Xuan | Quan Shu | Baoqiang Cao | Jinlong Hu | Hanye Xing | Wenshu Wu | Zhengbao Zha | Yang Lü
Source: Journal of Nanobiotechnology (article, 2022-08-19)
Field: Materials Science — Biomaterials

Injectable magnetic montmorillonite colloidal gel for the postoperative treatment of hepatocellular carcinoma Bioactive materials have been extensively developed for the adjuvant therapy of cancer. However, few materials can meet the requirements for the postoperative resection of hepatocellular carcinoma (HCC) due to massive bleeding and high recurrence. In particular, combination therapy for HCC has been highly recommended in clinical practice, including surgical resection, interventional therapy, ablation therapy and chemotherapy. Herein, an injectable magnetic colloidal gel (MCG) was developed by controllable electrostatic attraction between clinically available magnetic montmorillonites and amphoteric gelatin nanoparticles. The optimized MCG exhibited an effective magnetic heating effect, remarkable rheological properties, and high gel network stability, realizing the synergistic treatment of postoperative HCC by stimuli-responsive drug delivery, hemostasis and magnetic hyperthermia. Furthermore, a minimal invasive MCG-induced interventional magnetic hyperthermia therapy (MHT) under ultrasound guidance was realized on hepatic tumor rabbits, providing an alternative therapeutics to treat the postoperative recurrence. Overall, MCG is a clinically available injectable formulation for adjuvant therapy after HCC surgical resection. Medicine ; Hepatocellular carcinoma ; Hyperthermia ; Hyperthermia therapy ; Adjuvant ; Adjuvant therapy ; Radiation therapy ; Hemostasis ; Magnetic hyperthermia ; Magnetic nanoparticles ; Chemotherapy ; Surgery ; Oncology ; Internal medicine ; Nanoparticle ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; Hepatocellular Carcinoma Treatment and Prognosis ; Nanoplatforms for cancer theranostics
Authors: Mattia Bartoli | Donatella Duraccio | Maria Giulia Faga | Erik Piatti | Daniele Torsello | G. Ghigo | Giulio Malucelli
Source: Journal of Materials Science (article, 2022-08-01)
Field: Materials Science — Polymers and Plastics

Mechanical, electrical, thermal and tribological behavior of epoxy resin composites reinforced with waste hemp-derived carbon fibers Abstract Short hemp fibers, an agricultural waste, were used for producing biochar by pyrolysis at 1000 °C. The so-obtained hemp-derived carbon fibers (HFB) were used as filler for improving the properties of an epoxy resin using a simple casting and curing process. The addition of HFB in the epoxy matrix increases the storage modulus while damping factor is lowered. Also, the incorporation of HFB induces a remarkable increment of electrical conductivity reaching up to 6 mS/m with 10 wt% of loading. A similar trend is also observed during high-frequency measurements. Furthermore, for the first time wear of these composites has been studied. The use of HFB is an efficient method for reducing the wear rate resistance and the friction coefficient (COF) of the epoxy resin. Excellent results are obtained for the composite containing 2.5 wt% of HFB, for which COF and wear rate decrease by 21% and 80%, respectively, as compared with those of the unfilled epoxy resin. The overall results prove how a common waste carbon source can significantly wide epoxy resin applications by a proper modulation of its electrical and wear properties. Graphical abstract Materials science ; Epoxy ; Composite material ; Tribology ; Curing (chemistry) ; Composite number ; Pyrolysis ; Casting ; Carbon fibers ; Waste management Natural Fiber Reinforced Composites ; Electromagnetic wave absorption materials ; Tribology and Wear Analysis
Authors: Lajos Attila Papp | Sarolta Krizbai | Máté Dobó | Gabriel Hancu | Zoltán‐István Szabó | Gergő Tóth
Source: Molecules (article, 2022-05-06)
Field: Chemistry — Spectroscopy

