jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanhao Wang | Jinbo Pang | Qilin Cheng | Lin Han | Yufen Li | Xue Meng | Bergoi Ibarlucea | Wenrui Zhao | Feng Yang | Haiyun Liu | Hong Liu | Weijia Zhou | Xiao Wang | Mark H. Rümmeli | Yu Zhang | Gianaurelio Cuniberti
Source: Nano-Micro Letters (review, 2021-06-14)
Field: Materials Science — Materials Chemistry

Applications of 2D-Layered Palladium Diselenide and Its van der Waals Heterostructures in Electronics and Optoelectronics The rapid development of two-dimensional (2D) transition-metal dichalcogenides has been possible owing to their special structures and remarkable properties. In particular, palladium diselenide (PdSe<sub>2</sub>) with a novel pentagonal structure and unique physical characteristics have recently attracted extensive research interest. Consequently, tremendous research progress has been achieved regarding the physics, chemistry, and electronics of PdSe<sub>2</sub>. Accordingly, in this review, we recapitulate and summarize the most recent research on PdSe<sub>2</sub>, including its structure, properties, synthesis, and applications. First, a mechanical exfoliation method to obtain PdSe<sub>2</sub> nanosheets is introduced, and large-area synthesis strategies are explained with respect to chemical vapor deposition and metal selenization. Next, the electronic and optoelectronic properties of PdSe<sub>2</sub> and related heterostructures, such as field-effect transistors, photodetectors, sensors, and thermoelectric devices, are discussed. Subsequently, the integration of systems into infrared image sensors on the basis of PdSe<sub>2</sub> van der Waals heterostructures is explored. Finally, future opportunities are highlighted to serve as a general guide for physicists, chemists, materials scientists, and engineers. Therefore, this comprehensive review may shed light on the research conducted by the 2D material community. Heterojunction ; Nanotechnology ; Tungsten diselenide ; van der Waals force ; Electronics ; Materials science ; Diselenide ; Chemical vapor deposition ; Photodetector ; Exfoliation joint ; Palladium ; Engineering physics ; Optoelectronics ; Graphene ; Transition metal ; Physics ; Chemistry ; Molecule ; Physical chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Egor Egorov | Calvin Pieters | Hila Korach-Rechtman | Jeny Shklover | Avi Schroeder
Source: Drug Delivery and Translational Research (article, 2021-02-13)
Field: Materials Science — Biomaterials

Robotics, microfluidics, nanotechnology and AI in the synthesis and evaluation of liposomes and polymeric drug delivery systems Nanomedicine ; Nanotechnology ; Drug delivery ; Artificial intelligence ; Automation ; Robotics ; Coronavirus disease 2019 (COVID-19) ; Computer science ; Engineering ; Nanoparticle ; Medicine ; Materials science ; Robot ; Mechanical engineering Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation ; Nanoparticles: synthesis and applications
Authors: Mohammadreza Behi | Leila Gholami | Sina Naficy | Stefano Palomba | Fariba Dehghani
Source: Nanoscale Advances (review, 2021-12-13)
Field: Materials Science — Materials Chemistry

Carbon dots: a novel platform for biomedical applications Carbon dots (CDs) are a recently synthesised class of carbon-based nanostructures known as zero-dimensional (0D) nanomaterials, which have drawn a great deal of attention owing to their distinctive features, which encompass optical properties (<i>e.g</i>., photoluminescence), ease of passivation, low cost, simple synthetic route, accessibility of precursors and other properties. These newly synthesised nano-sized materials can replace traditional semiconductor quantum dots, which exhibit significant toxicity drawbacks and higher cost. It is demonstrated that their involvement in diverse areas of chemical and bio-sensing, bio-imaging, drug delivery, photocatalysis, electrocatalysis and light-emitting devices consider them as flawless and potential candidates for biomedical application. In this review, we provide a classification of CDs within their extended families, an overview of the different methods of CDs preparation, especially from natural sources, <i>i.e.</i>, environmentally friendly and their unique photoluminescence properties, thoroughly describing the peculiar aspects of their applications in the biomedical field, where we think they will thrive as the next generation of quantum emitters. We believe that this review covers a niche that was not reviewed by other similar publications. Nanotechnology ; Passivation ; Carbon fibers ; Nanomaterials ; Materials science ; Nanostructure ; Computer science ; Composite number ; Composite material ; Layer (electronics) Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Zhongzhou Cheng | Tong Zhao | Haibo Zeng
Source: Small Science (article, 2021-10-18)
Field: Materials Science — Materials Chemistry

