Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We ...simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.
The layered sodium transition metal oxide, NaTMO2 (TM = transition metal), with a binary or ternary phases has displayed outstanding electrochemical performance as a new class of strategy cathode ...materials for sodium‐ion batteries (SIBs). Herein, an in‐depth phase analysis of developed Na1−xTMO2 cathode materials, Na0.76Ni0.20Fe0.40Mn0.40O2 with P2‐ and O3‐type phases (NFMO‐P2/O3) is offered. Structural visualization on an atomic scale is also provided and the following findings are unveiled: i) the existence of a mixed‐phase intergrowth layer distribution and unequal distribution of P2 and O3 phases along two different crystal plane indices and ii) a complete reversible charge/discharge process for the initial two cycles that displays a simple phase transformation, which is unprecedented. Moreover, first‐principles calculations support the evidence of the formation of a binary NFMO‐P2/O3 compound, over the proposed hypothetical monophasic structures (O3, P3, O′3, and P2 phases). As a result, the synergetic effect of the simultaneous existence of P‐ and O‐type phases with their unique structures allows an extraordinary level of capacity retention in a wide range of voltage (1.5–4.5 V). It is believed that the insightful understanding of the proposed materials can introduce new perspectives for the development of high‐voltage cathode materials for SIBs.
In‐depth phase analysis of developed Na1−xTMO2 cathode materials, NFMO with P2‐ and O3‐type phases (NFMO‐P2/O3) is offered. As a result, the synergetic effect of the simultaneous existence of P‐ and O‐type phases with their unique structures allows an extraordinary level of capacity retention in a wide range of voltage (1.5–4.5 V).
Colloid Surfactants for Emulsion Stabilization Kim, Jin-Woong; Lee, Daeyeon; Shum, Ho Cheung ...
Advanced materials (Weinheim),
September 3, 2008, Letnik:
20, Številka:
17
Journal Article
Recenzirano
Colloid surfactants are fabricated with precisely controlled geometry and used for emulsion stabilization. These amphiphilic dimer particles (left) combine the benefits of emulsion stabilization of ...particles and the amphiphilicity of molecular surfactants to afford better emulsion stabilization. Remarkably, these colloidal surfactants stabilize not only spherical emulsion droplets but also nonspherical ones (right).
Abstract
Predicting mechanical properties such as yield strength (YS) and ultimate tensile strength (UTS) is an intricate undertaking in practice, notwithstanding a plethora of well-established ...theoretical and empirical models. A data-driven approach should be a fundamental exercise when making YS/UTS predictions. For this study, we collected 16 descriptors (attributes) that implicate the compositional and processing information and the corresponding YS/UTS values for 5473 thermo-mechanically controlled processed (TMCP) steel alloys. We set up an integrated machine-learning (ML) platform consisting of 16 ML algorithms to predict the YS/UTS based on the descriptors. The integrated ML platform involved regularization-based linear regression algorithms, ensemble ML algorithms, and some non-linear ML algorithms. Despite the dirty nature of most real-world industry data, we obtained acceptable holdout dataset test results such as R
2
> 0.6 and MSE < 0.01 for seven non-linear ML algorithms. The seven fully trained non-linear ML models were used for the ensuing ‘inverse design (prediction)’ based on an elitist-reinforced, non-dominated sorting genetic algorithm (NSGA-II). The NSGA-II enabled us to predict solutions that exhibit desirable YS/UTS values for each ML algorithm. In addition, the NSGA-II-driven solutions in the 16-dimensional input feature space were visualized using holographic research strategy (HRS) in order to systematically compare and analyze the inverse-predicted solutions for each ML algorithm.
High‐resolution 3D‐printable hydrogels with high mechanical strength and biocompatibility are in great demand because of their potential applications in numerous fields. In this study, a material ...system comprising Pluronic F‐127 dimethacrylate (FDMA) is developed to function as a direct ink writing (DIW) hydrogel for 3D printing. FDMA is a triblock copolymer that transforms into micelles at elevated temperatures. The transformation increases the viscosity of FDMA and preserves its structure during DIW 3D printing, whereupon the printed structure is solidified through photopolymerization. Because of this viscosity shift, various functionalities can be incorporated through the addition of other materials in the solution state. Acrylic acid is incorporated into the pregel solution to enhance the mechanical strength, because the carboxylate group of poly(acrylic acid) ionically crosslinks with Fe3+, increasing the toughness of the DIW hydrogel 37 times to 2.46 MJ m−3. Tough conductive hydrogels are also 3D printed by homogenizing poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate into the pregel solution. Furthermore, the FDMA platform developed herein uses DIW, which facilitates multicartridges 3D printing, and because all the materials included are biocompatible, the platform may be used to fabricate complex structures for biological applications.
In this work, Pluronic F‐127 dimethacrylate is evaluated as a platform for extrusion hydrogel 3D printing. The platform can be incorporated with additional materials to achieve specific functionalities such as enhanced mechanical strength and conductivity. The platform is also biocompatible, which makes it promising for the fabrication of complex structures for biological applications by 3D printing.
