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The fourth industrial revolution has necessitated the integration of sustainability and customer centricity into modern manufacturing systems, thereby emphasising their significance. However, the production process itself is characterised by inherent uncertainties. Hence, this paper primarily focuses on the multi-objective flexible job shop rescheduling problem with uncertain processing time and new job insertions. Two fuzzy constraints are incorporated: fuzzy processing time and fuzzy due date, which aim to model the uncertainty of processing time and ensure flexibility in meeting deadlines. The objective function aims to optimise both customer satisfaction and energy consumption. To address two types of rescheduling problems: time delay caused by fuzzy features and new job insertions, this paper is dedicated to providing a hybrid adaptative rescheduling strategy that combines a satisfaction-driven policy with two specific rescheduling methods. Moreover, an enhanced version of the non-dominated sorting genetic algorithm II (NSGA-II) is developed, incorporating population initialisation rules and multiple genetic operators to obtain solutions of superior quality. Experimental studies have demonstrated the impact of time delay caused by fuzzy features on customer satisfaction. The proposed hybrid adaptative rescheduling strategy and methods exhibit significant improvements in rescheduling plan performance under uncertain environments. [ABSTRACT FROM AUTHOR]

The dramatic increase in Chinese residents' income has driven a sharp increase in the purchase of household durable metal products (HDMPs), which are classified into automobiles and other durable products, mainly appliances and electronics. To explore the energy efficiency potential of the closed-loop supply chain (CLSC) for the HDMP industry in China, we develop a comprehensive evaluation framework based on dynamic material flow analysis and life cycle assessment. The results show that the demand for appliances and electronics and for electric cars will reach peaks in 2028 and 2035, respectively, and annual scrap for appliances and electronics and for electric cars will reach peaks in approximately 2036 and 2040, respectively. The recycling of scrap iron, aluminium, and copper can achieve energy conservation of approximately 72%, 94%, and 82%, respectively, compared with metal primary production, while remanufacturing processes can further reduce energy consumption by approximately 68%, 57%, and 72%, respectively. If the energy consumption of metal production, recycling rate, and remanufacturing rate can reach internationally advanced levels by 2035, approximately 50.5 Mtce (million-ton coal equivalent) will be saved compared to low-speed development by 2050; among these factors, the regeneration of waste metals can achieve the most significant energy efficiency effect. [ABSTRACT FROM AUTHOR]

At present, the concept of green and sustainable development has been recognised by everyone. For industrial applications, the concept is both a matter of energy consumption and a matter of production methods. Based on the hybrid flow shop problem, a multi-energy supply system is constructed, and external renewable energy microgrid is introduced to further improve the low-carbon level. From the perspectives of energy supply and internal load, the output of wind power and photovoltaic, energy storage cost and energy consumption cost in the flow shop on a certain day are analysed under the demand–response electricity price. After considering the natural endowments and demand differences of different microgrids, construct a multi-energy complementarity model, determine the coordination transaction strategy among microgrids and obtain the solution of the subproblem and the equilibrium solution of the whole problem. Finally, an example is given to evaluate the effectiveness of the proposed strategy under time-of-use prices. The results show that the proposed strategy can effectively improve the utilisation rate of renewable energy in the flow shop while reducing the overall operating cost. [ABSTRACT FROM AUTHOR]

The calculation of the interfacial free energy between two thermodynamic phases is crucial across various fields, including materials science, chemistry, and condensed matter physics. In this study, we apply an existing thermodynamic approach, the Gibbs–Cahn integration method, to determine the interfacial free energy under different coexistence conditions, relying on data from a single-state calculation at specified pressure and temperature. This approach developed by Laird et al. [J. Chem. Phys. 131, 114110 (2009)] reduces the computational demand and enhances efficiency compared to methods that require separate measurements at each thermodynamic state. The integration scheme computes the excess interfacial free energy using unbiased constant volume, temperature, and number of particle simulations (NVT), where the two phases coexist, to provide input for the calculations. We apply this method to the Lennard-Jones and mW water models for liquid–solid interfaces, as well as the Lennard-Jones and TIP4P/2005 models for liquid–vapor interfaces. Our results demonstrate the accuracy and effectiveness of this integration route for estimating the interfacial free energy along a coexistence line. [ABSTRACT FROM AUTHOR]

