jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fanqi Liu | Xindi Li | Yumin Li | Suying Xu | Chang Guo | Leyu Wang
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Biomaterials

Visualization of drug release in a chemo-immunotherapy nanoplatform <i>via</i> ratiometric <sup>19</sup>F magnetic resonance imaging Visualization of drug release <i>in vivo</i> is crucial for improving therapeutic efficacy and preventing inappropriate medication dosing, yet, challenging. Herein, we report a pH-activated chemo-immunotherapy nanoplatform with visualization of drug release <i>in vivo</i> by ratiometric <sup>19</sup>F magnetic resonance imaging (<sup>19</sup>F MRI). This nanoplatform consists of ultra-small histamine-modified perfluoro-15-crown-5-ether (PFCE) nanodroplets loaded with doxorubicin (Dox), which are packaged in trifluoromethyl-containing metal-organic assemblies <i>via</i> coordination-driven self-assembly. The chemical shifts of two types of <sup>19</sup>F atoms in the nanoplatform are significantly different in <sup>19</sup>F nuclear magnetic resonance (NMR) spectra, which facilitates the implementation of ratiometric <sup>19</sup>F MRI without any signal crosstalk. In an acidic tumor microenvironment, this nanoplatform gradually degrades, which results in a sustained drug release with a real-time change in the ratiometric <sup>19</sup>F MRI signal. Therefore, a linear correlation between the Dox release profile and ratiometric <sup>19</sup>F MRI signal is established to visualize Dox release. Moreover, the pH-triggered disassembly of the nanoplatform leads to cell pyroptosis, which evokes immunogenic cell death (ICD), resulting in the regression of the primary tumor and inhibition of distal tumor growth. This study provides the proof-of-concept application of ratiometric <sup>19</sup>F MRI to visualize drug release <i>in vivo</i>. Magnetic resonance imaging ; Drug ; Visualization ; Nuclear magnetic resonance ; Immunotherapy ; Chemistry ; Nanotechnology ; Materials science ; Medicine ; Pharmacology ; Physics ; Computer science ; Radiology ; Data mining ; Immunology ; Immune system Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Ning Guo | C.S. Chen | Guangwei Zhang | Rongsheng Sun | Yulong Wang | Junguo Gao
Source: High Voltage (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Effect of nanofillers with different energy levels on the electrical properties of epoxy‐based nanocomposites Abstract The authors investigate the effects of nanofillers with varying band‐gap energies on the space charge properties, breakdown field strength, and bulk resistivity of epoxy (EP)‐based composites. Additionally, the molecular orbital distribution of both the epoxy resin and nanofillers were examined through density functional theory. Experimental results indicate that the space charge accumulation within silicon dioxide/EP and germanium oxide/EP is reduced, leading to a more uniformly distributed electric field intensity within the specimen when compared to epoxy. As a result, both materials exhibit improved AC breakdown field strength and volume resistivity. Conversely, the amount of charge accumulated within tin dioxide/EP is higher, resulting in lower breakdown field strength than epoxy. The lowest unoccupied molecular orbital and the highest occupied molecular orbital energy level differences between epoxy and nanofillers introduce electron traps and hole traps at the interface, forming interfacial traps that affect the space charge distribution within the specimen, as well as the trap energy levels within the material. From the experimental results, shallow traps promote space charge accumulation and reduce the breakdown field strength, while deep traps have the opposite effect. Epoxy ; Nanocomposite ; Materials science ; Composite material High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Shijie Xiong | Xianguang Yang
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical color routing enabled by deep learning Nano-color routing has emerged as an immensely popular and widely discussed subject in the realms of light field manipulation, image sensing, and the integration of deep learning. The conventional dye filters employed in commercial applications have long been hampered by several limitations, including subpar signal-to-noise ratio, restricted upper bounds on optical efficiency, and challenges associated with miniaturization. Nonetheless, the advent of bandpass-free color routing has opened up unprecedented avenues for achieving remarkable optical spectral efficiency and operation at sub-wavelength scales within the area of image sensing applications. This has brought about a paradigm shift, fundamentally transforming the field by offering a promising solution to surmount the constraints encountered with traditional dye filters. This review presents a comprehensive exploration of representative deep learning-driven nano-color routing structure designs, encompassing forward simulation algorithms, photonic neural networks, and various global and local topology optimization methods. A thorough comparison is drawn between the exceptional light-splitting capabilities exhibited by these methods and those of traditional design approaches. Additionally, the existing research on color routing is summarized, highlighting a promising direction for forthcoming development, delivering valuable insights to advance the field of color routing and serving as a powerful reference for future endeavors. Sorting ; Deep learning ; Routing (electronic design automation) ; Computer science ; Field (mathematics) ; Artificial intelligence ; Nanoscopic scale ; Materials science ; Nanotechnology ; Computer network ; Algorithm ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: Jie Li | Boya Zhang | Xinghua Zhang | Yixuan Li | Kaixuan Li | Tianyu Wang | Xingwen Li
Source: Small (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Revolutionizing High‐Temperature Electrical Properties of Epoxy Resin via Tailoring Weak Conjugation Rigid Structures The escalating demand for high-power and compact-size advanced electronic devices and power systems necessitates polymers to exhibit superior electrical properties even under harsh environments. However, reconciling the seemingly contradictory attributes of excellent electrical properties and thermal stability poses a formidable challenge for current epoxy polymer (EP) materials and their applications. To meet the need, here two classes of bi-aryl diamine curing agents are described that enable polymers to exhibit well-balanced thermal and dielectric properties with functional bridging groups. A weak conjugation system in highly thermally stable polymers with an aromatic backbone is constructed, using electron-modulating bridging groups to immobilize intramolecular free carriers by tailoring trap sites, and bulky bridging groups to prevent molecular stacking to inhibit intermolecular charge transport. The resultant polymer exhibits a volume resistance of 7.45 × 10<sup>12</sup> Ω m and a direct current breakdown strength of 368.74 kV mm<sup>-1</sup> at 120 °C, which are 2.2 and 2.4 times higher than that of commercial anhydride-cured EP, respectively. It is demonstrated to be due to the inhibition of charge injection and transport. The proposed aromatic amine multimolecule approach, combined with diverse functional bridging groups, is a promising direction for exploring next-generation EP insulation materials suitable for extreme conditions. Materials science ; Epoxy ; Polymer ; Stacking ; Bridging (networking) ; Thermal stability ; Intermolecular force ; Curing (chemistry) ; Dielectric ; Intramolecular force ; Polymer chemistry ; Chemical engineering ; Composite material ; Molecule ; Optoelectronics ; Organic chemistry ; Chemistry ; Computer science High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Synthesis and properties of polymers
Authors: Yaohong Zhao | Yihua Qian | Guobin Zhong | Siyuan Wu | Siwei Pan
Source: Fire (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

A Thermal Characteristics Study of Typical Industrial Oil Based on Thermogravimetric-Differential Scanning Calorimetry (TG-DSC) Recent incidents of fire accidents attributed to oil combustion have emerged as a significant threat to both industrial safety and environmental conservation. In this study, the thermal oxidation and thermal analysis kinetics parameters of transformer oil, engine oil, and hydraulic oil in the air atmosphere were explored based on thermogravimetric-differential scanning calorimetry (TG-DSC). Industrial oils showed the same decomposition process in the thermal decomposition process. The peak temperature of the DSC curve was higher than that of the DTG curve, and the peak values of DTG and DSC curves increased with the increase of heating rate. The industrial oils underwent a main mass loss process, with respective ranges of approximately 80–84% for transformer oil, 73–79% for engine oil, and 86–89% for hydraulic oil. Notably, engine oil demonstrated the highest average apparent activation energy, amounting to 110.50 kJ/mol, significantly surpassing hydraulic oil (105.13 kJ/mol) and transformer oil (60.95 kJ/mol). The optimal kinetic model for the evaporative oxidation reaction of the industrial oils in air was identified as the reaction order model (Fn), with the corresponding kinetic mechanism function expressed as f(α) = (1 − α)n. The use of TG-DSC offers novel perspectives on the thermal stability and safety evaluation of oil products. Meanwhile, the optimal kinetic model and thermal oxidation stability of typical industrial oil evaporation and oxidation reaction in air was determined, possessing a good reference for the safety and the application of industrial oil. Thermogravimetric analysis ; Differential scanning calorimetry ; Materials science ; Differential (mechanical device) ; Chemical engineering ; Thermodynamics ; Engineering ; Physics Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Petroleum Processing and Analysis
Authors: Qing Chu | Xin Jiang | Ying Xiao
Source: Frontiers in Bioengineering and Biotechnology (review, 2024-09-20)
Field: Materials Science — Biomaterials