Gold nanoparticles (AuNPs) are used in various biological applications because of their small surface area-to-volume ratios, ease of synthesis and modification, low toxicity, and unique optical ...properties. These properties can vary significantly with changes in AuNP size, shape, composition, and arrangement. Thus, the stabilization of AuNPs is crucial to preserve the properties required for biological applications. In recent years, various polymer-based physical and chemical methods have been extensively used for AuNP stabilization. However, a new stabilization approach using biomolecules has recently attracted considerable attention. Biomolecules such as DNA, RNA, peptides, and proteins are representative of the biomoieties that can functionalize AuNPs. According to several studies, biomolecules can stabilize AuNPs in biological media; in addition, AuNP-conjugated biomolecules can retain certain biological functions. Furthermore, the presence of biomolecules on AuNPs significantly enhances their biocompatibility. This review provides a representative overview of AuNP functionalization using various biomolecules. The strategies and mechanisms of AuNP functionalization using biomolecules are comprehensively discussed in the context of various biological fields.
High-performance tube-supported standard oil synthetic zeolite-13 (SSZ-13) membranes were prepared using low-temperature ozone calcination and modularized in different-sized permeation cells. The ...hydrophobic SSZ-13 membrane exhibited robust, marked CO2/N2 separation performances at a H2O vapor partial pressure of 10 kPa at 50 °C (CO2 permeance of 1.3 × 10−7 mol∙m−2 s−1∙Pa−1 and CO2/N2 separation factor (SF) of ca. 31.5). However, these intrinsic values were obtained at high feed flow rates, where the optimal recovery of CO2 molecules cannot be obtained. Thus, we correlated membrane (permeance and SF) and feed stream (Reynolds number) properties, finding that convective mass transfer from feed to outer membrane surface (Sherwood number) was described by the Reynolds number and cell dimensions. This further accounted for the CO2 molar flux and CO2/N2 SF. Based on this, we proposed critical parameters (comprising total feed flow rate and pressure, and characteristic module dimension) to describe the representative module properties of the recovery, purity, and process efficiency (PE) for CO2. Finally, the PE of the membrane unit was improved in double-stage configuration, yielding noticeable improvements in CO2 purity and PE at the slight expense of CO2 recovery. Crucially, the process-based module properties were well understood and predicted using the feed stream properties.
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•Low-temperature ozone calcination led to high-quality CHA zeolite membranes.•Separation performances from membrane and module perspectives were correlated.•Feed properties accounted for permeance and separation factor (membrane properties).•Feed properties accounted for recovery, purity, and efficiency (module properties).•CO2 separation efficiency was predictable and improved by combining two modules.
Most data-driven machine learning (ML) approaches established in metallurgy research fields are focused on a build-up of reliable quantitative models that predict a material property from a given set ...of material conditions. In general, the input feature dimension (the number of material condition variables) is much higher than the output feature dimension (the number of material properties of concern). Rather than such a forward-prediction ML model, it is necessary to develop so-called inverse-design modeling, wherein required material conditions could be deduced from a set of desired material properties. Here we report a novel inverse design strategy that employs two independent approaches: a metaheuristics-assisted inverse reading of conventional forward ML models and an atypical inverse ML model based on a modified variational autoencoder. These two unprecedented approaches were successful and led to overlapped results, from which we pinpointed several novel thermo-mechanically controlled processed (TMCP) steel alloy candidates that were validated by a rule-based thermodynamic calculation tool (Thermo-Calc.). We also suggested a practical protocol to elucidate how to treat engineering data collected from industry, which is not prepared as independent and identically distributed (IID) random data.
Wound closure is a critical step in postoperative wound recovery. Substantial advancements have been made in many different means of facilitating wound closure, including the use of tissue adhesives. ...Compared to conventional methods, such as suturing, tissue bioadhesives better accelerate wound closure. However, several existing tissue adhesives suffer from cytotoxicity, inadequate tissue adhesive strength, and high costs. In this study, a series of bioadhesives was produced using non-swellable spider silk-derived silk fibroin protein and an outer layer of swellable polyethylene glycol and tannic acid. The gelation time of the spider silk-derived silk fibroin protein bioadhesive is less than three minutes and thus can be used during rapid surgical wound closure. By adding polyethylene glycol (PEG) 2000 and tannic acid as co-crosslinking agents to the N-Hydroxysuccinimide (NHS), and 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) reaction, the adhesive strength of the bioadhesive became 2.5 times greater than that of conventional fibrin glue adhesives. Silk fibroin bioadhesives do not show significant cytotoxicity in vitro compared with other bioadhesives. In conclusion, silk fibroin bioadhesive is promising as a new medical tool for more effective and efficient surgical wound closure, particularly in bone fractures.
Highly stretchable 2D fabrics are prepared by weaving fibers for a fabric-structured triboelectric nanogenerator (FTENG). The fibers mainly consist of Al wires and polydimethylsiloxane (PDMS) tubes ...with a high-aspect-ratio nanotextured surface with vertically aligned nanowires. The fabrics were produced by interlacing the fibers, which was bonded to a waterproof fabric for all-weather use for fabric-structured triboelectric nanogenerator (FTENG). It showed a stable high-output voltage and current of 40 V and 210 μA, corresponding to an instantaneous power output of 4 mW. The FTENG also exhibits high robustness behavior even after 25% stretching, enough for use in smart clothing applications and other wearable electronics. For wearable applications, the nanogenerator was successfully demonstrated in applications of footstep-driven large-scale power mats during walking and power clothing attached to the elbow.
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