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Piezoelectric DC Generator Through Sequential In-Phase Polarization Variation
Full text linkEnergy harvesting has drawn growing interest as a reliable power source for IoT applications, with piezoelectric materials notable for their high sensitivity and straightforward integration. Their robust mechanical-electrical coupling also makes them ideal for harnessing environmental vibrations or mechanical motions. Still, standard piezoelectric harvesters inherently produce alternating current (AC), necessitating complex rectification steps and leading to substantial energy loss. This work introduces a direct current (DC) harvesting method that employs a novel in-phase polarization strategy, enabling a stable, continuous DC output. This approach surpasses prior attempts that offered only low or pulsed DC signals, achieving an open-circuit voltage of 33.44 V and a short-circuit current of 3.72 mA with a size of 7.5 cm2. A prototype generator demonstrated a maximum power output of 29.73 mW. Moreover, this design is both miniaturizable and scalable, broadening its potential deployment across diverse sectors. Its practical value was exemplified by directly powering CO2 electrolysis, where it achieveds a Faradaic efficiency of 86.22%, underscoring the method\u27s ability to circumvent AC-based inefficiencies and pave the way for more effective, sustainable energy solutions
Energy Service Security in the Context of the Rural and Urban Divide
No full textThe concept of energy security has evolved in response to global crises such as the 1970s oil crisis, the COVID-19 pandemic, and the Russia-Ukraine war, which have highlighted vulnerabilities in fossil fuel supply chains and spurred policy and academic responses. Traditional frameworks of energy security focus on the reliable, affordable, and sustainable supply of energy but often overlook the enduse services that energy enables. This chapter argues that energy security should be reconceptualized as energy services security (ESS), emphasizing the activities energy facilitates rather than the energy sources themselves. ESS varies significantly across geopolitical, urban, and rural contexts, with disparities exacerbated by income levels, infrastructure, and policy frameworks. Using India and the USA as case studies, this research explores the rural-urban divide in ESS within lower-middle-income (LMIC) and high-income (HIC) countries. It highlights how factors such as supply diversity, electrification policies, and demographic shifts shape ESS differently in these contexts. The chapter underscores the need to integrate energy justice into ESS frameworks to address inequities in energy access and ensure a just transition to renewable energy. By examining ESS through an intergenerational and justice-oriented lens, this study contributes to a more nuanced understanding of energy security in a decarbonizing global economy
Adaptive PI Control Using Recursive Least Squares for Centrifugal Pump Pipeline Systems
No full textPipeline transportation of petroleum products remains one of the safest and most efficient methods of bulk energy delivery, yet overpressure events continue to pose serious operational and regulatory challenges. Traditional fixed-gain PI controllers, commonly used with centrifugal pump drives, cannot adapt to varying product densities or transient disturbances such as valve closures that generate water hammer. This paper proposes a self-tuning adaptive controller based on Recursive Least Squares (RLS) parameter estimation to improve safety and efficiency in pipeline pump operations. A nonlinear simulation model of a centrifugal pump driven by an induction motor is developed, incorporating pipeline friction losses via the Darcy–Weisbach relation and pressure transients induced by rapid valve closures. The RLS algorithm continuously estimates effective loop dynamics, enabling online adjustment of proportional and integral gains under changing fluid and operating conditions. Simulation results demonstrate that the proposed RLS-based adaptive controller maintains discharge pressure within ±2% of the target setpoint under density variations from 710 to 900 kg/m3 and during severe transient events. Compared to a fixed-gain PI controller, the adaptive strategy reduced pressure overshoot by approximately 31.9% and settling time by 6%. Model validation using SCADA field data yielded an (Formula presented.) = 0.957, RMSE = 3.95 m3/h, and normalized NRMSE of 12.6% (by range), confirming strong agreement with measured system behavior. The findings indicate that RLS-based self-tuning provides a practical enhancement to existing pipeline control architectures, offering both improved robustness to abnormal transients and greater efficiency during steady-state operation. This work establishes a foundation for higher-level supervisory and game-theoretic coordination strategies to be explored in subsequent studies