2D Material‐Based Photodetectors for Infrared Imaging As a dynamic research field, infrared (IR) detection and imaging presents broad prospect in spectra-chemistry, biomedicine, and artificial intelligence. Due to the excellent properties, 2D materials are expected to overcome the serious manufacturing cost and integration problems of Si-based photodetectors, providing opportunities for the universal application of IR imaging. Up to now, dozens of 2D materials are used to manufacture IR photodetectors. The ultrathin and flexible 2D material-based optoelectronic devices, which are easy to machine and integrate, will demonstrate the application beyond imagination. In this review, first, the principle of IR detection and imaging is introduced, in which the single pixel and array imaging are discussed. Then the promising materials, toward broadband detection and special detection, are introduced in brief and the devices for imaging are summarized, including the mode based on light intensity and the strategy originated from polarization. Finally, it is prospected that the 2D materials have the capability to show the integral IR world, which contains large amount of valuable information such as molecular vibration spectra and significant applications such as artificial retina. Photodetector ; Infrared ; Broadband ; Materials science ; Computer science ; Optoelectronics ; Pixel ; Nanotechnology ; Optics ; Artificial intelligence ; Physics ; Telecommunications 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors ; Nanowire Synthesis and Applications
Authors: Chizoba I. Ezugwu | Shengwei Liu | Chuanhao Li | Serge Zhuiykov | Soumyajit Roy | Francis Verpoort
Source: Coordination Chemistry Reviews (article, 2021-10-18)
Field: Chemistry — Inorganic Chemistry

Engineering metal-organic frameworks for efficient photocatalytic conversion of CO2 into solar fuels Photocatalysis ; Artificial photosynthesis ; Economic shortage ; Metal-organic framework ; Chemistry ; Nanotechnology ; Solar energy ; Solar fuel ; Rational design ; Adsorption ; Biochemical engineering ; Process engineering ; Materials science ; Catalysis ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Selvakumar Murugesan | Thomas Scheibel
Source: Journal of Polymer Science (article, 2021-06-08)
Field: Materials Science — Biomaterials

<scp>Chitosan‐based</scp> nanocomposites for medical applications Abstract Chitosan as a biobased polymer is gaining increasing attention due to its extraordinary physico‐chemical characteristics and properties. While a primary use of chitosan has been in horticultural and agricultural applications for plant defense and to increase crop yield, recent research reports display various new utilizations in the field of advanced biomedical devices, targeted drug delivery, and as bioimaging sensors. Chitosan possesses multiple characteristics such as antimicrobial properties, stimuli‐responsiveness, tunable mechanical strength, biocompatibility, biodegradability, and water‐solubility. Further, chitosan can be processed into nanoparticles, nano‐vehicles, nanocapsules, scaffolds, fiber meshes, and 3D printed scaffolds for a variety of applications. In recent times, nanoparticles incorporated in chitosan matrices have been identified to show superior biological activity, as cells tend to proliferate/differentiate faster when they interact with nanocomposites rather than bulk or micron size substrates/scaffolds. The present article intents to cover chitosan‐based nanocomposites used for regenerative medicine, wound dressings, drug delivery, and biosensing applications. Chitosan ; Biocompatibility ; Drug delivery ; Nanocomposite ; Nanotechnology ; Materials science ; Nanoparticle ; Biodegradation ; Chemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; Electrospun Nanofibers in Biomedical Applications ; Nanoparticles: synthesis and applications
Authors: Ritesh Kumar | Abhishek K. Singh
Source: npj Computational Materials (article, 2021-12-09)
Field: Materials Science — Materials Chemistry

Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts Abstract Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the ML models. Therefore, we devised an intuitive method of interpreting such opaque ML models through SHapley Additive exPlanations (SHAP) values and coupling them with the HT approach for finding efficient 2D water-splitting photocatalysts. We developed a new database of 3099 2D materials consisting of metals connected to six ligands in an octahedral geometry, termed as 2DO (octahedral 2D materials) database. The ML models were constructed using a combination of composition and chemical hardness-based features to gain insights into the thermodynamic and overall stabilities. Most importantly, it distinguished the target properties of the isocompositional 2DO materials differing in bond connectivities by combining the advantages of both elemental and structural features. The interpretable ML regression, classification, and data analysis lead to a new hypothesis that the highly stable 2DO materials follow the HSAB principle. The most stable 2DO materials were further screened based on suitable band gaps within the visible region and band alignments with respect to standard redox potentials using the GW method, resulting in 21 potential candidates. Moreover, HfSe 2 and ZrSe 2 were found to have high solar-to-hydrogen efficiencies reaching their theoretical limits. The proposed methodology will enable materials scientists and engineers to formulate predictive models, which will be accurate, physically interpretable, transferable, and computationally tractable. Octahedron ; Computer science ; HSAB theory ; Band gap ; Artificial intelligence ; Machine learning ; Algorithm ; Biochemical engineering ; Materials science ; Chemistry ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; 2D Materials and Applications
Authors: Mahshid Ahmadi | Maxim Ziatdinov | Yuanyuan Zhou | Eric A. Lass | Sergei V. Kalinin
Source: Joule (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Machine learning for high-throughput experimental exploration of metal halide perovskites Throughput ; Halide ; Materials science ; Perovskite (structure) ; Metal ; Nanotechnology ; Computer science ; Engineering ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Metallurgy ; Telecommunications Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Cinmayii Manliguez | John Y. Chiang
Source: Sensors (article, 2021-02-11)
Field: Chemistry — Analytical Chemistry

