Hierarchical Multiscale Hyperporous Block Copolymer Membranes via Tunable Dual-Phase Separation

The rational design and realization of revolutionary porous structures have been long-standing challenges in membrane science. We demonstrate a new class of amphiphilic polystyrene-block-poly(4-vinylpyridine) block copolymer (BCP)-based porous membranes featuring hierarchical multiscale hyperporous structures. The introduction of surface energy-modifying agents and the control of major phase separation parameters (such as nonsolvent polarity and solvent drying time) enable tunable dual-phase separation of BCPs, eventually leading to macro/nanoscale porous structures and chemical functionalities far beyond those accessible with conventional approaches. Application of this BCP membrane to a lithium-ion battery separator affords exceptional improvement in electrochemical performance. The dual-phase separation-driven macro/nanopore construction strategy, owing to its simplicity and tunability, is expected to be readily applicable to a rich variety of membrane fields including molecular separation, water purification, and energy-related devices.clos

Well-organized raspberry-like Ag@Cu bimetal nanoparticles for highly reliable and reproducible surface-enhanced Raman scattering

Surface-enhanced Raman scattering (SERS) is ideally suited for probing and mapping surface species and incipient phases on fuel cell electrodes because of its high sensitivity and surface-selectivity, potentially offering insights into the mechanisms of chemical and energy transformation processes. In particular, bimetal nanostructures of coinage metals (Au, Ag, and Cu) have attracted much attention as SERS-active agents due to their distinctive electromagnetic field enhancements originated from surface plasmon resonance. Here we report excellent SERS-active, raspberry-like nanostructures composed of a silver (Ag) nanoparticle core decorated with smaller copper (Cu) nanoparticles, which displayed enhanced and broadened UV-Vis absorption spectra. These unique Ag@Cu raspberry nanostructures enable us to use blue, green, and red light as the excitation laser source for surface-enhanced Raman spectroscopy (SERS) with a large enhancement factor (EF). A highly reliable SERS effect was demonstrated using Rhodamine 6G (R6G) molecules and a thin film of gadolinium doped ceria.close3

High-temperature surface enhanced Raman spectroscopy for in situ study of solid oxide fuel cell materials

In situ probing of surface species and incipient phases is vital to unraveling the mechanisms of chemical and energy transformation processes. Here we report Ag nanoparticles coated with a thin-film SiO2 shell that demonstrate excellent thermal robustness and chemical stability for surface enhanced Raman spectroscopy (SERS) study of solid oxide fuel cell materials under in situ conditions (at ???400 ??C).close3

Block-Copolymer-Based Nanostructured Materials for Energy Conversion and Storage Applications

Department of Energy Engineering (Battery Science and Technology)clos

Electrochemical Sensors for Antibiotic Susceptibility Testing: Strategies and Applications

Increasing awareness of the impacts of infectious diseases has driven the development of advanced techniques for detecting pathogens in clinical and environmental settings. However, this process is hindered by the complexity and variability inherent in antibiotic-resistant species. A great deal of effort has been put into the development of antibiotic-resistance/susceptibility testing (AST) sensors and systems to administer proper drugs for patient-tailored therapy. Electrochemical sensors have garnered increasing attention due to their powerful potential to allow rapid, sensitive, and real-time monitoring, alongside the low-cost production, feasibility of minimization, and easy integration with other techniques. This review focuses on the recent advances in electrochemical sensing strategies that have been used to determine the level of antibiotic resistance/susceptibility of pathogenic bacteria. The recent examples of the current electrochemical AST sensors discussed here are classified into four categories according to what is detected and quantitated: the presence of antibiotic-resistant genes, changes in impedance caused by cell lysis, current response caused by changes in cellular membrane properties, and changes in the redox state of redox molecules. It also discusses potential strategies for the development of electrochemical AST sensors, with the goal of broadening their practical applications across various scientific and technological fields

Modeling forecast errors for microgrid operation using Gaussian process regression

Abstract Microgrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators. The inherent uncertainty tied to renewable power generation, typified by photovoltaic and wind turbine systems, necessitates counterbalancing mechanisms. These mechanisms encompass Energy storage systems or conventional thermal fossil-fuel generators imbued with heightened flexibility. Addressing the uncertainty stemming from renewable generators mandates a cost-effective assessment and operational strategy for said compensatory devices. To this end, myriad uncertainty factors warrant scrutiny, conceivably concretized into a unified probability distribution function (PDF) that takes into account their temporal inter-dependencies. Diverse uncertainty factors, characterized by varying marginal distributions and scales, can be assimilated into a multivariate probability distribution through a conversion to normal distributions via rank correlation. However, with the escalation in the number of uncertainty factors embraced within a microgrid context, the endeavour becomes notably intricate when aiming to define conditional probability distributions originating from joint PDFs. This paper presents a method proposing the modelling of net-load forecast error distribution, considering the interplay among uncertainty factors. The approach introduces a data-driven Gaussian process regression technique for training and validating conditional PDFs among these uncertainty factors. Notably, this approach facilitates the transformation of said factors into normal distributions while preserving their inherent marginal characteristics. The resultant conditional density function, as per the proposed methodology, exhibits enhanced suitability for estimating net-load error distribution. Consequently, the conditional density function stemming from this proposed approach demonstrates superior aptitude in approximating the distribution of net load error

Large-Scale Synthesis of Interconnected Si/SiOx Nanowire Anodes for Rechargeable Lithium-Ion Batteries

Down to the wire: Three-dimensional interconnected Si-based nanowires are produced through the combination of thermal decomposition of SiO and a metal-catalyzed nanowire growth process. This low-cost and scalable approach provides a promising candidate for high-capacity anodes in lithium-ion batteries.close2