Economic and environmental impact of recovering and upgrading lignin via the ALPHA process on an ethanol biorefinery
No full textLignin valorization is limited by both impurities and broad molecular weight (MW) distributions. The Aqueous Lignin Purification using Hot Agents (ALPHA) process exploits the novel liquid–liquid equilibrium that exists between lignin and hot, one-phase solutions of aqueous renewable solvents to simultaneously purify and fractionate raw bulk lignins. Our analysis considered the valorization of lignin recovered from two ethanol biorefinery waste streams, black liquor and lignin cake. By applying sequential ALPHA stages, distinct lignin fractions of controlled MW and purity were isolated suitable for producing carbon fibers, polyurethane foams (PUF), and activated carbons (ACs). Surprisingly, only a single extraction stage was required to split lignin cake into two useful fractions: a purified, low MW lignin for PUFs and a ‘dirty’, cellulose-contaminated lignin that proved to be effective for AC production. Using inputs generated in lab, an ASPEN Plus simulation was developed to model a 130 000 metric tons lignin (dry)/yr plant for valorizing the lignin by-product from a lignocellulosic ethanol biorefinery. Economic results generated using a 30 yr discounted cash flow table show an annual profit of 238 million for the best-performing scenario. Accounting for net unfavorable uncertainties through a Monte Carlo simulation, the ALPHA process has a 92% chance of being profitable over the 30 yr lifespan. Environmental impacts were calculated using a life cycle assessment methodology and the software SimaPro. The fractionated lignin products have GHG emissions and cumulative energy demand equivalent to or lower than their fossil precursor equivalents. Considering the sequestration of carbon by the final products lowered the GHG emissions for all scenarios by 40%–50%. Coupling the economic and environmental results indicate that the ALPHA process is a promising emerging technology for valorizing lignin, aiding in fulfilling the UN Sustainable development goal of using sustainable raw materials
Investigation of Insertion Loss in Inkjet-Printed Coplanar Waveguide Based on Drying Temperature
No full textIn this study, we propose an optimized inkjet printing process to improve the insertion loss of inkjet-printed coplanar waveguide (CPW) transmission lines. The process involves varying the drying temperature and adjusting the number of printing steps to investigate their effects on the electrical characteristics of the printed CPW. The relationships between surface roughness, surface cavities, morphological changes, and insertion loss are studied by conducting atomic force microscopy analysis and by examining the insertion loss up to 3 GHz. The printed CPW that underwent low-temperature drying after the first printing and high-temperature drying after the second printing before sintering showed improved results in terms of insertion loss, surface microcavities, and surface morphology compared to the CPWs prepared under different drying conditions. In particular, the proposed process led to an improvement in the insertion loss from 0.167 dB to 0.082 dB at 3 GHz. These findings provide valuable insights for improving the reliability and efficiency of printed electronics used in high-frequency applications
Torsion-induced π-conjugation Offers an Approach to Small Coumarin-based Color-changing Fluorescent Sensors of Esterase Activity
No full textFluorescent sensors for esterase activity include a diverse array of compounds that utilize intramolecular charge transfer induced by the unmasking of the electron-donating hydroxyl group through esterase action. These sensors exhibit different outcomes, ranging from turn-on fluorescence to fluorescence color changes. In this work, we demonstrate the application of torsion-induced fluorescence changes in designing esterase-dependent chemosensors. We present a method for detecting esterase activity based on the torsion-induced geometrical changes between the ester and carboxylate forms of a fluorophore. Our approach shows that aligning electronic interactions between the C7 heteroatom and the C4 ester substituents within a small coumarin core stabilizes the planar geometry of the coumarin ester, leading to fluorescence emission above 570 nm. Upon esterase-mediated hydrolysis, the coumarin exhibits a blue shift in emission to 460 nm, corresponding to the carboxylate form. This shift occurs due to the nonplanar orientation of the carbonyl relative to the fluorophore. As a result, C4-coumarin ester emissions can be observed in the red fluorescence channel, while carboxylate emissions are detected in the blue/green fluorescence channels. Consequently, we introduce small, highly permeable aryl and acetoxymethyl coumarin esters as indicators of esterase activity and as tools to differentiate between live and dead cells