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya fruit. Deep learning architectures have gained respect and brought breakthroughs in unimodal processing. This paper suggests a novel non-destructive and multimodal classification using deep convolutional neural networks that estimate fruit maturity by feature concatenation of data acquired from two imaging modes: visible-light and hyperspectral imaging systems. Morphological changes in the sample fruits can be easily measured with RGB images, while spectral signatures that provide high sensitivity and high correlation with the internal properties of fruits can be extracted from hyperspectral images with wavelength range in between 400 nm and 900 nm-factors that must be considered when building a model. This study further modified the architectures: AlexNet, VGG16, VGG19, ResNet50, ResNeXt50, MobileNet, and MobileNetV2 to utilize multimodal data cubes composed of RGB and hyperspectral data for sensitivity analyses. These multimodal variants can achieve up to 0.90 F1 scores and 1.45% top-2 error rate for the classification of six stages. Overall, taking advantage of multimodal input coupled with powerful deep convolutional neural network models can classify fruit maturity even at refined levels of six stages. This indicates that multimodal deep learning architectures and multimodal imaging have great potential for real-time in-field fruit maturity estimation that can help estimate optimal harvest time and other in-field industrial applications. Hyperspectral imaging ; Convolutional neural network ; RGB color model ; Deep learning ; Artificial intelligence ; Computer science ; Maturity (psychological) ; Pattern recognition (psychology) ; Concatenation (mathematics) ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Adeel Y. Abid | Yuanwei Sun | Xu Hou | Congbing Tan | Xiangli Zhong | Ruixue Zhu | Haoyun Chen | Ke Qu | Yuehui Li | Mei Wu | Jingmin Zhang | Jinbin Wang | Kaihui Liu | Xuedong Bai | Dapeng Yu | Xiaoping Ouyang | Jie Wang | Jiangyu Li | Peng Gao
Source: Nature Communications (article, 2021-04-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Creating polar antivortex in PbTiO3/SrTiO3 superlattice Nontrivial topological structures offer a rich playground in condensed matters and promise alternative device configurations for post-Moore electronics. While recently a number of polar topologies have been discovered in confined ferroelectric PbTiO<sub>3</sub> within artificially engineered PbTiO<sub>3</sub>/SrTiO<sub>3</sub> superlattices, little attention was paid to possible topological polar structures in SrTiO<sub>3</sub>. Here we successfully create previously unrealized polar antivortices within the SrTiO<sub>3</sub> of PbTiO<sub>3</sub>/SrTiO<sub>3</sub> superlattices, accomplished by carefully engineering their thicknesses guided by phase-field simulation. Field- and thermal-induced Kosterlitz-Thouless-like topological phase transitions have also been demonstrated, and it was discovered that the driving force for antivortex formation is electrostatic instead of elastic. This work completes an important missing link in polar topologies, expands the reaches of topological structures, and offers insight into searching and manipulating polar textures. Superlattice ; Polar ; Ferroelectricity ; Topology (electrical circuits) ; Network topology ; Field (mathematics) ; Condensed matter physics ; Phase (matter) ; Materials science ; Physics ; Computer science ; Optoelectronics ; Dielectric ; Electrical engineering ; Quantum mechanics ; Mathematics ; Engineering Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials ; Magnetic properties of thin films
Authors: Ziteng Liu | Liqiang Lin | Qingqing Jia | Zheng Cheng | Yanyan Jiang | Yanwen Guo | Jing Ma
Source: Journal of Chemical Information and Modeling (article, 2021-02-25)
Field: Materials Science — Materials Chemistry

