jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Minkyung Kim | Dasol Lee | Younghwan Yang | Yeseul Kim | Junsuk Rho
Source: Nature Communications (article, 2022-04-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reaching the highest efficiency of spin Hall effect of light in the near-infrared using all-dielectric metasurfaces The spin Hall effect of light refers to a spin-dependent transverse splitting of light at a planar interface. Previous demonstrations to enhance the splitting have suffered from exceedingly low efficiency. Achievements of the large splitting with high efficiency have been reported in the microwave, but those in the optical regime remain elusive. Here, an approach to attain the large splitting with high efficiency in the near-infrared is proposed and experimentally demonstrated at 800 nm by using a dielectric metasurface. Modulation of the complex transmission of the metasurface leads to the shifts that reach 10λ along with efficiencies over 70% under two linear polarizations. Our work extends the recent attempts to achieve the large and efficient spin Hall effect of light, which have been limited only to the microwave, to the optical regime. Spin Hall effect ; Dielectric ; Infrared ; Spin (aerodynamics) ; Optoelectronics ; Hall effect ; Materials science ; Condensed matter physics ; Physics ; Optics ; Spin polarization ; Quantum mechanics ; Magnetic field ; Electron Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Wenjin Wang | Zhaoxian Li | Mancheng Li | Lvye Fang | Fubin Chen | Songjia Han | Liuyuan Lan | Junxin Chen | Qize Chen | Hongshang Wang | Chuan Liu | Yabin Yang | Wan Yue | Zhuang Xie
Source: Nano-Micro Letters (article, 2022-09-12)
Field: Materials Science — Polymers and Plastics

High-Transconductance, Highly Elastic, Durable and Recyclable All-Polymer Electrochemical Transistors with 3D Micro-Engineered Interfaces Organic electrochemical transistors (OECTs) have emerged as versatile platforms for broad applications spanning from flexible and wearable integrated circuits to biomedical monitoring to neuromorphic computing. A variety of materials and tailored micro/nanostructures have recently been developed to realized stretchable OECTs, however, a solid-state OECT with high elasticity has not been demonstrated to date. Herein, we present a general platform developed for the facile generation of highly elastic all-polymer OECTs with high transconductance (up to 12.7 mS), long-term mechanical and environmental durability, and sustainability. Rapid prototyping of these devices was achieved simply by transfer printing lithium bis(trifluoromethane)sulfonimide doped poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) (PEDOT:PSS/LiTFSI) microstructures onto a resilient gelatin-based gel electrolyte, in which both depletion-mode and enhancement-mode OECTs were produced using various active channels. Remarkably, the elaborate 3D architectures of the PEDOT:PSS were engineered, and an imprinted 3D-microstructured channel/electrolyte interface combined with wrinkled electrodes provided performance that was retained (> 70%) through biaxial stretching of 100% strain and after 1000 repeated cycles of 80% strain. Furthermore, the anti-drying and degradable gelatin and the self-crosslinked PEDOT:PSS/LiTFSI jointly enabled stability during > 4 months of storage and on-demand disposal and recycling. This work thus represents a straightforward approach towards high-performance stretchable organic electronics for wearable/implantable/neuromorphic/sustainable applications. Materials science ; PEDOT:PSS ; Nanotechnology ; Bioelectronics ; Electrolyte ; Polymer ; Electrode ; Composite material ; Layer (electronics) ; Biosensor ; Chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Supercapacitor Materials and Fabrication
Authors: Zhengdong Wang | Tong Zhang | Jinkai Wang | Ganqiu Yang | Mengli Li | Guanglei Wu
Source: Nanomaterials (article, 2022-01-28)
Field: Materials Science — Materials Chemistry