Constructing genotype and phenotype network helps reveal disease heritability and phenome-wide association studies
No full textBayesian Alloy Design with Additive Synthesis
Full text linkTo reduce carbon emissions and increase power out, steam powerplants need to increase the operating temperatures and pressures of steam turbines to improve efficiency. This necessitates the development of high temperature alloys with superior strength and stability. This research aims to design a class of solid solution High Entropy Alloys (HEAs) to exceed the high temperature performance of commercial alloys like Haynes 230 while maintaining comparable costs for Advanced Ultra Supercritical (A-USC) steam cycles. This project integrates Bayesian optimization and Calculation of Phase Diagrams (CALPHAD) within an Integrated Computational Materials Engineering (ICME) framework to predict and optimize key material properties: high temperature solid solution strengthening, diffusion coefficients, freezing range, solidification cracking resistance, and pricing. A novel aspect of this work is the use of multi-Wire Arc Additive Manufacturing (mWAAM), enabling high-throughput fabrication of small test coupons with tailored compositions from the same wire stock. Alloys were screened for performance metrics using hot hardness, nanoindentation, metallography with SEM and EDS ensuring a single-phase microstructure. Promising candidates were printed into larger coupons for mechanical testing, including hot tensile tests between 538°C and 871°C and time to 1% creep strain tests at 760°C under various stresses. Cracking during fabrication was identified in optimized alloys containing vanadium, and the nature of this cracking was investigated via EBSD and EDS. Results indicated that unexpected forms of cracking related to grain misorientation, stress concentration at the grain boundaries, and thermal cycling became prominent. This highlights the need to balance performance and printability by optimizing composition and cooling rates to mitigate cracking and ensure printability
Understanding and Modeling Drivers\u27 Diversion Behavior during Congestion
No full textTraffic congestion causes significant economic losses due to delays and excessive fuel consumption. Understanding drivers\u27 behaviors, particularly in terms of diversionary routing to avoid congestion, is crucial for addressing this issue. This study investigates driver behavior during congestion and develops a predictive model for route diversion decisions using about 20 million anonymized trips from global positioning system (GPS) data in Sydney, Australia. Among the most unique and significant contributions of this work was the development of a methodology to identify and graphically depict commute trips and assess the impact of prevailing traffic conditions and related driving factors on diversion likelihood without relying on access to supplemental data. Examples of driving factors considered include distances and times from origins and destinations, durations of delays caused by congestion, length of congested areas, and roadway classification. Findings indicate that experienced delay per congestion distance and remaining distance to the destination positively influence diversion, while expected increases in travel time on alternative routes discourage it. These results can enhance traffic simulation models and improve traffic management strategies. Overall, the contributions of this paper have both practical importance and theoretical applicability and provide a long-absent step toward understanding how individual drivers respond to congestion and make subsequent routing choices
A solvent-targeted recovery and precipitation scheme for the recycling of up to ten polymers from post-industrial mixed plastic waste
No full textSolvent-Targeted Recovery and Precipitation (STRAPTM) separates polymers within a plastic waste stream by selective dissolution. In this work, the STRAP framework, which combines computational modeling and experiments, was applied to develop a series of steps to separate up to 10 polymers from post-industrial mixed plastic waste (MPW) and the main components recovered were LDPE, HDPE, and PET. The STRAP steps were initially demonstrated with a physical polymer mixture containing LDPE, HDPE, PS, PVC, EVOH, PET, PP, PA6, PA66, and PA66/6, in which recoveries of 89% or higher were achieved for each polymer. This paper demonstrates a solvent selection approach that can be applied to separate unknown plastics materials into purer components