Magnetic microcalorimeters (MMCs) have become a key technology for applications requiring outstanding energy resolution, fast signal rise time and excellent linearity. MMCs measure the temperature rise upon absorption of a single particle within a particle absorber by using a paramagnetic temperature sensor that is thermally coupled to the absorber. The design and fabrication of the particle absorber is key for excellent detector performance. Here, we present a microfabrication process for free-standing particle absorbers made of two stacked and independently electroplated high-purity Au layers. This enables, for example, the embedding of radioactive sources within the absorber for realizing a 4 π detection geometry in radionuclide metrology or preparing detector arrays with variable quantum efficiency and energy resolution as requested for future applications in high-energy physics. Due to careful optimization of photoresist processing and electroplating parameters, the Au films are of very high purity and very high residual resistivity ratio values above 40, allowing for fast internal absorber thermalization. [ABSTRACT FROM AUTHOR]

Fusion will be and needs to be achieved. But when? Its primary fuel, deuterium, exists in sufficient quantity to satisfy any conceivable energy demands for thousands of millions of years. The cost of obtaining it from water is less than one per cent the present cost of coal. Is fusion power a distant dream or a near-term possibility? The first applications could come in the 1980s. Richard F. Post, a physicist, is head of the magnetic mirror program in controlled thermonuclear research at the Lawrence Radiation Laboratory, Livermore, California. This report on progress in fusion research was adapted from his presentation on April 26, 1971 to the National Academy of Sciences Symposium on "Energy for the Future." [ABSTRACT FROM AUTHOR]

In response to the challenges posed by economic globalisation and increasing customer demands, enterprises are compelled to adapt and refine their production models and operational objectives, which motivates this paper to address the integrated production scheduling and vehicle routing problem in the distributed manufacturing environment (IPSVRP-DME). The objective is to simultaneously minimise both total energy consumption and total earliness/tardiness. Initially, a mixed integer programming model is proposed to address small-scale problems using Gurobi. Considering the NP-hardness of the problem, a novel Self-adapted Gaussian Mutation-based Arithmetic Optimiser Algorithm (SGMAOA) is developed to handle medium-scale and large-scale instances. To address the complexity of decision-making, an innovative two-level encoding method that encompasses four decision dimensions is introduced. Additionally, an incremental repair strategy is devised to rectify infeasible solutions caused by unreasonable delivery batching, while a time relaxation strategy is proposed to further enhance service levels without compromising energy consumption. Comparative experiments are conducted to demonstrate the effectiveness of SGMAOA, which is benchmarked against five prominent metaheuristics. In addition, a specific example is applied for the discussion of managerial applications and to illustrate the practicality of the proposed model and solution method. [ABSTRACT FROM AUTHOR]

Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs. [ABSTRACT FROM AUTHOR]

The robotic live-cube compact storage system can provide a higher storage capacity and more flexible throughput than other automated warehousing systems. The extant studies usually fix the input/output point at the bottom-left of such storage systems and have not investigated other configurations of the input/output point. In this study, we propose six policies for the configuration of the input/output point and evaluate their performance by using an analytical model and simulations. A sensitivity analysis is carried out by considering different shape-related factors, system heights, and energy consumption to explore the optimal configuration of the input/output point. For most input/output point policies, a shape factor (length divided by depth) of 0.5 results in good system performance in terms of retrieval time. When using optimal system dimension, the input/output point located in the upper-middle part along the length and depth performs best compared with other policies. Specifically, this configuration reduces the retrieval time by 40.23% and energy consumption by 42.31% compared with the bottom-left corner input/output point. We find that there exists a trade-off among the cost of setup, number of empty grids, and retrieval time when varying the number of input/output points. [ABSTRACT FROM AUTHOR]

Although K0.5Na0.5NbO3 (KNN) possesses large maximum polarization and relatively high breakdown strength, the large remnant polarization constrains their practical applications as energy storage materials. In this work, through multi-element doping, (K0.5−0.5xNa0.5−0.5xBix)(Nb1−xSn0.2xZn0.6xTa0.2x)O3 relaxor ferroelectrics were prepared. As the superparaelectric states (SPE) were adjusted to room temperature, orthorhombic, tetragonal, and cubic phases coexisted, accompanied by the highly dynamic polar nanoregions (PNRs). In particular, a high recoverable energy storage density of 4.5 J/cm3 and an energy storage efficiency of 83% were achieved for the x = 0.125 ceramic, with the variations less than 11% and 4%, respectively, in the wide temperature range of 20–180 °C. These results demonstrate that the multiphase PNRs in the SPE state is an effective strategy for improving the energy storage performance of KNN-based ceramics. [ABSTRACT FROM AUTHOR]

In this work, we study the energy flow through anharmonic coupling of vibrational modes after excitation of gas-phase fluorobenzene with a multi-THz pump. We show that to predict the efficiency of anharmonic energy transfer, simple models that only include the anharmonic coupling coefficients and motion of modes at their resonant frequency are not adequate. The full motion of each mode is needed, including the time while the mode is being driven by the pump pulse, because all the frequencies present in the multi-THz pump contribute to the excitation of the non-resonantly excited vibrational modes. Additionally, the model gives us the insight that modes with either A1 or B2 symmetry are more actively involved in anharmonic coupling because these modes have more symmetry-allowed energy transfer pathways. [ABSTRACT FROM AUTHOR]

Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

A fully cylindrical Hall thruster prototype was tested in the power range of 30–300 W with the objective of understanding the behavior of the discharge as a function of input parameters. Various operating conditions were compared, including two magnetic field configurations, a set of propellant mass flow rates, and a range of discharge voltages. Plasma properties were measured in the plume, with a Langmuir probe, a retarding potential analyzer, and a Faraday cup. The experimental results showed that the mass flow rate strongly affects the ionization and, consequently, other related properties such as the plasma density, currents, and propellant utilization. The discharge voltage also appeared to influence the ion energy and propellant utilization. The performance accessible from the measured magnitudes is assessed, resulting in a maximum thrust efficiency of about 18% at 0.35 mg s − 1 and 168 W. [ABSTRACT FROM AUTHOR]

This study investigates the influence of trimming layer thickness on the performance characteristics of thin-film bulk acoustic resonators (FBARs). By adjusting the thickness of the trimming layer, we aimed to optimize the resonant (f s) and antiresonant frequencies (f p) , which are critical for the filter's performance in blocking or passing specific frequency bands. Employing theoretical modeling and experimental validation, we explored how variations in the trimming layer thickness affect the electromechanical coupling coefficient (k t 2) and the overall energy efficiency of FBARs. The study also considered the impact of piezoelectric layer thickness on the effectiveness of these adjustments, revealing that thinner piezoelectric layers are more sensitive to changes in the trimming layer, enhancing the tuning capability for high-frequency applications. Our results confirm that reducing the trimming layer thickness increases both resonant and antiresonant frequencies, with a more pronounced effect on the latter, suggesting a significant phase correction effect from the trimming layer, resulting in the quality Q factor reductions. Our experimental results show that as the trimming layer thickness increases from 200 to 300 nm, the Q factor increases by 11%, while k t 2 decreases by 4%. This research provides critical insights into the design and optimization of FBARs, offering guidelines that can help advance the development of more efficient and effective RF components for future mobile communication technologies. [ABSTRACT FROM AUTHOR]