Determination of Chiral Impurity of Naproxen in Different Pharmaceutical Formulations Using Polysaccharide-Based Stationary Phases in Reversed-Phased Mode A novel, validated, reversed-phase (RP), chiral high performance liquid chromatography (HPLC) method was developed for the enantiopurity control analysis of naproxen, a frequently used non-steroidal anti-inflammatory agent using polysaccharide-type chiral stationary phase (CSP). In the screening phase of method development, seven columns were tested in polar organic (PO) mode using mobile phases consisting of 0.1% acetic acid in methanol, ethanol, 2-propanol, and acetonitrile. Enantiorecognition was observed only in five cases. The best enantioseparation was observed on a Lux Amylose-1 column with 0.1% (<i>v</i>/<i>v</i>) acetic acid in ethanol with a resolution (<i>R</i><sub>s</sub>) of 1.24. The enantiomer elution order was unfavorable, as the distomer eluted after the eutomer. When the ethanolic mobile phase was supplemented with water, enantiomer elution order reversal was observed, indicating a difference in the enantiorecognition mechanism upon switching from PO to RP mode. Furthermore, by changing ethanol to methanol, not only lower backpressure, but also higher resolution was obtained. Subsequent method optimization was performed using a face-centered central composite design (FCCD) to achieve higher chiral resolution in a shorter analysis time. Optimized parameters offering baseline separation were as follows: Lux Amylose-1 stationary phase, thermostated at 40 °C, and a mobile phase consisting of methanol:water:acetic acid 85:15:0.1 (<i>v</i>/<i>v</i>/<i>v</i>), delivered with 0.65 mL/min flow rate. Using these optimized parameters, a <i>R</i><sub>s</sub> = 3.21 ± 0.03 was achieved within seven minutes. The optimized method was validated according to the ICH guidelines and successfully applied for the analysis of different pharmaceutical preparations, such as film-coated tablets and gel, as well as fixed-dose combination tablets, containing both naproxen and esomeprazole. Acetic acid ; Chromatography ; Elution ; Methanol ; Chemistry ; Enantiomer ; Resolution (logic) ; Naproxen ; Ethanol ; High-performance liquid chromatography ; Chiral resolution ; Phase (matter) ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Microfluidic and Capillary Electrophoresis Applications
Authors: Atsunobu Masuno
Source: Journal of the Ceramic Society of Japan (article, 2022-07-31)
Field: Materials Science — Ceramics and Composites

Functionalities in unconventional oxide glasses prepared using a levitation technique Over the last 20 years, many unconventional oxide glasses have been fabricated in bulk using a levitation technique. These glasses contain no or less network-former oxides; thus, they have been supposed not to vitrify so far. The levitation technique could vitrify them because the levitated melt was maintained without touching anything; thus, crystal nucleation was extremely suppressed. This review presents a brief introduction to the levitation technique. It also summarizes some functionalities that emerged in the glasses prepared using this technique, such as high refractive index, infrared transparency, strong luminescence, large magneto-optical effect, high elastic moduli, and crack resistance. Materials science ; Levitation ; Oxide ; Nucleation ; Refractive index ; Luminescence ; Infrared ; Composite material ; Optics ; Mineralogy ; Optoelectronics ; Thermodynamics ; Metallurgy ; Mechanical engineering Glass properties and applications ; Magneto-Optical Properties and Applications ; Mineralogy and Gemology Studies
Authors: Daniel P. Weller | Donald T. Morelli
Source: Frontiers in Electronic Materials (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Tetrahedrite Thermoelectrics: From Fundamental Science to Facile Synthesis Thermoelectric materials have a long and storied history in the research and development of semiconductor materials, being the first such class of materials to be investigated. Thermoelectrics may be used to convert heat to electricity or, alternatively, to liberate or absorb heat upon electrical excitation. They thus find application in thermoelectric generators for converting heat from a primary source or a waste stream to useful electrical power, and as solid state heating and cooling devices. In spite of their great potential in such important applications, thermoelectrics have suffered from a number of drawbacks that have hindered their utilization on a large scale. Chief among these is the fact that most high performance thermoelectric materials are comprised of elements that are in relatively low abundance. Additionally, their synthesis typically involves complex and multi-step processes, hindering manufacturability. Thermoelectric materials derived from Earth-abundant sources are thus of strong current interest, from both scientific and economic points of view. One of these, the family of semiconductors based on tetrahedrite compounds, has generated enormous interest over the last decade due to not only its potential low cost, but also for its fascinating science. In this review, we summarize the state of the art of tetrahedrite as a thermoelectric, with special emphasis on the relationship between crystal structure and bonding in the crystal and its unusually low lattice thermal conductivity; on its fascinating electronic structure; and on the wide array of compositions that have been synthesized and whose thermoelectric properties have been studied. We further highlight some rapid and facile synthesis techniques that have been developed for these compounds which, in combination with their potential low material cost, may open the door to widespread application of these fascinating materials. Thermoelectric materials ; Tetrahedrite ; Thermoelectric effect ; Materials science ; Nanotechnology ; Engineering physics ; Thermoelectric generator ; Waste heat ; Semiconductor ; Electricity ; Optoelectronics ; Thermal conductivity ; Mechanical engineering ; Electrical engineering ; Engineering ; Physics ; Metallurgy ; Composite material Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Advanced Condensed Matter Physics
Authors: Mingkai Wang | Chuanyu Hu | Qianqian Su
Source: Biosensors (review, 2022-02-19)
Field: Materials Science — Materials Chemistry

