1,721,017 research outputs found
Synthesis of Sulfur-Doped PtRuNi Alloy Catalyst for Efficient Hydrogen Evolution Reaction
No full textGreen hydrogen production via electrocatalytic water splitting is a promising strategy for enabling renewable energy technologies. To improve hydrogen generation efficiency, extensive efforts have been devoted to developing electrocatalysts with lower energy requirements and higher stability. Among these, randomly mixed alloy catalysts have attracted significant attention due to their ability to exhibit synergistic effects surpassing those of single-component materials. Here, the synthesis of a sulfur-doped PtRuNi alloy catalyst for efficient hydrogen evolution reaction (HER) using carbothermal shock (CTS) method is reported. This rapid, high temperature synthesis technique enabled the formation of PtRuNi/S alloy nanoparticles with a finely tuned local electronic structure, driven by sulfur incorporation. The resulting catalyst exhibited outstanding HER activity in both acidic and alkaline solution, with overpotentials of 23.3 and 23.9 mV, respectively. Compared to Pt catalyst synthesized on the same substrate, the sulfur-doped alloy demonstrated positive overpotential shifts of approximate to 50 mV in acidic and 90 mV in alkaline environment, as well as significantly enhanced kinetics and electrochemical stability. This work not only presents an efficient and scalable synthesis strategy for heteroatom-doped alloy catalysts but also provides a promising platform for broader applications in other fields requiring tunable electronic structures and long-term durability.
Selective Electrocatalytic CO2 Reduction into CO Using Au-Coated Dendritic Silica Nanoparticle Arrays
No full textHighly selective electrocatalytic CO2 reductionforCO production has attracted tremendous attention for achieving theforthcoming goals of carbon neutrality and widespread industrial utilizationand recycling of carbon. Among various approaches, the structuralcontrol of the catalyst is particularly interesting because of thefacile control of CO2 reduction conditions, such as reactionmedia and reaction pathways. Thus far, a wide range of nanostructuredcatalysts, including Au needle tips, Au nanowires, and Au wrinkles,have been used for the enhancement of the selectivity of CO production.In this study, an electrocatalyst with a hierarchical nanostructurefor the highly selective production of CO is reported. This hierarchicalstructure is fabricated by the deposition of Au via e-beam evaporationon a dendritic fibrous nanosilica (KCC-1) template, which is a sphericalsilica particle consisting of uniformly distributed center-radialfibers. The conversion efficiency of this catalyst is strongly affectedby the thickness of the Au deposited on the KCC-1 template, and thehighest CO selectivity of similar to 98% (at -0.5 V vs. RHE) isobtained at an optimum Au thickness of 50 nm. According to the CO2 electrocatalytic reduction results obtained from KCC-1 withdendritic fibers and a conventional spherical particle without thefibers under various electrolyte conditions, such selectivity enhancementof Au on the KCC-1 template is attributed to the increase in the localpH near the hierarchical catalyst surface. This work provides potentialpromising templates that exhibit a unique nanostructure for efficientelectrocatalysis.
Solution-free synthesis of MXene composite hybrid nanostructures by rapid Joule heating
No full textMXene-based composite hybrid nanostructures have attracted considerable attention in recent years due to their potential for enhanced electrochemical, electronic and optical performances. However, conventional solution-based methods for fabricating MXene composites suffer from the drawback of MXene oxidation during synthesis. In this study, we present a solution-free approach using rapid Joule heating to overcome this limitation. By applying rapid thermal shock to the MXene substrate loaded with precursors, we successfully synthesized MXene composite hybrid nanostructures incorporating various components, including Pt, Co, Cu, Ni, Fe, Pd, and their alloys. Our experimental results show that the rapid Joule heating technique has several significant advantages, including the ability to synthesize various MXene composite hybrid nanostructures with minimized MXene oxidation, uniform distribution of hybrid components without severe aggregation, and homogeneous polyelemental alloy synthesis. We demonstrate the effectiveness of our approach through the synthesis of a Pt-MXene nanocomposite, showing remarkable electrocatalytic activity for the HER. The Pt-MXene exhibits a low overpotential for the HER and excellent stability, arising from the preserved active sites on MXenes, uniform distribution of Pt nanoparticles, and strong interaction between the metal and MXene. The rapid Joule heating technique presented in this study enables the successful synthesis of a wide range of hybrid materials without compromising the unique properties of MXenes, making them suitable for various applications where the synergistic effect of MXene composites can yield significant performance enhancements. A rapid Joule heating technique enables the successful synthesis of a wide range of hybrid materials without compromising the unique properties of MXenes making them suitable for various applications where the synergistic effect of MXene composites can yield significant performance enhancements.
