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The rapid growth of wearable technology demands sustainable, flexible, and lightweight energy sources for various applications ranging from health monitoring to electronic textiles. Although wearable devices based on the piezoelectric effect are widespread, achieving simultaneous breathability, waterproof, and enhanced piezoelectric performance remains challenging. Herein, this study aims to develop a piezoelectric nanogenerator (PENG) using ZnO nanofillers in two morphologies (nanoparticles and nanorods), with a carbon coating (ZnO@C) core-cell structure to enhance piezoelectric performance. Electrospinning technique was employed to fabricate a lightweight, breathable, and water-resistant ZnO@C/PVDF membrane, enabling in situ electrical poling and mechanical stretching to enhance electroactive \b{eta}-phase formation and thus improve piezoelectric performance. A maximum power density of 384.83 {\mu}W/cm3 was obtained at RL = 104 k{\Omega}, with a maximum Vout = 19.9 V for ZnO@C nanorod-incorporated PVDF samples. The results demonstrate that ZnO@C nanorods exhibit superior voltage output due to their larger surface-to-volume ratio, leading to enhanced interaction with PVDF chains compared to nanoparticles. The fabricated membrane showed promising results with a water vapor transmission rate (WVTR) of ~0.5 kg/m2/day, indicating excellent breathability, and a water contact angle of ~116{\deg}, demonstrating significant waterproofness. These findings highlight the potential of the ZnO@C/PVDF electrospun membrane as an effective piezoelectric nanogenerator and energy harvester for wearable applications., Comment: 27 pages, 10 Figures

The increasing integration of renewable energy sources results in fluctuations in carbon intensity throughout the day. To mitigate their carbon footprint, datacenters can implement demand response (DR) by adjusting their load based on grid signals. However, this presents challenges for private datacenters with diverse workloads and services. One of the key challenges is efficiently and fairly allocating power curtailment across different workloads. In response to these challenges, we propose the Carbon Responder framework. The Carbon Responder framework aims to reduce the carbon footprint of heterogeneous workloads in datacenters by modulating their power usage. Unlike previous studies, Carbon Responder considers both online and batch workloads with different service level objectives and develops accurate performance models to achieve performance-aware power allocation. The framework supports three alternative policies: Efficient DR, Fair and Centralized DR, and Fair and Decentralized DR. We evaluate Carbon Responder polices using production workload traces from a private hyperscale datacenter. Our experimental results demonstrate that the efficient Carbon Responder policy reduces the carbon footprint by around 2x as much compared to baseline approaches adapted from existing methods. The fair Carbon Responder policies distribute the performance penalties and carbon reduction responsibility fairly among workloads.

This paper discusses a framework to design elements of the plant and control systems for floating offshore wind turbines in an integrated manner using linear parameter-varying models. Multiple linearized models derived from aeroelastic simulation software in different operating regions characterized by the incoming wind speed are combined to construct an approximate low-fidelity model of the system. The combined model is then used to generate open-loop, optimal control trajectories as part of a nested control co-design strategy that explores the system's power production and stability using the platform pitch tilt as a proxy in the context of crucial plant and control design decisions. The radial distance between the central and outer columns and the diameter of the outer columns of the semisubmersible platform are the plant design variables. The platform stability and power production are studied for different plant design decisions. The effect of plant decisions on subsequent power production and stability response of the floating wind turbine is quantified in terms of the levelized cost of energy. The results show that the inner-loop constraints and the plant design decisions affect the turbine's power and, subsequently, the cost of the system., Comment: 16 pages 47 figures

Models that balance accuracy against computational costs are advantageous when designing dynamic systems with optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. The efficacy and use of derivative function surrogate models (DFSMs), or approximate models of the state derivative function, have been well-established in the literature. However, previous studies have assumed an a priori state dynamic model is available that can be directly evaluated to construct the DFSM. In this article, we propose an approach to extract the state derivative information from system simulations using piecewise polynomial approximations. Once the required information is available, we propose a multi-fidelity DFSM approach as a predictive model for the system's dynamic response. This multi-fidelity model consists of summation between a linear-fit lower-fidelity model and an additional nonlinear error corrective function that compensates for the error between the high-fidelity simulations and low-fidelity models. We validate the model by comparing the simulation results from the DFSM to the high-fidelity tools. The DFSM model is, on average, five times faster than the high-fidelity tools while capturing the key time domain and power spectral density~(PSD) trends. Then, an optimal control study using the DFSM is conducted with outcomes showing that the DFSM approach can be used for complex systems like floating offshore wind turbines~(FOWTs) and help identify control trends and trade-offs., Comment: 14 pages,45 figures

Metallic contaminants in complex heterostructures are important topics due to their significant roles in determining physical properties as well as device performance. Heterostructures of polyimide via on Al pad and Cu redistribution layer (RDL) on polyimide have shown exotic properties and are important for advanced semiconductor packaging systems. One main problem is significant leakage current variations, which affect the performance of the devices, yet the origin is far from understood. Furthermore, metal contaminations would occur at the buried interfaces and it is particularly challenging to probe them. Until now, the electronic and optical properties of complex polyimide heterostructures and the roles of metallic contaminants, especially in the deep ultraviolet (DUV) have not been studied extensively. Herewith, using spectroscopic ellipsometry (SE) in a broad DUV range supported with finite-difference time-domain (FDTD) calculations, we determine optical properties of contaminants with various concentrations and reveal their influence on device performance of under-bump vias and redistribution layer (RDL) architectures. The complex dielectric function shows varying contamination levels and different metals responsible for chip performance. Metallic contaminants are found embedded within 50 nm in the polyimide and different metals are distinguishable with varying concentrations, in agreement with contact measurements in highly complex structures. Our result shows the potency of spectroscopic ellipsometry in the DUV and paves the way for non-destructive, advanced quality control and metrology applications in integrated advanced electronics packaging systems., Comment: 20 pages, 5 figures, accepted in Applied Physics Letters