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science. However, predicting global electronic structure properties like Frontier molecular orbital highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels and their HOMO-LUMO gaps from the small-sized molecule data to larger molecules remains a challenge. Here, we develop a multilevel attention neural network, named DeepMoleNet, to enable chemical interpretable insights being fused into multitask learning through (1) weighting contributions from various atoms and (2) taking the atom-centered symmetry functions (ACSFs) as the teacher descriptor. The efficient prediction of 12 properties including dipole moment, HOMO, and Gibbs free energy within chemical accuracy is achieved by using multiple benchmarks, both at the equilibrium and nonequilibrium geometries, including up to 110,000 records of data in QM9, 400,000 records in MD17, and 280,000 records in ANI-1ccx for random split evaluation. The good transferability for predicting larger molecules outside the training set is demonstrated in both equilibrium QM9 and Alchemy data sets at the density functional theory (DFT) level. Additional tests on nonequilibrium molecular conformations from DFT-based MD17 data set and ANI-1ccx data set with coupled cluster accuracy as well as the public test sets of singlet fission molecules, biomolecules, long oligomers, and protein with up to 140 atoms show reasonable predictions for thermodynamics and electronic structure properties. The proposed multilevel attention neural network is applicable to high-throughput screening of numerous chemical species in both equilibrium and nonequilibrium molecular spaces to accelerate rational designs of drug-like molecules, material candidates, and chemical reactions. HOMO/LUMO ; Density functional theory ; Artificial neural network ; Chemistry ; Statistical physics ; Electronic structure ; Computer science ; Molecule ; Artificial intelligence ; Machine learning ; Chemical physics ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yapai Song | Mengyang Lu | Gabrielle A. Mandl | Yao Xie | Guotao Sun | Jiabo Chen | Xin Liu | John A. Capobianco | Lining Sun
Source: Angewandte Chemie International Edition (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

Energy Migration Control of Multimodal Emissions in an Er<sup>3+</sup>‐Doped Nanostructure for Information Encryption and Deep‐Learning Decoding Modulating the emission wavelengths of materials has always been a primary focus of fluorescence technology. Nanocrystals (NCs) doped with lanthanide ions with rich energy levels can produce a variety of emissions at different excitation wavelengths. However, the control of multimodal emissions of these ions has remained a challenge. Herein, we present a new composition of Er<sup>3+</sup> -based lanthanide NCs with color-switchable output under irradiation with 980, 808, or 1535 nm light for information security. The variation of excitation wavelengths changes the intensity ratio of visible (Vis)/near-infrared (NIR-II) emissions. Taking advantage of the Vis/NIR-II multimodal emissions of NCs and deep learning, we successfully demonstrated the storage and decoding of visible light information in pork tissue. Ion ; Doping ; Decoding methods ; Fluorescence ; Materials science ; Excitation ; Wavelength ; Nanocrystal ; Lanthanide ; Photon upconversion ; Near-infrared spectroscopy ; Nanostructure ; Photochemistry ; Optoelectronics ; Nanotechnology ; Computer science ; Chemistry ; Telecommunications ; Optics ; Physics Luminescence and Fluorescent Materials ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Zixin Xiong | Lei Zhong | Haotian Wang | Xiaoyan Li
Source: Materials (review, 2021-03-03)
Field: Materials Science — Materials Chemistry

Structural Defects, Mechanical Behaviors, and Properties of Two-Dimensional Materials Since the success of monolayer graphene exfoliation, two-dimensional (2D) materials have been extensively studied due to their unique structures and unprecedented properties. Among these fascinating studies, the most predominant focus has been on their atomic structures, defects, and mechanical behaviors and properties, which serve as the basis for the practical applications of 2D materials. In this review, we first highlight the atomic structures of various 2D materials and the structural and energy features of some common defects. We then summarize the recent advances made in experimental, computational, and theoretical studies on the mechanical properties and behaviors of 2D materials. We mainly emphasized the underlying deformation and fracture mechanisms and the influences of various defects on mechanical behaviors and properties, which boost the emergence and development of topological design and defect engineering. We also further introduce the piezoelectric and flexoelectric behaviors of specific 2D materials to address the coupling between mechanical and electronic properties in 2D materials and the interactions between 2D crystals and substrates or between different 2D monolayers in heterostructures. Finally, we provide a perspective and outlook for future studies on the mechanical behaviors and properties of 2D materials. Materials science ; Monolayer ; Graphene ; Nanotechnology ; Piezoelectricity ; Exfoliation joint ; Deformation (meteorology) ; Engineering physics ; Composite material ; Engineering MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications