jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chaogan Liu | Fangfang Xiao | Yushu Li | Da Lei | Yuan Liu | Xiaowei Ma | Chengzhi Gu | Xincun Dou
Source: Aggregate (article, 2023-01-29)
Field: Materials Science — Materials Chemistry

Intermolecular through‐space charge transfer enabled by bicomponent assembly for ultrasensitive detection of synthetic cannabinoid JWH‐018 Abstract Launching the intermolecular through‐space charge transfer (TSCT) from a bicomponent assembly for photophysical property manipulation is of great significance in fluorescence probe design. Here, we demonstrate the elaborate control of droplet evaporation dynamics for intermolecular TSCT can facilitate the ultrasensitive detection of JWH‐018, a representative synthetic cannabinoid. Driven by diverse intermolecular interactions, the probe, and JWH‐018 assemble in a closely stacked manner to emit strong fluorescence at 477 nm, ascribing to the intermolecular TSCT at the S 2 state. The strategy realizes an ultra‐low limit of detection of 11 nmol/mL and great selectivity towards JWH‐018. The practicability is further verified by constructing a sensing chip for JWH‐018 aerosol detection, which facilitates the on‐site drug abuser screening with the naked eye. Moreover, the proposed assembly‐enabled TSCT is expected to find a variety of applications for optoelectronic materials design and photophysical mechanism‐dominated molecular recognition. Intermolecular force ; Nanotechnology ; Fluorescence ; Materials science ; Naked eye ; Detection limit ; Chemistry ; Chemical physics ; Molecule ; Physics ; Organic chemistry Luminescence and Fluorescent Materials ; Innovative Microfluidic and Catalytic Techniques Innovation ; Supramolecular Chemistry and Complexes
Authors: Zhichao Liu | Simona Baluchová | Bob Brocken | Essraa Ahmed | Paulius Pobedinskas | Ken Haenen | Josephus G. Buijnsters
Source: ACS Applied Materials & Interfaces (article, 2023-08-09)
Field: Materials Science — Materials Chemistry

Inkjet Printing-Manufactured Boron-Doped Diamond Chip Electrodes for Electrochemical Sensing Purposes Fabrication of patterned boron-doped diamond (BDD) in an inexpensive and straightforward way is required for a variety of practical applications, including the development of BDD-based electrochemical sensors. This work describes a simplified and novel bottom-up fabrication approach for BDD-based three-electrode sensor chips utilizing direct inkjet printing of diamond nanoparticles on silicon-based substrates. The whole seeding process, accomplished by a commercial research inkjet printer with piezo-driven drop-on-demand printheads, was systematically examined. Optimized and continuous inkjet-printed features were obtained with glycerol-based diamond ink (0.4% vol/wt), silicon substrates pretreated by exposure to oxygen plasma and subsequently to air, and applying a dot density of 750 drops (volume 9 pL) per inch. Next, the dried micropatterned substrate was subjected to a chemical vapor deposition step to grow uniform thin-film BDD, which satisfied the function of both working and counter electrodes. Silver was inkjet-printed to complete the sensor chip with a reference electrode. Scanning electron micrographs showed a closed BDD layer with a typical polycrystalline structure and sharp and well-defined edges. Very good homogeneity in diamond layer composition and a high boron content (∼2 × 10<sup>21</sup> atoms cm<sup>-3</sup>) was confirmed by Raman spectroscopy. Important electrochemical characteristics, including the width of the potential window (2.5 V) and double-layer capacitance (27 μF cm<sup>-2</sup>), were evaluated by cyclic voltammetry. Fast electron transfer kinetics was recognized for the [Ru(NH<sub>3</sub>)<sub>6</sub>]<sup>3+/2+</sup> redox marker due to the high doping level, while somewhat hindered kinetics was observed for the surface-sensitive [Fe(CN)<sub>6</sub>]<sup>3-/4-</sup> probe. Furthermore, the ability to electrochemically detect organic compounds of different structural motifs, such as glucose, ascorbic acid, uric acid, tyrosine, and dopamine, was successfully verified and compared with commercially available screen-printed BDD electrodes. The newly developed chip-based manufacture method enables the rapid prototyping of different small-scale electrode designs and BDD microstructures, which can lead to enhanced sensor performance with capability of repeated use. Materials science ; Diamond ; Nanotechnology ; Electrode ; Doping ; Electrochemistry ; Boron ; Chip ; Optoelectronics ; Metallurgy ; Electrical engineering Diamond and Carbon-based Materials Research ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: А. К. Ахмедов | А. Х. Абдуев | E. K. Murliev | V. V. Belyaev | А. Ш. Асваров
Source: Materials (article, 2023-05-15)
Field: Materials Science — Materials Chemistry

Transparent Conducting Amorphous IZO Thin Films: An Approach to Improve the Transparent Electrode Quality It is common knowledge that using different oxygen contents in the working gas during sputtering deposition results in fabrication of indium zinc oxide (IZO) films with a wide range of optoelectronic properties. It is also important that high deposition temperature is not required to achieve excellent transparent electrode quality in the IZO films. Modulation of the oxygen content in the working gas during RF sputtering of IZO ceramic targets was used to deposit IZO-based multilayers in which the ultrathin IZO unit layers with high electron mobility (μ-IZO) alternate with ones characterized by high concentration of free electrons (<i>n</i>-IZO). As a result of optimizing the thicknesses of each type of unit layer, low-temperature 400 nm thick IZO multilayers with excellent transparent electrode quality, indicated by the low sheet resistance (<i>R</i> ≤ 8 Ω/sq.) with high transmittance in the visible range (T¯ > 83%) and a very flat multilayer surface, were obtained. Materials science ; Sputtering ; Sheet resistance ; Electrode ; Optoelectronics ; Amorphous solid ; Layer (electronics) ; Transmittance ; Fabrication ; Sputter deposition ; Deposition (geology) ; Thin film ; Ceramic ; Nanotechnology ; Composite material ZnO doping and properties ; Thin-Film Transistor Technologies ; GaN-based semiconductor devices and materials
Authors: Midori Kawashiro | Tatsuya Mori | Masato Ito | Naoki Ando | Shigehiro Yamaguchi
Source: Angewandte Chemie International Edition (article, 2023-04-04)
Field: Chemistry — Organic Chemistry

Photodissociative Modules that Control Dual‐Emission Properties in Donor–π–Acceptor Organoborane Fluorophores Donor-π-acceptor fluorophores that consist of an electron-donating amino group and an electron-accepting triarylborane moiety generally exhibit substantial solvatochromism in their fluorescence while retaining high fluorescence quantum yields even in polar media. Herein, we report a new family of this compound class, which bears ortho-P(=X)R<sub>2</sub> -substituted phenyl groups (X=O or S) as a photodissociative module. The P=X moiety that intramolecularly coordinates to the boron atom undergoes dissociation in the excited state, giving rise to dual emission from the corresponding tetra- and tricoordinate boron species. The susceptibility of the systems to photodissociation depends on the coordination ability of the P=O and P=S moieties, whereby the latter facilitates dissociation. The intensity ratios of the dual emission bands are sensitive to environmental parameters, including temperature, solution polarity, and the viscosity of the medium. Moreover, precise tuning of the P(=X)R<sub>2</sub> group and the electron-donating amino moiety led to single-molecule white emission in solution. Moiety ; Chemistry ; Solvatochromism ; Excited state ; Fluorescence ; Dissociation (chemistry) ; Photochemistry ; Acceptor ; Molecule ; Quantum yield ; Photodissociation ; Chemical polarity ; Boron ; Stereochemistry ; Physical chemistry ; Organic chemistry ; Atomic physics Organoboron and organosilicon chemistry ; Luminescence and Fluorescent Materials ; Boron Compounds in Chemistry
Authors: Tadeáš Hanuš | Javier Arias‐Zapata | Bouraoui Ilahi | Philippe‐Olivier Provost | Jinyoun Cho | Kristof Dessein | Abderraouf Boucherif
Source: Advanced Materials Interfaces (article, 2023-04-13)
Field: Materials Science — Materials Chemistry

Large‐Scale Formation of Uniform Porous Ge Nanostructures with Tunable Physical Properties Abstract Porous germanium (PGe) nanostructures attract a lot of attention for various emerging applications due to their unique properties. Consequently, there is an increasing need for the development of low‐cost synthesis routes that are compatible with large‐scale production. Bipolar electrochemical etching (BEE) is a widely used solution for producing porous Ge layers. However, the lack of controllable production of large‐scale uniform PGe layers is the limiting factor for mainstream applications. Large‐scale homogeneous PGe layers formation is demonstrated by improving the BEE process. The PGe structures demonstrate excellent homogeneity in thickness and porosity, with a relative variation of below 2% across the 100 mm wafer. Furthermore, this process enables accurate tuning of the PGe's physical properties through variation of the etching parameters. PGe structures with porosity ranging from 40% to 80% and an adjustable thickness, while preserving low surface roughness are demonstrated, giving access to a large variety of PGe nanostructures for a wide range of applications. Ellipsometry and X‐ray reflectivity are employed to measure the porosity and thickness of PGe layers, providing fast and non‐destructive methods of characterization. These findings lay the groundwork for the large‐scale production of high‐quality PGe layers with on‐demand characteristics. Materials science ; Porosity ; Wafer ; Surface roughness ; Surface finish ; Nanostructure ; Ellipsometry ; Etching (microfabrication) ; Nanotechnology ; Alloy ; Composite material ; Thin film ; Layer (electronics) Silicon Nanostructures and Photoluminescence ; Semiconductor materials and devices ; Nanowire Synthesis and Applications
Authors: Martin Fritsch | Martin Wolter
Source: IEEE Transactions on Instrumentation and Measurement (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Saturation of High-Frequency Current Transformers: Challenges and Solutions When using high-frequency current transformers (HFCT) to measure partial discharges (PD) on power cables, core saturation caused by the 50 Hz operating current of the power cable is a major problem. Saturation leads to nonlinearity in the transfer function of the HFCT sensor and significantly reduces its sensitivity to the PD signals. To avoid magnetic saturation and ensure linear HFCT operation and thus maximum PD sensitivity, a split ferrite core can be used. However, finding the right air gap length between the two halves of the core is a complicated task, as the optimal length depends on several parameters and should be neither too small nor too large. This article deals with the air gap problem in detail and presents solutions for determining the optimal air gap length of HFCT cores. We present numerous measurements with exemplary split-core HFCTs from three different ferrite materials. Based on the obtained data, we then derive mathematical functions describing the optimal air gap length of the different ferrite cores. Based on these optimal air gap functions, we then derive and validate an HFCT split-core model. Using the model, the optimal air gap function for any ferrite material can be calculated with a few simple equations, significantly speeding up the air gap design process. In this way, the model improves the computer-aided design of split-core HFCTs. With optimal air gap length, the HFCT does not saturate and its sensitivity for PD measurements is maximized. Our results show that for online monitoring of power cables, the optimal air gap length depends mainly on the amplitude of the 50 Hz current and thus changes with the load on the power cable. Therefore, the conventional HFCT design with constant air gap length is not practical. Instead, we want to develop an improved HFCT with active air gap control in the future. Current transformer ; Ferrite core ; Transformer ; Air gap (plumbing) ; Electronic engineering ; Saturation (graph theory) ; Sensitivity (control systems) ; Materials science ; Magnetic core ; Transfer function ; Acoustics ; Engineering ; Electrical engineering ; Mathematics ; Physics ; Voltage ; Electromagnetic coil High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Electrostatic Discharge in Electronics
Authors: Natasha Videcrantz Faurschou | Rolf H. Taaning | Christian Pedersen
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Substrate specific closed-loop optimization of carbohydrate protective group chemistry using Bayesian optimization and transfer learning A new way of performing reaction optimization within carbohydrate chemistry is presented. This is done by performing closed-loop optimization of regioselective benzoylation of unprotected glycosides using Bayesian optimization. Both 6-<i>O</i>-monobenzoylations and 3,6-<i>O</i>-dibenzoylations of three different monosaccharides are optimized. A novel transfer learning approach, where data from previous optimizations of different substrates is used to speed up the optimizations, has also been developed. The optimal conditions found by the Bayesian optimization algorithm provide new insight into substrate specificity, as the conditions found are significantly different. In most cases, the optimal conditions include Et<sub>3</sub>N and benzoic anhydride, a new reagent combination for these reactions, discovered by the algorithm, demonstrating the power of this concept to widen the chemical space. Further, the developed procedures include ambient conditions and short reaction times. Bayesian optimization ; Reagent ; Chemistry ; Bayesian probability ; Monosaccharide ; Regioselectivity ; Chemical space ; Substrate (aquarium) ; Carbohydrate chemistry ; Computer science ; Transfer of learning ; Combinatorial chemistry ; Organic chemistry ; Artificial intelligence ; Catalysis ; Drug discovery Carbohydrate Chemistry and Synthesis ; Chemical Synthesis and Analysis ; Glycosylation and Glycoproteins Research
Authors: Katherine McCullough | Daniel S. King | Saumil Chheda | Magali Ferrandon | Timothy A. Goetjen | Zoha H. Syed | Trent R. Graham | Nancy Washton | Omar K. Farha | Laura Gagliardi | Massimiliano Delferro
Source: ACS Central Science (article, 2023-01-26)
Field: Chemistry — Inorganic Chemistry

High-Throughput Experimentation, Theoretical Modeling, and Human Intuition: Lessons Learned in Metal–Organic-Framework-Supported Catalyst Design We have screened an array of 23 metals deposited onto the metal-organic framework (MOF) NU-1000 for propyne dimerization to hexadienes. By a first-of-its-kind study utilizing data-driven algorithms and high-throughput experimentation (HTE) in MOF catalysis, yields on Cu-deposited NU-1000 were improved from 0.4 to 24.4%. Characterization of the best-performing catalysts reveal conversion to hexadiene to be due to the formation of large Cu nanoparticles, which is further supported by reaction mechanisms calculated with density functional theory (DFT). Our results demonstrate both the strengths and weaknesses of the HTE approach. As a strength, HTE excels at being able to find interesting and novel catalytic activity; any <i>a priori</i> theoretical approach would be hard-pressed to find success, as high-performing catalysts required highly specific operating conditions difficult to model theoretically, and initial simple single-atom models of the active site did not prove representative of the nanoparticle catalysts responsible for conversion to hexadiene. As a weakness, our results show how the HTE approach must be designed and monitored carefully to find success; in our initial campaign, only minor catalytic performances (up to 4.2% yield) were achieved, which were only improved following a complete overhaul of our HTE approach and questioning our initial assumptions. Intuition ; Computer science ; Throughput ; Nanotechnology ; Biochemical engineering ; Systems engineering ; Engineering ; Materials science ; Cognitive science ; Psychology Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Atta Ur Rehman | Sehrish Sharif | H.H. Hegazy | Nicola Morley | Nasir Amin | Maria Akhtar | Muhammad Imran Arshad | Zahid Farooq | Z. Munir | Talha Munir
Source: Materials Today Communications (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Low dielectric loss, and enhanced magneto-dielectric properties of Cu0.5Cd0.5-CoFe2O4 ferrites via Co2+ substitution Materials science ; Dielectric ; Spinel ; Analytical Chemistry (journal) ; Dielectric loss ; Lattice constant ; Coercivity ; Doping ; Dissipation factor ; Nuclear magnetic resonance ; Condensed matter physics ; Diffraction ; Optoelectronics ; Optics ; Metallurgy Magnetic Properties and Synthesis of Ferrites ; Multiferroics and related materials ; Electromagnetic wave absorption materials
Authors: Jinming Lai | Likang Xiao | Zhengwei Xiong | Leiming Fang | Wenkun Zhu | Fangguang Kuang | Zhipeng Gao
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced soft magnetic properties and high-frequency stability of FeNiMo powder cores by coating SiO<sub>2</sub> insulation layer FeNiMo/SiO<sub>2</sub> powder cores were prepared using the sol-gel method. Tetraethyl orthosilicate (TEOS) was added to produce an amorphous SiO<sub>2</sub> coating outside the FeNiMo particles to form a core-shell structure. The thickness of the SiO<sub>2</sub> layer was designed by varying the concentration of TEOS, and the optimized permeability and magnetic loss of the powder core could achieve 78.15 and 633.44 kW m<sup>-3</sup> at 100 kHz and 100 mT, respectively. Compared with other soft magnetic composites, these FeNiMo/SiO<sub>2</sub> powder cores have a significantly higher effective permeability and a lower core loss. Surprisingly, the high-frequency stability of permeability could be substantially enhanced through the insulation coating process in which <i>μ</i><sub>f</sub>/<i>μ</i><sub>100 kHz</sub> could reach 98.7% at 1 MHz. In comparison with 60<i>μ</i> commercial products, the comprehensive soft magnetic properties of the FeNiMo/SiO<sub>2</sub> cores were superior to most manufacturers, which would be potentially applied to high-performance inductance devices in high-frequency ranges. Coating ; Materials science ; Layer (electronics) ; Composite material Magnetic Properties of Alloys ; Magnetic properties of thin films ; Metallic Glasses and Amorphous Alloys
Authors: Kumar Ayush | Abhishek Seth | Tarak K. Patra
Source: Soft Matter (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

nanoNET: machine learning platform for predicting nanoparticles distribution in a polymer matrix Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relationship in PNCs due to their wide-ranging composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure relation of a PNC through an intelligent machine-learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the distribution of NPs in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-microstructure relationships in PNCs and other molecular systems. Computer science ; Pipeline (software) ; Artificial intelligence ; Convolutional neural network ; Deep learning ; Smart polymer ; Machine learning ; Biological system ; Materials science ; Polymer Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Xueyun Gao | Haiyan Wang | Huijie Tan | Lei Xing | Zhiyu Hu
Source: Materials Today Communications (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Data-driven machine learning for alloy research: Recent applications and prospects Alloy ; Materials science ; Workflow ; Microstructure ; Process (computing) ; Field (mathematics) ; Phase (matter) ; Process engineering ; Hydrogen storage ; Artificial intelligence ; Mechanical engineering ; Machine learning ; Nanotechnology ; Computer science ; Systems engineering ; Metallurgy ; Database ; Engineering ; Physics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Ingo Dierking | Jason Dominguez | James Harbon | Joshua Heaton
Source: Frontiers in Soft Matter (article, 2023-02-03)
Field: Chemistry — Spectroscopy

Deep learning techniques for the localization and classification of liquid crystal phase transitions Deep Learning techniques such as supervised learning with convolutional neural networks and inception models were applied to phase transitions of liquid crystals to identify transition temperatures and the respective phases involved. In this context achiral as well as chiral systems were studied involving the isotropic liquid, the nematic phase of solely orientational order, fluid smectic phases with one-dimensional positional order and hexatic phases with local two-dimensional positional, so-called bond-orientational order. Discontinuous phase transition of 1st order as well as continuous 2nd order transitions were investigated. It is demonstrated that simpler transitions, namely Iso-N, Iso-N*, and N-SmA can accurately be identified for all unseen test movies studied. For more subtle transitions, such as SmA*-SmC*, SmC*-SmI*, and SmI*-SmF*, proof-of-principle evidence is provided, demonstrating the capability of deep learning techniques to identify even those transitions, despite some incorrectly characterized test movies. Overall, we demonstrate that with the provision of a substantial and varied dataset of textures there is no principal reason why one could not develop generalizable deep learning techniques to automate the identification of liquid crystal phase sequences of novel compounds. Liquid crystal ; Phase transition ; Artificial intelligence ; Context (archaeology) ; Deep learning ; Isotropy ; Phase (matter) ; Convolutional neural network ; Computer science ; Materials science ; Condensed matter physics ; Physics ; Optics ; Quantum mechanics Molecular spectroscopy and chirality ; Liquid Crystal Research Advancements ; Complex Systems and Time Series Analysis