An Analytical 1-D Model for Ion Implantation of Any Species into Single-Crystal Silicon Based on Legendre Polynomials

A computationally-efficient 1-D analytical model for ion implantation of any species into single crystal silicon is presented. By interpolating between a few species, the model can predict as-implanted profiles for all the other species. The model uses Legendre polynomials as basis functions. The results of the model are in good agreement with UT-MARLOWE, which is a physically-based and experimentally verified Monte Carlo simulator.     

Dynamic optimization of a dividing-wall column using model predictive control

Dividing-wall column (DWC) is one of the best examples of process intensification, as it can bring significant reduction in the capital invested as well as savings in the operating costs. Conventional ternary separations progressed from the (in-)direct sequences to thermally coupled columns such as Petlyuk configuration, and later to the DWC compact design that integrates the two distillation columns into a single shell. Nevertheless, this integration leads also to changes in the control and operating mode due to the higher number of degrees of freedom.In this work we explore the dynamic optimization and advanced control strategies based on model predictive control (MPC), coupled or not with PID. These structures were enhanced by adding an extra loop controlling the heavy component in the top of the feed side of the column, using the liquid split as manipulated variable, thus implicitly achieving energy minimization. To allow a fair comparison with previously published references, this work considers as a case-study the industrially relevant separation of the mixture benzene–toluene–xylene (BTX) in a DWC.The results show that MPC leads to a significant increase in performance, as compared to previously reported conventional PID controllers within a multi-loop framework. Moreover, the optimization employed by the MPC efficiently accommodates the goal of minimum energy requirements – possible due to the addition of an extra loop – in a transient state. The practical benefits of coupling MPC with PID controllers are also clearly demonstrated.     

Metal oxide nanoparticles for advanced energy applications

Hot-wire chemical vapor deposition (HWCVD) has been employed as an economically scalable method for the deposition of crystalline molybdenum oxide nanoparticles at high density. Under optimal synthesis conditions, only crystalline nanostructures with a smallest dimension of ~ 3-50 nm are observed with extensive transmission electron microscopy analyses. The incorporation of crystalline molybdenum oxide nanoparticles into battery electrodes has led to profound advancements in state-of-the-art negative electrodes (anodes) in lithium-ion batteries. The nanoparticle materials exhibit a high rate capability as anticipated for the reduced solid-state Li-ion diffusion length.     

A Light Harvesting,Self‐Powered Monolith Tactile Sensor Based on Electric Field Induced Effects in MAPbI3 Perovskite

Organolead trihalide perovskite MAPbI₃ shows a distinctive combination of properties such as being ferroelectric and semiconducting, with ion migration effects under poling by electric fields. The combination of its ferroelectric and semiconducting nature is used to make a light harvesting, self‐powered tactile sensor. This sensor interfaces ZnO nanosheets as a pressure‐sensitive drain on the MAPbI₃ film and once poled is operational for at least 72 h with just light illumination. The sensor is monolithic in structure, has linear response till 76 kPa, and is able to operate continuously as the energy harvesting mechanism is decoupled from its pressure sensing mechanism. It has a sensitivity of 0.57 kPa^?1, which can be modulated by the strength of the poling field. The understanding of these effects in perovskite materials and their application in power source free devices are of significance to a wide array of fields where these materials are being researched and applied.     

Unleashing the Potential of Sodium-Ion Batteries: Current State and Future Directions for Sustainable Energy Storage

Rechargeable sodium-ion batteries (SIBs) are emerging as a viable alternative to lithium-ion battery (LIB) technology, as their raw materials are economical, geographically abundant (unlike lithium), and less toxic. The matured LIB technology contributes significantly to digital civilization, from mobile electronic devices to zero electric-vehicle emissions. However, with the increasing reliance on renewable energy sources and the anticipated integration of high-energy-density batteries into the grid, concerns have arisen regarding the sustainability of lithium due to its limited availability and consequent price escalations. In this context, SIBs have gained attention as a potential energy storage alternative, benefiting from the abundance of sodium and sharing electrochemical characteristics similar to LIBs. Furthermore, high-entropy chemistry has emerged as a new paradigm, promising to enhance energy density and accelerate advancements in battery technology to meet the growing energy demands. This review uncovers the fundamentals, current progress, and the views on the future of SIB technologies, with a discussion focused on the design of novel materials. The crucial factors, such as morphology, crystal defects, and doping, that can tune electrochemistry, which should inspire young researchers in battery technology to identify and work on challenging research problems, are also reviewed.     

A robust face hallucination technique based on adaptive learning method

Position-patch based approaches have been proposed for single-image face hallucination. This paper models the face hallucination problem as a coefficient recovery problem with respect to an adaptive training set for improved noise robustness. The image-adaptive training set is constructed by corrupting a local training set of position-patches by adding specific amounts of noise depending on the input image noise level. In this proposed method, image denoising and super-resolution are simultaneously carried out to obtain superior results. Though the principle is general and can be extended to most super-resolution algorithms, we discuss this in context of existing locality-constrained representation (LcR) approach in order to compare their performances. It can be demonstrated that the proposed approach can quantitatively and qualitatively yield better results in high noisy environments.

     

Doping and Switchable Photovoltaic Effect in Lead‐Free Perovskites Enabled by Metal Cation Transmutation

Creating defect tolerant lead‐free halide perovskites is the major challenge for development of high‐performance photovoltaics with nontoxic absorbers. Few compounds of Sn, Sb, or Bi possess ns² electronic configuration similar to lead, but their poor photovoltaic performances inspire us to evaluate other factors influencing defect tolerance properties. The effect of heavy metal cation (Bi) transmutation and ionic migration on the defects and carrier properties in a 2D layered perovskite (NH₄)₃(Sb_(1?x)Bi_x)₂I₉ system is investigated. It is shown, for the first time, the possibility of engineering the carriers in halide perovskites via metal cation transmutation to successfully form intrinsic p‐ and n‐type materials. It is also shown that this material possesses a direct–indirect bandgap enabling high absorption coefficient, extended carrier lifetimes >100 ns, and low trap densities similar to lead halide perovskites. This study also demonstrates the possibility of electrical poling to induce switchable photovoltaic effect without additional electron and hole transport layers.