Fluctuations in energy gap and coupling constants between chromophores can play an important role in absorption and energy transfer across a collection of two-level systems. In photosynthesis, light-induced quantum coherence can affect the efficiency of energy transfer to the designated "trap" state. Theoretically, the interplay between fluctuations and coherence has been studied often, employing either a Markovian or a perturbative approximation. In this study, we depart from these approaches to incorporate memory effects by using Kubo's quantum stochastic Liouville equation. We introduce the effects of decay of the created excitation (to the ground state) on the desired propagation and trapping that provides a direction of flow of the excitation. In the presence of light-induced pumping, we establish a relation between the efficiency, the mean survival time, and the correlation decay time of the bath-induced fluctuations. A decrease in the steady-state coherence during the transition from the non-Markovian regime to the Markovian limit results in a decrease in efficiency. As in the well-known Haken–Strobl model, the ratio of the square of fluctuation strength to the rate plays a critical role in determining the mechanism of energy transfer and in shaping the characteristics of the efficiency profile. We recover a connection between the transfer flux and the imaginary part of coherences in both equilibrium and excited bath states, in both correlated and uncorrelated bath models. We uncover a non-monotonic dependence of efficiency on site energy heterogeneity for both correlated and uncorrelated bath models. [ABSTRACT FROM AUTHOR]

Aluminum powder particle size (D50) is quite important for how to improve the energy release level and formulation design of aluminized explosives. In this paper, four experimental samples of CL-20-based aluminized explosives with aluminum powder particle sizes including 2, 13, 24, and 43 μm were designed. The experiments were carried out through an underwater explosion tank and compared with explosives containing lithium fluoride of the same particle size. The results show that the burning of aluminum powder promoted the shock wave propagation and bubble expansion. Aluminized explosives have a lower and then higher decay rate than lithium fluoride-containing explosives. For the experimental range of aluminum powder particle sizes, with an increase in D50, the peak shock wave pressure first increased and then decreased, reaching the maximum at 24 μm; the shock wave and loss energy gradually increased, while the bubble energy, energy utilization, and underwater explosion total energy all gradually decreased. To a certain degree, the energy output structure can be regulated by adjusting the D50 under the premise of constant total energy of explosives. The experimental results improve our understanding of how D50 affects the underwater explosion parameters of aluminized explosives, which is of great significance for improving their energy utilization. [ABSTRACT FROM AUTHOR]

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration. [ABSTRACT FROM AUTHOR]

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP. [ABSTRACT FROM AUTHOR]

In the current study, complementary metasurface units with toroidal geometry were fabricated, using the computer numerical control engraving method. The metasurfaces were engraved in copper-coated, FR-4 plates. The produced metasurfaces were electromagnetically characterized in the microwave regime. Furthermore, they were studied regarding their energy harvesting capability, in the microwave range, where they absorb electromagnetic energy. It was found that toroidal structures harvest energy from the incident microwaves and transform it to electric power, through a simple rectification circuit. Moreover, their energy harvesting efficiency was found to be comparable or even superior to those of others reported so far. Therefore, the hereby obtained experimental results evidently show that engraved toroidal metasurfaces could potentially be used as energy harvesters in the microwave regime. [ABSTRACT FROM AUTHOR]

The singlet fission process involves the conversion of one singlet excited state into two triplet states, which has significant potential for enhancing the energy utilization efficiency of solar cells. Carotenoid, a typical π conjugated chromophore, exhibits specific aggregate morphologies known to display singlet fission behavior. In this study, we investigate the singlet fission process in lycopene H-aggregates using femtosecond stimulated Raman spectroscopy aided by quantum chemical calculation. The experimental results reveal two reaction pathways that effectively relax the S2 (11Bu+) state populations in lycopene H-aggregates: a monomer-like singlet excited state relaxation pathway through S2 (11Bu+) → 11Bu− → S1 (21Ag−) and a dominant sequential singlet fission reaction pathway involving the S2 (11Bu+) state, followed by S* state, a triplet pair state [1(TT)], eventually leading to a long lifetime triplet state T1. Importantly, the presence of both anionic and cationic fingerprint Raman peaks in the S* state is indicative of a substantial charge-transfer character. [ABSTRACT FROM AUTHOR]