Effect of Feature Shape and Dimension of a Confinement Geometry on Selectivity of Electrocatalytic CO2 Reduction
No full textThe local confinement effect, which can generate a high concentration of hydroxide ions and reaction intermediates near the catalyst surface, is an important strategy for converting CO2 into multi-carbon products in electrocatalytic CO2 reduction. Therefore, understanding how the shape and dimension of the confinement geometry affect the product selectivity is crucial. In this study, we report for the first time the effect of the shape (degree of confinement) and dimension of the confined space on the product selectivity without changing the intrinsic property of Cu. We demonstrate that geometry influences the outcomes of products, such as CH4, C2H4, and EtOH, in different ways: the selectivity of CH4 and EtOH is affected by shape, while the selectivity of C2H4 is influenced by dimension of geometry predominantly. These phenomena are demonstrated, both experimentally and through simulation, to be induced by the local confinement effect within the confined structure. Our geometry model could serve as basis for designing the confined structures tailored for the production of specific products.
Transparent Au Nanopatterned Catalysts: A Strategy for Improved Light Absorption in Photoelectrochemical CO2-to-Syngas Conversion
No full textPhotoelectrochemical (PEC) catalysis is a promising approach for converting solar energy into chemical fuels, but a fundamental challenge lies in balancing catalytic activity with efficient light absorption. Catalyst surface coverage on light-harvesting supports often leads to a trade-off that limits overall performance. To address this issue, we developed a high-aspect-ratio (>20) gold (Au) nanopatterned catalyst designed to maximize both light harvesting and catalytic efficiency. Compared to conventional film-type catalysts, the nanopatterned catalyst exhibits a 3.6-fold increase in electrochemical surface area and a 2.5-fold improvement in transparency, enabling greater light transmission to the photoabsorber. In the reduction of CO2 to syngas (H-2:CO = 1:1), the nanopatterned catalyst achieves a 240 mV lower onset potential and a 6.2-fold increase in syngas formation rates compared to its film counterpart. These enhancements are attributed to the unique structure of the nanopatterns, which feature smaller grain sizes, higher surface area, and improved light transmittance. This versatile nanopatterning approach is not limited to Au but can be extended to other catalytic materials, including metals, metal oxides, and transition metal dichalcogenides. The design offers a scalable solution to improve PEC performance for a wide range of applications, from CO2 reduction to other catalytic processes. By overcoming the trade-offs associated with traditional catalysts, this study provides a pathway toward more efficient and sustainable PEC systems.
Effects of temperature and coating speed on the morphology of solution-sheared halide perovskite thin-films
No full textIn this work, we have conducted a systematic study of the crystallization of perovskite thin-films during solution-shearing to elucidate how parameters such as substrate temperature and coating speed influence the morphology of the thin-film. Four distinct phases are identified and a morphological phase map is constructed. The formation of these phases is attributed to a delicate balance between the degree and rate of supersaturation and the flux of solution supply to the meniscus line, which dictates the nucleation and the crystal growth process. An optimal phase window is identified and the photovoltaic device under the chosen conditions exhibits a power conversion efficiency over 15%, which is comparable to that of a reference device prepared by the conventional spin-coating process. Furthermore, a large area perovskite film of 57 cm2 is prepared. Small-area devices from different locations within the large-area film show uniform efficiencies with a deviation coefficient of 4.2%, demonstrating the high uniformity of the thin-film.
Cu/Cu2O Interconnected Porous Aerogel Catalyst for Highly Productive Electrosynthesis of Ethanol from CO2
No full textUse of Cu and Cu+ is one of the most promising approaches for the production of C-2 products by the electrocatalytic CO2 reduction reaction (CO2RR) because it can facilitate CO2 activation and C-C dimerization. However, the selective electrosynthesis of C2+ products on Cu-0-Cu+ interfaces is critically limited due to the low electrocatalytic production of ethanol relative to ethylene. In this study, a novel porous Cu/Cu2O aerogel network is introduced to afford high ethanol productivity by the electrocatalytic CO2RR. The aerogel is synthesized by a simple chemical redox reaction of a precursor and a reducing agent. CO2RR results reveal that the Cu/Cu2O aerogel produces ethanol as the major product, exhibiting a Faradaic efficiency (FEEtOH) of 41.2% and a partial current density (J(EtOH)) of 32.55 mA cm(-2) in an H-cell reactor. This is the best electrosynthesis performance for ethanol production reported thus far. Electron microscopy and electrochemical analysis results reveal that this dramatic increase in the electrosynthesis performance for ethanol can be attributed to a large number of Cu-0-Cu+ interfaces and an increase of the local pH in the confined porous aerogel network structure with a high-surface-area.
