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Register a new userHigh-Throughput Production of Cheap Mineral-Based 2D Electrocatalysts for High-Current-Density Hydrogen Evolution
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The high-throughput scalable production of cheap, efficient and durable electrocatalysts that work well at high current densities demanded by industry is a great challenge for the large-scale implementation of electrochemical technologies. Here we report the production of a 2D MoS2-based ink-type electrocatalyst by a scalable top-down exfoliation technique followed by a simple heat treatment. The catalyst shows a high current density of 1000 mA cm^-2 at an overpotential of 454 mV for the hydrogen evolution reaction (HER) without the need of iR correction, as well as good stability over 24 hours. Using the same method, we have, for the first time, produced a cheap MoS2 mineral-based catalyst and found that it had an excellent performance for high-current-density HER. Noteworthy, production rate of this MoS2-based catalyst is one to two orders of magnitude higher than those previously reported. In addition, the price of the MoS2 mineral is five orders of magnitude lower than commercial Pt catalysts, making the MoS2 mineral-based catalyst cheap, and the ink-type catalyst dispersions can be easily integrated with other technologies for large-scale catalyst electrode preparation. These advantages indicate the huge potentials of this method and mineral-based cheap and abundant natural resources as catalysts in the electrochemical technologies.
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Large scale production of hydrogen by electrochemical water splitting is considered as a promising technology to address critical energy challenges caused by the extensive use of fossil fuels. Although nonprecious nickel-based catalysts work well at low current densities, they need large overpotentials at high current densities that hinders their potential applications in practical industry. Here we report a hydroxide-mediated nickel based electrocatalyst for high current density hydrogen evolution, which delivers a current density of 1000 mA cm-2 at an overpotential of 98 mV. Combined X-ray absorption spectroscopy and high resolution X-ray photoelectron spectroscopy results show charge redistribution of nickel sites caused by Mo and surface FeOx clusters, which can stabilize the surface nickel hydroxide at high current densities for promoting water dissociation step. Such catalyst is synthesized at the metre scale and shows a current density of 500 mA cm-2 at 1.56 V in the overall water splitting, which demonstrate its potential for practical use. This work highlights a charge-engineering strategy for rational design of catalysts that work well at high current densities.
Two-dimensional (2D) materials have many promising applications, but their scalable production remains challenging. Herein, we develop a glue-assisted grinding exfoliation (GAGE) method in which the adhesive polymer acts as a glue to massively produce 2D materials with large lateral sizes, high quality, and high yield. Density functional theory simulation shows that the exfoliation mechanism involves the competition between the binding energy of selected polymers and the 2D materials which is larger than the exfoliation energy of the layered materials. Taking h-BN as an example, the GAGE produces 2D h-BN with an average lateral size of 2.18 {mu}m and thickness of 3.91 nm. The method is also extended to produce various other 2D materials, including graphene, MoS2, Bi2O2Se, vermiculite, and montmorillonite. Two representative applications of thus-produced 2D materials have been demonstrated, including h-BN/polymer composites for insulating thermal conduction and MoS2 electrocatalysts for large-current-density hydrogen evolution, indicating the great potential of massively produced 2D materials.
Combining high-throughput experiments with machine learning allows quick optimization of parameter spaces towards achieving target properties. In this study, we demonstrate that machine learning, combined with multi-labeled datasets, can additionally be used for scientific understanding and hypothesis testing. We introduce an automated flow system with high-throughput drop-casting for thin film preparation, followed by fast characterization of optical and electrical properties, with the capability to complete one cycle of learning of fully labeled ~160 samples in a single day. We combine regio-regular poly-3-hexylthiophene with various carbon nanotubes to achieve electrical conductivities as high as 1200 S/cm. Interestingly, a non-intuitive local optimum emerges when 10% of double-walled carbon nanotubes are added with long single wall carbon nanotubes, where the conductivity is seen to be as high as 700 S/cm, which we subsequently explain with high fidelity optical characterization. Employing dataset resampling strategies and graph-based regressions allows us to account for experimental cost and uncertainty estimation of correlated multi-outputs, and supports the proving of the hypothesis linking charge delocalization to electrical conductivity. We therefore present a robust machine-learning driven high-throughput experimental scheme that can be applied to optimize and understand properties of composites, or hybrid organic-inorganic materials.
In this work it is reported a vertical electrolyte transistor (VET) whose structure is based on stacked layers as described below: bottom contact (source or drain) - channel - permeable intermediate electrode (drain or source) - ion gel (electrolyte
gate dielectric) - gate top contact. This VET depicts versatility to work as Electrolyte-Gated Vertical Organic Field Effect Transistor (Electrolyte-Gated VOFET) or Vertical Organic Electrochemical Transistor (VOECT) as never reported before. The distinction of these operation modes is regarding to the transistor transconductance that occurs due to induced charge carriers or ionic current, respectively. Both modes of operation show that this VET is able to work at very low voltage range and drive a high current density. These observed features make VETs a good candidate for applications in iontronic devices, bio-sensors and/or very low power optoelectronic circuits.
High throughput experimental methods are known to accelerate the rate of research, development, and deployment of electronic materials. For example, thin films with lateral gradients in composition, thickness, or other parameters have been used alongside spatially-resolved characterization to assess how various physical factors affect material properties under varying measurement conditions. Similarly, multi-layer electronic devices that contain such graded thin films as one or more of their layers can also be characterized spatially in order to optimize the performance. In this work, we apply these high throughput experimental methods to thin film transistors (TFTs), demonstrating combinatorial device fabrication and semi-automated characterization using sputtered Indium-Gallium-Zinc-Oxide (IGZO) TFTs as a case study. We show that both extrinsic and intrinsic types of device gradients can be generated in a TFT library, such as channel thickness and length, channel cation compositions, and oxygen atmosphere during deposition. We also present a semi-automated method to measure the 44 devices fabricated on a 50x50mm substrate that can help to identify properly functioning TFTs in the library and finish the measurement in a short time. Finally, we propose a fully automated characterization system for similar TFT libraries, which can be coupled with high throughput data analysis. These results demonstrate that high throughput methods can accelerate the investigation of TFTs and other electronic devices.
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