E-commerce is a continuously growing sector significantly affected by sustainability issues during the last few years. To deal with economic, environmental and social sustainability aspects, e-commerce platforms consolidate orders to pick-up several requests from the same location, defining the so-called Few-to-Many Pick-up and Delivery Vehicle Routing Problem (F-M VRPPD). The proposed contribution addresses the optimisation of this problem by developing a multi-objective simulated annealing algorithm distinguished by four tailored Local Search (LS) operators specifically developed to increase the probability to identify feasible solutions and decrease the computational time. This algorithm is validated with several instances of a case study e-commerce platform based in an European mountain region. Firstly, the original LS operators are compared to benchmark literature ones to solve identical problems, reporting better performance in 84% of these instances. Furthermore, for the most relevant scenarios significant results are presented and discussed concerning the economic, environmental and social performance of the defined solutions according to the characteristics of the instances, as the routes height profile and the drivers' metabolic energy consumption. The tri-dimensional Pareto frontiers suggest how through a slight worsening in the economic objective function it is possible to improve the social one by up to 18.3% on average. [ABSTRACT FROM AUTHOR]

There is a lack of a design method for the manufacturing system reconfiguration to cope with the changing demand and evolving production technologies while minimising energy consumption. The key drivers for the new industrial paradigm are flexibility and sustainable manufacturing, which have been studied independently in the prior research. The aim of this research is to study two drivers simultaneously by designing robust models and analysing manufacturing system configurations to achieve feasible solutions in any scenario that may arise due to evolving, incomplete, and unforeseen production requirements, while minimising energy usage during product manufacture. To achieve this goal, this research develops a robustly validated pre-emptive decision engineering framework (DEF) for the manufacturing system reconfiguration process to manage future uncertainty of future conditions and identifies current production vulnerabilities and alternative production portfolios. In this research, a robust RMS reconfiguration strategy is designed using a compromise decision support problem (cDSP), and decentralised decision-making designs are explored through the use of game theory. The findings provide a new production system for adaptable, responsive, and sustainable manufacturing processes in the dynamic global economy. These results can empower stakeholders to make timely design decisions that lead to significant cost savings and sustainable manufacturing. [ABSTRACT FROM AUTHOR]

High charging loads associated with fleets of commercial electric vehicles (EVs) are expected to significantly stress electric power distribution networks, leading to high costs seen by fleet operators. To address these challenges, this report presents a highly flexible smart charging (SC) algorithm for managing EV fleets that arrive and depart from a common depot on a schedule. The algorithm features (i) primary consideration of multiple fleet operator preferences (e.g. minimizing cost, using carbon-free energy), (ii) secondary consideration of grid impact that leverages the existence of multiple optimal (or near-optimal) ways to satisfy fleet operator preferences, and (iii) automatic detection and handling of infeasibility due to large energy demands (characteristic of fleet charging). Provided in this document are two numerical impact assessment studies in which the SC algorithm is shown to be superior to conventional rapid charging, and conventional ‘smart’ charging solutions on the market. These studies utilize a set of synthetic, but realistic fleet charging requirements, a physics-based model of a real feeder and one year of real, hourly load data for that feeder. The first numerical study shows that the proposed SC algorithm can lead to significant (up to 44%, but scenario-dependent) reductions in a fleet operator’s annual electricity bill. The second numerical study shows that significant transformer overloading and voltage drop issues can be associated with conventional fleet charging methods, and that the proposed SC algorithm eliminates these issues, thereby enabling higher EV penetration levels and offsetting infrastructure upgrades. View the NCST Project Webpage

The widespread adoption of electric vehicles (EV) is a centerpiece of California’s strategy to reach net-zero carbon emissions, but it is not fully known how and where EVs are being used, and how and where they are being charged. This report provides the first at-scale estimate of EV home charging. Previous estimates were based on conflicting surveys or extrapolated from a small, unrepresentative sample of households with dedicated EV meters. We combined billions of hourly electricity meter measurements with address-level EV registration records from California households, including roughly 40,000 EV owners. The average EV increases overall household load by 2.9 kilowatt-hours per day, well under half the amount assumed by state regulators. Results imply that EVs travel less than expected on electric power, raising questions about transportation electrification for climate policy.