Rebuilding the myocardial microenvironment to enhance mesenchymal stem cells-mediated regeneration in ischemic heart disease Mesenchymal stem cells (MSCs) are naturally-derived regenerative materials that exhibit significant potential in regenerative medicine. Previous studies have demonstrated that MSCs-based therapy can improve heart function in ischemia-injured hearts, offering an exciting therapeutic intervention for myocardial ischemic infarction, a leading cause of worldwide mortality and disability. However, the efficacy of MSCs-based therapies is significantly disturbed by the myocardial microenvironment, which undergoes substantial changes following ischemic injury. After the ischemic injury, blood vessels become obstructed and damaged, and cardiomyocytes experience ischemic conditions. This activates the hypoxia-induced factor 1 (HIF-1) pathway, leading to the rapid production of several cytokines and chemokines, including vascular endothelial growth factor (VEGF) and stromal-derived factor 1 (SDF-1), which are crucial for angiogenesis, cell migration, and tissue repair, but it is not sustainable. MSCs respond to these cytokines and chemokines by homing to the injured site and participating in myocardial regeneration. However, the deteriorated microenvironment in the injured myocardium poses challenges for cell survival, interacting with MSCs, and constraining their homing, retention, and migration capabilities, thereby limiting their regenerative potential. This review discusses how the deteriorated microenvironment negatively affects the ability of MSCs to promote myocardial regeneration. Recent studies have shown that optimizing the microenvironment through the promotion of angiogenesis can significantly enhance the efficacy of MSCs in treating myocardial infarction. This approach harnesses the full therapeutic potential of MSCs-based therapies for ischemic heart disease. Homing (biology) ; Mesenchymal stem cell ; Medicine ; Angiogenesis ; Regeneration (biology) ; Stem cell ; Myocardial infarction ; Vascular endothelial growth factor ; Therapeutic angiogenesis ; Chemokine ; Stromal cell ; Cancer research ; Regenerative medicine ; Immunology ; Cardiology ; Cell biology ; Inflammation ; Neovascularization ; Pathology ; Biology Electrospun Nanofibers in Biomedical Applications ; Mesenchymal stem cell research ; Tissue Engineering and Regenerative Medicine
Authors: João Mendes | José Lima | Lino Costa | Nuno Rodrigues | Ana I. Pereira
Source: Smart Agricultural Technology (article, 2024-05-15)
Field: Chemistry — Analytical Chemistry

Deep learning networks for olive cultivar identification: A comprehensive analysis of convolutional neural networks Deep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques. Convolutional neural network ; Identification (biology) ; Deep learning ; Artificial intelligence ; Computer science ; Artificial neural network ; Cultivar ; Machine learning ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Mega Novita | Alok Singh Chauhan | Rizky Muliani Dwi Ujianti | Dian Marlina | Haryo Kusumo | Muchamad Taufiq Anwar | M. Piasecki | M.G. Brik
Source: Journal of Luminescence (article, 2024-02-02)
Field: Materials Science — Materials Chemistry

Exploring deep learning and machine learning for novel red phosphor materials Interpretability ; Lasso (programming language) ; Artificial intelligence ; Computer science ; Machine learning ; Regression ; Deep learning ; Regression analysis ; Phosphor ; Linear regression ; Elastic net regularization ; Feature selection ; Materials science ; Mathematics ; Statistics Machine Learning in Materials Science ; Luminescence Properties of Advanced Materials ; Advanced Photocatalysis Techniques
Authors: Brian H. Lee | James P. Larentzos | John K. Brennan | Alejandro Strachan
Source: npj Computational Materials (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

Graph neural network coarse-grain force field for the molecular crystal RDX Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities. Graph ; Crystal (programming language) ; Computer science ; Artificial neural network ; Force field (fiction) ; Materials science ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Zirui Zhao | Haifeng Li
Source: ACS Applied Materials & Interfaces (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Investigating Material Interface Diffusion Phenomena through Graph Neural Networks in Applied Materials Understanding and predicting interface diffusion phenomena in materials is crucial for various industrial applications, including semiconductor manufacturing, battery technology, and catalysis. In this study, we propose a novel approach utilizing Graph Neural Networks (GNNs) to investigate and model material interface diffusion. We begin by collecting experimental and simulated data on diffusion coefficients, concentration gradients, and other relevant parameters from diverse material systems. The data are preprocessed, and key features influencing interface diffusion are extracted. Subsequently, we construct a GNN model tailored to the diffusion problem, with a graph representation capturing the atomic structure of materials. The model architecture includes multiple graph convolutional layers for feature aggregation and update, as well as optional graph attention layers to capture complex relationships between atoms. We train and validate the GNN model using the preprocessed data, achieving accurate predictions of diffusion coefficients, diffusion rates, concentration profiles, and potential diffusion pathways. Our approach offers insights into the underlying mechanisms of interface diffusion and provides a valuable tool for optimizing material design and engineering. Additionally, our method offers possible strategies to solve the longstanding problems related to materials interface diffusion. Materials science ; Interface (matter) ; Diffusion ; Artificial neural network ; Nanotechnology ; Composite material ; Artificial intelligence ; Computer science ; Thermodynamics ; Wetting Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Zirui Zhao | Xiaoke Wang | Si Wu | Pengfei Zhou | Qian Zhao | Guangping Xu | Kaitong Sun | Haifeng Li
Source: AAPPS bulletin (article, 2024-09-17)
Field: Materials Science — Materials Chemistry

Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance Abstract NASICON (Na $$_{1+x}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow/> </mml:mmultiscripts> </mml:math> Zr $$_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mn>2</mml:mn> <mml:mrow/> </mml:mmultiscripts> </mml:math> Si $$_x$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mi>x</mml:mi> <mml:mrow/> </mml:mmultiscripts> </mml:math> P $$_{3-x}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>-</mml:mo> <mml:mi>x</mml:mi> </mml:mrow> <mml:mrow/> </mml:mmultiscripts> </mml:math> O $$_{12}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mmultiscripts> <mml:mrow/> <mml:mn>12</mml:mn> <mml:mrow/> </mml:mmultiscripts> </mml:math> ) is a well-established solid-state electrolyte, renowned for its high ionic conductivity and excellent chemical stability, rendering it a promising candidate for solid-state batteries. However, the intricate influence of ion doping on their performance has been a central focus of research, with existing studies often lacking comprehensive evaluation methods. This study introduces a deep-learning-based approach to efficiently evaluate ion-doped NASICON materials. We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation. The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model’s predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations. Graphical Abstract Materials science ; Doping ; Fast ion conductor ; Battery (electricity) ; Ion ; Solid-state ; Computer science ; Optoelectronics ; Engineering physics ; Electrolyte ; Chemistry ; Electrode ; Engineering ; Physical chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research