Contribution des réseaux de neurones dans le domaine de l'ellipsométrie: Application à la scatterométrie
No full textNowadays, miniaturization is the most explored research topic in various domains of science and technology. Manufacturing processes, such as lithography, have been prodigiously developed during these last years and allow high scale integration of devices. This technological progress has created systematically the need of reliable, efficient and if possible low cost characterization techniques. The aim of this PhD is to study and implement an original mathematical tool, namely neural networks, within the framework of optical and dimensional metrology achieved by ellipsometry. In the first part of this work, we have shown that the neural network can be effectively employed for the determination of the optical and geometrical properties (refractive index and thickness) of thin films. For instance, neural classification has been proposed in order to estimate the thickness range of films without any prior information about the structure. This technique can be coupled with any other optimization algorithm requiring a prior knowledge about the solution. In the second part, we have clearly shown the contribution of the neural network in scatterometry for the characterization of diffraction gratings with different geometrical profiles. The neural method can also be employed to determine the grating pitch when it is required. Neural classification has been applied for structural identification of the geometrical model, giving thus a direct application in lithography for automatic detection of the residual layer undesirable for the etching step.La miniaturisation est actuellement la voie de recherche la plus explorée dans divers domaines de la science et de la technologie. Les processus de fabrication comme la lithographie se sont prodigieusement développés au cours de ces dernières années et permettent ainsi une réduction importante de la taille des composants. Ce progrès technologique a créé systématiquement le besoin de techniques de caractérisation fiables, efficaces et si possible à moindre coût. L'objet de cette thèse porte sur l'étude d'un outil mathématique original, à savoir les réseaux de neurones, dans le cadre de la métrologie optique et dimensionnelle achevée par voie ellipsométrique. Dans le premier volet de ce travail nous avons montré que le réseau de neurones peut être efficacement employé pour la détermination des propriétés optiques et géométriques (indice de réfraction et épaisseur) des couches minces. A titre illustratif, la classification neuronale a été proposée pour estimer la gamme d'épaisseur des couches sans aucune information a priori sur la structure. Cette technique peut être couplée avec n'importe quel autre algorithme d'optimisation nécessitant une connaissance préalable de la solution. Le second volet montre clairement l'apport des réseaux de neurones dans le domaine de la scatterométrie pour la caractérisation des réseaux de diffraction possédant différents profils géométriques. La méthode neuronale peut également être employée pour la détermination de la période du réseau lorsque cela est nécessaire. La classification neuronale a ensuite été appliquée pour l'identification structurale du modèle géométrique, donnant ainsi une application directe en lithographie pour la détection automatique d'une couche résiduelle nuisible à l'étape de gravure
Contribution des réseaux de neurones dans le domaine de l'ellipsométrie: Application à la scatterométrie
No full textNowadays, miniaturization is the most explored research topic in various domains of science and technology. Manufacturing processes, such as lithography, have been prodigiously developed during these last years and allow high scale integration of devices. This technological progress has created systematically the need of reliable, efficient and if possible low cost characterization techniques. The aim of this PhD is to study and implement an original mathematical tool, namely neural networks, within the framework of optical and dimensional metrology achieved by ellipsometry. In the first part of this work, we have shown that the neural network can be effectively employed for the determination of the optical and geometrical properties (refractive index and thickness) of thin films. For instance, neural classification has been proposed in order to estimate the thickness range of films without any prior information about the structure. This technique can be coupled with any other optimization algorithm requiring a prior knowledge about the solution. In the second part, we have clearly shown the contribution of the neural network in scatterometry for the characterization of diffraction gratings with different geometrical profiles. The neural method can also be employed to determine the grating pitch when it is required. Neural classification has been applied for structural identification of the geometrical model, giving thus a direct application in lithography for automatic detection of the residual layer undesirable for the etching step.La miniaturisation est actuellement la voie de recherche la plus explorée dans divers domaines de la science et de la technologie. Les processus de fabrication comme la lithographie se sont prodigieusement développés au cours de ces dernières années et permettent ainsi une réduction importante de la taille des composants. Ce progrès technologique a créé systématiquement le besoin de techniques de caractérisation fiables, efficaces et si possible à moindre coût. L'objet de cette thèse porte sur l'étude d'un outil mathématique original, à savoir les réseaux de neurones, dans le cadre de la métrologie optique et dimensionnelle achevée par voie ellipsométrique. Dans le premier volet de ce travail nous avons montré que le réseau de neurones peut être efficacement employé pour la détermination des propriétés optiques et géométriques (indice de réfraction et épaisseur) des couches minces. A titre illustratif, la classification neuronale a été proposée pour estimer la gamme d'épaisseur des couches sans aucune information a priori sur la structure. Cette technique peut être couplée avec n'importe quel autre algorithme d'optimisation nécessitant une connaissance préalable de la solution. Le second volet montre clairement l'apport des réseaux de neurones dans le domaine de la scatterométrie pour la caractérisation des réseaux de diffraction possédant différents profils géométriques. La méthode neuronale peut également être employée pour la détermination de la période du réseau lorsque cela est nécessaire. La classification neuronale a ensuite été appliquée pour l'identification structurale du modèle géométrique, donnant ainsi une application directe en lithographie pour la détection automatique d'une couche résiduelle nuisible à l'étape de gravure
Application of neural classification in ellipsometry for robust thin films characterization
No full textInternational audienc
- …