The Investigation of the Effect of Filler Sizes in 3D-BN Skeletons on Thermal Conductivity of Epoxy-Based Composites Thermally conductive and electrically insulating materials have attracted much attention due to their applications in the field of microelectronics, but through-plane thermal conductivity of materials is still low at present. In this paper, a simple and environmentally friendly strategy is proposed to improve the through-plane thermal conductivity of epoxy composites using a 3D boron nitride (3D-BN) framework. In addition, the effect of filler sizes in 3D-BN skeletons on thermal conductivity was investigated. The epoxy composite with larger BN in lateral size showed a higher through-plane thermal conductivity of 2.01 W/m·K and maintained a low dielectric constant of 3.7 and a dielectric loss of 0.006 at 50 Hz, making it desirable for the application in microelectronic devices. Epoxy ; Boron nitride ; Microelectronics ; Materials science ; Thermal conductivity ; Composite material ; Dielectric ; Composite number ; Filler (materials) ; Conductivity ; Electrical conductor ; Thermal conduction ; Nanotechnology ; Optoelectronics Thermal properties of materials ; Dielectric materials and actuators ; Graphene research and applications
Authors: James E. M. Lewis
Source: Chemical Communications (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Molecular engineering of confined space in metal–organic cages Metal-organic cages (MOCs) have become an intensely studied class of abiotic host molecules. This is due to the ability to generate a myriad of polyhedral architectures from relatively simple, and minimal numbers of, components in high yield and under thermodynamic control. The encapsulation of molecular guests within the nanoscale, confined cavities of these cages frequently draws comparisons with enzymatic binding sites. In this regard, the ostensible ease with which chemical modifications can be made to these internal cavities adds to their attractiveness, as they can be readily tailored with a high degree of precision. In this Feature Article, the ways in which the cavities of MOCs can be engineered at the molecular level will be looked at. The discussion will be divided across three key parameters: size, shape and functionality. Most concepts will be exemplified with a focus on the Pd<sub>2</sub>L<sub>4</sub> class of assemblies due to their relative structural simplicity and the wealth of studies reported in the literature. The core principles discussed will, however, be generalisable to other classes of MOCs, and abiotic host systems as a whole. Gaining increasing mastery over the fine tuning of MOC cavity properties, whilst retaining facile, high-fidelity self-assembly processes, will lead to ever more precise engineering of the cavities of artificial host systems with complex and highly specific functionality. Nanotechnology ; Yield (engineering) ; Nanoscopic scale ; Fidelity ; Chemical space ; Computer science ; Materials science ; Chemistry Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Porphyrin and Phthalocyanine Chemistry
Authors: Rana Muhammad Hasan Bilal | Muhammad Ahsan Saeed | Naveed Muhammad | Muhammad Zubair | Muhammad Qasim Mehmood | Yehia Massoud
Source: Nanomaterials (article, 2022-09-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nickel-Based High-Bandwidth Nanostructured Metamaterial Absorber for Visible and Infrared Spectrum The efficient control of optical light at the nanoscale level attracts marvelous applications, including thermal imaging, energy harvesting, thermal photovoltaics, etc. These applications demand a high-bandwidth, thermally robust, angularly stable, and miniaturized absorber, which is a key challenge to be addressed. So, in this study, the simple and cost-effective solution to attain a high-bandwidth nanostructured absorber is demonstrated. The designed nanoscale absorber is composed of a simple and plain circular ring of nickel metal, which possesses many interesting features, including a miniaturized geometry, easily fabricable design, large operational bandwidth, and polarization insensitivity, over the previously presented absorbers. The proposed nanoscale absorber manifests an average absorption of 93% over a broad optical window from 400 to 2800 nm. Moreover, the detailed analysis of the absorption characteristics is also performed by exciting the optical light's various incident and polarization angles. From the examined outcome, it is concluded that the nanostructured absorber maintains its average absorption of 80% at oblique incident angles in a broad wavelength range from 400 to 2800 nm. Owing to its appealing functionalities, such as the large bandwidth, simple geometry, low cost, polarization insensitivity, and thermal robustness of the constituting metal, nickel (Ni), this nano-absorber is made as an alternative for the applications of energy harvesting, thermal photovoltaics, and emission. Materials science ; Photovoltaics ; Metamaterial ; Optoelectronics ; Metamaterial absorber ; Polarization (electrochemistry) ; Optics ; Nanoscopic scale ; Bandwidth (computing) ; Ray ; Nanotechnology ; Photovoltaic system ; Computer science ; Telecommunications ; Tunable metamaterials ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Elizabeth Champa-Bujaico | Pilar García-Díaz | Ana M. Díez‐Pascual
Source: International Journal of Molecular Sciences (review, 2022-09-14)
Field: Materials Science — Polymers and Plastics

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted. Property (philosophy) ; Nanocomposite ; State (computer science) ; Materials science ; Computer science ; Polymer science ; Nanotechnology ; Algorithm ; Philosophy Polymer Nanocomposites and Properties ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Dhruv Menon | Raghavan Ranganathan
Source: ACS Omega (review, 2022-07-24)
Field: Materials Science — Materials Chemistry

A Generative Approach to Materials Discovery, Design, and Optimization Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research. Computer science ; Generative grammar ; Artificial intelligence ; Deep learning ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhilong Kang | Yuchen Zhao | Lei Chen | Yanju Guo | Qingshuang Mu | Shenyi Wang
Source: Food Engineering Reviews (review, 2022-09-06)
Field: Chemistry — Analytical Chemistry

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain Hyperspectral imaging ; Supply chain ; Computer science ; Quality (philosophy) ; Machine learning ; Artificial intelligence ; Process (computing) ; Sorting ; Food quality ; Food science ; Business Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Dongxiao Wang | Qiangqiang Zhu | Zhenxiong Wei | Baosen Lin | Yi Jing | Yuan Shi | R.D.K. Misra | Jianping Li
Source: Journal of Alloys and Compounds (article, 2022-03-15)
Field: Materials Science — Biomaterials

Hot deformation behaviors of AZ91 magnesium alloy: Constitutive equation, ANN-based prediction, processing map and microstructure evolution Microstructure ; Constitutive equation ; Magnesium alloy ; Deformation (meteorology) ; Materials science ; Magnesium ; Metallurgy ; Alloy ; Evolution equation ; Composite material ; Thermodynamics ; Mathematics ; Physics ; Mathematical analysis ; Finite element method Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: Jie Liu | Yanjun Li | Zhichao Lou
Source: Sustainability (review, 2022-05-10)
Field: Chemistry — Inorganic Chemistry

Recent Advancements in MOF/Biomass and Bio-MOF Multifunctional Materials: A Review Metal–organic frameworks (MOFs) and their derivatives have delivered perfect answers in detection, separation, solving water and electromagnetic pollution and improving catalysis and energy storage efficiency due to their advantages including their highly tunable porosity, structure and versatility. Recently, MOF/biomass, bio-MOFs and their derivatives have gradually become a shining star in the MOF family due to the improvement in the application performance of MOFs using biomass and biomolecules. However, current studies lack a systematic summary of the synthesis and advancements of MOF/biomass, bio-MOFs and their derivatives. In this review, we describe their research progress in detail from the following two aspects: (1) synthesis of MOF/biomass using biomass as a template to achieve good dispersion and connectivity at the same time; (2) preparing bio-MOFs by replacing traditional organic linkers with biomolecules to enhance the connection stability between metal ions/clusters and ligands and avoid the formation of toxic by-products. This enables MOFs to possess additional unique advantages, such as improved biocompatibility and mechanical strength, ideal reusability and stability and lower production costs. Most importantly, this is a further step towards green and sustainable development. Additionally, we showcase some typical application examples to show their great potential, including in the fields of environmental remediation, energy storage and electromagnetic wave absorption. Metal-organic framework ; Nanotechnology ; Reusability ; Biomass (ecology) ; Materials science ; Energy storage ; Biomolecule ; Biochemical engineering ; Computer science ; Chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials ; Magnesium Oxide Properties and Applications
Authors: Dilbag Singh | Yavuz Selim Taşpınar | Ramazan Kursun | İlkay Çınar | Murat Köklü | İlker Ali Özkan | Heung-No Lee
Source: Electronics (article, 2022-03-22)
Field: Chemistry — Analytical Chemistry

Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer vision system. The pre-trained dataset includes a total of 2148 images, 1232 of Kirmizi type and 916 of Siirt type. Three different convolutional neural network models were used to classify these images. Models were trained by using the transfer learning method, with AlexNet and the pre-trained models VGG16 and VGG19. The dataset is divided as 80% training and 20% test. As a result of the performed classifications, the success rates obtained from the AlexNet, VGG16, and VGG19 models are 94.42%, 98.84%, and 98.14%, respectively. Models’ performances were evaluated through sensitivity, specificity, precision, and F-1 score metrics. In addition, ROC curves and AUC values were used in the performance evaluation. The highest classification success was achieved with the VGG16 model. The obtained results reveal that these methods can be used successfully in the determination of pistachio types. Convolutional neural network ; Artificial intelligence ; Scope (computer science) ; Pattern recognition (psychology) ; Computer science ; Mathematics ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Nuts composition and effects
Authors: Shahin Homaeigohar | Aldo R. Boccaccini
Source: Frontiers in Chemistry (review, 2022-01-19)
Field: Materials Science — Biomaterials

Nature-Derived and Synthetic Additives to poly(ɛ-Caprolactone) Nanofibrous Systems for Biomedicine; an Updated Overview As a low cost, biocompatible, and bioresorbable synthetic polymer, poly (ɛ-caprolactone) (PCL) is widely used for different biomedical applications including drug delivery, wound dressing, and tissue engineering. An extensive range of <i>in vitro</i> and <i>in vivo</i> tests has proven the favourable applicability of PCL in biomedicine, bringing about the FDA approval for a plethora of PCL made medical or drug delivery systems. This popular polymer, widely researched since the 1970s, can be readily processed through various techniques such as 3D printing and electrospinning to create biomimetic and customized medical products. However, low mechanical strength, insufficient number of cellular recognition sites, poor bioactivity, and hydrophobicity are main shortcomings of PCL limiting its broader use for biomedical applications. To maintain and benefit from the high potential of PCL, yet addressing its physicochemical and biological challenges, blending with nature-derived (bio)polymers and incorporation of nanofillers have been extensively investigated. Here, we discuss novel additives that have been meant for enhancement of PCL nanofiber properties and thus for further extension of the PCL nanofiber application domain. The most recent researches (since 2017) have been covered and an updated overview about hybrid PCL nanofibers is presented with focus on those including nature-derived additives, e.g., polysaccharides and proteins, and synthetic additives, e.g., inorganic and carbon nanomaterials. Electrospinning ; Nanofiber ; Caprolactone ; Drug delivery ; Nanotechnology ; Materials science ; Biocompatible material ; Biomedicine ; Polymer ; Tissue engineering ; Limiting ; Biomedical engineering ; Copolymer ; Composite material ; Bioinformatics Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Tissue Engineering and Regenerative Medicine
Authors: Xueye Chen | Honglin Lv
Source: NPG Asia Materials (article, 2022-08-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent control of nanoparticle synthesis on microfluidic chips with machine learning Abstract Nanoparticles play irreplaceable roles in optoelectronic sensing, medical therapy, material science, and chemistry due to their unique properties. There are many synthetic pathways used for the preparation of nanoparticles, and different synthetic pathways can produce nanoparticles with different properties. Therefore, it is crucial to control the properties of nanoparticles precisely to impart the desired functions. In general, the properties of nanoparticles are influenced by their sizes and morphologies. Current technology for the preparation of nanoparticles on microfluidic chips requires repeated experimental debugging and significant resources to synthesize nanoparticles with precisely the desired properties. Machine learning-assisted synthesis of nanoparticles is a sensible choice for addressing this challenge. In this paper, we review many recent studies on syntheses of nanoparticles assisted by machine learning. Moreover, we describe the working steps of machine learning, the main algorithms, and the main ways to obtain datasets. Finally, we discuss the current problems of this research and provide an outlook. Nanoparticle ; Nanotechnology ; Microfluidics ; Debugging ; Materials science ; Computer science Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning in Materials Science