Luminescent Lifetime Regulation of Lanthanide-Doped Nanoparticles for Biosensing Lanthanide-doped nanoparticles possess numerous advantages including tunable luminescence emission, narrow peak width and excellent optical and thermal stability, especially concerning the long lifetime from microseconds to milliseconds. Differing from other shorter-lifetime fluorescent nanomaterials, the long lifetime of lanthanide-doped nanomaterials is independent with background fluorescence interference and biological tissue depth. This review presents the recent advances in approaches to regulating the lifetime and applications of bioimaging and biodetection. We begin with the introduction of the strategies for regulating the lifetime by modulating the core-shell structure, adjusting the concentration of sensitizer and emitter, changing energy transfer channel, establishing a fluorescence resonance energy transfer pathway and changing temperature. We then summarize the applications of these nanoparticles in biosensing, including ion and molecule detecting, DNA and protease detection, cell labeling, organ imaging and thermal and pH sensing. Finally, the prospects and challenges of the lanthanide lifetime regulation for fundamental research and practical applications are also discussed. Lanthanide ; Nanomaterials ; Nanotechnology ; Biosensor ; Nanoparticle ; Luminescence ; Materials science ; Fluorescence ; Energy transfer ; Doping ; Förster resonance energy transfer ; Chemistry ; Optoelectronics ; Ion ; Optics Nanocluster Synthesis and Applications ; Luminescence Properties of Advanced Materials ; Lanthanide and Transition Metal Complexes
Authors: Anthony Wang | Mahamad Salah Mahmoud | Mathias Czasny | Aleksander Gurlo
Source: Integrating materials and manufacturing innovation (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

CrabNet for Explainable Deep Learning in Materials Science: Bridging the Gap Between Academia and Industry Abstract Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike. Interpretability ; Bridging (networking) ; Data science ; Computer science ; Popularity ; Informatics ; Process (computing) ; Artificial intelligence ; Similarity (geometry) ; Element (criminal law) ; Property (philosophy) ; Interpretation (philosophy) ; Materials informatics ; Management science ; Knowledge management ; Health informatics ; Engineering informatics ; Engineering ; Epistemology ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Makdud Islam | Praveen Saini | Rahul Das | Shubhra Shekhar | Akhouri Sanjay Kumar Sinha | K. Prasad
Source: BioResources (review, 2022-12-21)
Field: Materials Science — Biomaterials

Rice straw as a source of nanocellulose for sustainable food packaging materials: A Review Asian countries, despite being the largest producers and yielding a significant proportion of the world’s rice, have poor disposal facilities for the harvested rice straw (stubble). Due to higher costs in their handling relative to their value, local farmers prefer the burning of stubble fields, thus creating environmental problems. Even though the government has taken initiatives, no effective solution has been discovered to handle this large agro-waste problem efficiently. In this regard, the utilization of rice straw to develop nanocellulose (NC) products is of interest. Renewability and biodegradability, along with suitable mechanical and thermal properties required for the packaging functions, are key advantages of NC. The bio-nanocomposites prepared using NC and other bio-based polymers are also being widely considered for sustainable food packaging applications due to the reinforcement provided by NC and alternative petroleum-based packaging materials. This review provides an overview of process utilization for preparing NC products using rice straw, pulping methods, and isolation to produce bio-nanocomposites for sustainable food packaging applications. The challenges and future aspects covering the utilization of rice straw for producing NC and eventually producing active packaging materials are also discussed. Straw ; Nanocellulose ; Waste management ; Agriculture ; Food packaging ; Sustainable agriculture ; Environmental science ; Environmentally friendly ; Business ; Agricultural engineering ; Pulp and paper industry ; Engineering ; Cellulose ; Agronomy Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Natural Fiber Reinforced Composites
Authors: Zheren Wang | Kevin Cruse | Yuxing Fei | Ann Chia | Yan Zeng | Haoyan Huo | Tanjin He | Bowen Deng | Olga Kononova | Gerbrand Ceder
Source: Digital Discovery (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols A Unified Language of Synthesis Actions (ULSA) for describing inorganic synthesis is developed to help create a synthesis ontology and a solid foundation for autonomous robotic synthesis. Foundation (evidence) ; Computer science ; Representation (politics) ; Ontology ; Programming language ; Epistemology ; Philosophy Machine Learning in Materials Science ; Semantic Web and Ontologies ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Isaiah A. Moses | Verónica Barone | Juan E. Peralta
Source: Journal of Power Sources (article, 2022-08-26)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of battery electrode materials through data mining and deep learning models Battery (electricity) ; Electrode ; Computer science ; Anode ; Voltage ; Protocol (science) ; Database ; Artificial intelligence ; Electrical engineering ; Engineering ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Felix Mayr | Milan Harth | Ioannis Kouroudis | Michael Rinderle | Alessio Gagliardi
Source: The Journal of Physical Chemistry Letters (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up <i>ab initio</i> calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined. Generative grammar ; Photovoltaic system ; Computer science ; Perspective (graphical) ; Materials science ; Semiconductor ; Energy consumption ; Nanotechnology ; Artificial intelligence ; Optoelectronics ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Computational Drug Discovery Methods
Authors: Mallory Wittwer | Matteo Seita
Source: npj Computational Materials (article, 2022-01-19)
Field: Materials Science — Materials Chemistry

A machine learning approach to map crystal orientation by optical microscopy Abstract Mapping grain orientation in crystalline solids is essential to investigate the relationships between local microstructure and crystallography and interpret materials properties. One of the main techniques used to perform these studies is electron backscatter diffraction (EBSD). Due to the limited measurement throughput, however, EBSD is not suitable for characterizing samples with long-range microstructure heterogeneity, nor for building large material libraries that include numerous specimens. We present a machine learning approach for high-throughput crystal orientation mapping, which relies on the optical technique called directional reflectance microscopy. We successfully apply our method on Inconel 718 specimens produced by additive manufacturing, which exhibit complex, spatially-varying microstructures. These results demonstrate that optical orientation mapping on a metal alloy is achievable. Since our method is data-driven, it can be easily extended to different alloy systems produced using different manufacturing processes. Electron backscatter diffraction ; Microstructure ; Materials science ; Orientation (vector space) ; Inconel ; Alloy ; Optical microscope ; Throughput ; Optics ; Scanning electron microscope ; Artificial intelligence ; Computer science ; Composite material ; Geometry ; Physics Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques ; Additive Manufacturing Materials and Processes
Authors: Yueh‐Ting Shih | Yunfeng Shi | Liping Huang
Source: Journal of Non-Crystalline Solids (article, 2022-02-23)
Field: Materials Science — Materials Chemistry

Predicting glass properties by using physics- and chemistry-informed machine learning models Support vector machine ; Artificial neural network ; Shear modulus ; Random forest ; Machine learning ; Domain (mathematical analysis) ; Property value ; Modulus ; Elastic modulus ; Artificial intelligence ; Materials science ; Statistical physics ; Computer science ; Thermodynamics ; Mathematics ; Physics ; Mathematical analysis ; Composite material Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Authors: Kyungmin Baek | Taehyun Hwang | Wonseok Lee | Hayoung Chung | Maenghyo Cho
Source: Composites Science and Technology (article, 2022-07-31)
Field: Materials Science — Materials Chemistry

Deep learning aided evaluation for electromechanical properties of complexly structured polymer nanocomposites Materials science ; Nanocomposite ; Convolution (computer science) ; Cluster analysis ; Polymer nanocomposite ; Artificial neural network ; Polypropylene ; Deep learning ; Polymer ; Graph ; Composite material ; Nanoparticle ; Computer science ; Artificial intelligence ; Nanotechnology ; Theoretical computer science Carbon Nanotubes in Composites ; Smart Materials for Construction ; Thermal properties of materials