The global demand for high energy density batteries, mostly for application in electric vehicles, offering increased durability, safety, and sustainability is growing rapidly. In the past, this demand has been met primarily by the development and/or improvement of new/established battery materials and technologies. The high-entropy design concept—aiming at increasing chemical complexity/occupational disorder—has recently been introduced into the field of electrochemical energy storage. Various high-entropy battery materials that are seemingly capable of outperforming low-entropy counterparts by offering desirable properties have been reported. However, future studies are required to explore if the concept is broadly applicable and can be extended to all types of battery materials, especially those that are of industrial relevance. Herein, we provide a brief overview of the existing high-entropy anodes, cathodes, and solid/liquid electrolytes for use in rechargeable Li- or Na-ion batteries and discuss potential research directions and opportunities. [ABSTRACT FROM AUTHOR]

In this paper, a wideband metasurface-loaded (MTS-L) rectenna system is proposed to capture electromagnetic (EM) energy at arbitrary azimuth angles. The radiation patterns of different modes in the original MTS configuration are analyzed using the characteristic mode theory, and potential modes with omnidirectional radiation are screened out. By the arrangement of patches, the roundness performance of the radiation pattern can be ameliorated, and the omnidirectional characteristic is obtained over a wide frequency band. Subsequently, the surface current density of the selected mode is carefully and artificially designed to facilitate probe excitation as well as refrain from introducing complex power-combining networks. A wideband rectifier circuit is designed as the load of the proposed antenna. Eventually, measured results show that it operates from 4.6 to 9.6 GHz with a fractional bandwidth of 70.4%, and the peak system efficiency is 52.2%. The proposed system demonstrates excellent potential for wireless power transmission and EM energy harvesting in indoor environments. [ABSTRACT FROM AUTHOR]

Roughly, 50% of primary energy worldwide is rejected as waste heat over a wide range of temperatures. Waste heat above 573 K has the highest Carnot potential (> --> 50 %) to be converted to electricity due to higher Carnot efficiency. Thermoelectric (TE) materials have gained significant attention as potential candidates for efficient thermal energy conversion devices. Silicon nanowires (SiNWs) are promising materials for TE devices due to their unique electrical and thermal properties. In this study, we report the successful fabrication of high-quality double-sided SiNW arrays using advanced techniques. We engineered the double-sided structure to increase the surface area and the number of TE junctions, enhancing TE energy conversion efficiency. We also employed non-agglomeration wire tip engineering to ensure uniformity of the SiNWs and designed effective Ohmic contacts to improve overall TE efficiency. Additionally, we post-doped the double-sided SiNW arrays to achieve high electrical conductivity. Our results showed a significant improvement in the TE performance of the SiNW array devices, with a maximum figure-of-merit (ZT) value of 0.24 at 700 K, fabricated from the single SiNW with ZT of 0.71 at 700 K in our previous work [Yang et al., Nat. Commun. 12(1), 3926(2021)]. [ABSTRACT FROM AUTHOR]

In this study, we present a novel low-frequency magnetic field energy harvester (EH) employing beryllium bronze/Pb(Zr,Ti)O3 ceramic composited dual-beam structures with tip magnets attached to the inner and outer beams. This design incorporates the internal magnetic-coupled (IMC) effect, resulting in significantly enhanced coupling ability and a wide bandwidth. The validity of the IMC mechanism is confirmed through theoretical formulas and numerical simulations. By leveraging the IMC condition, the EH achieves an expanded bandwidth, which increases from 22 to 43 Hz. Moreover, the total output voltages at the inherent resonance and internal resonance are boosted by 15.4% and 32%, respectively. The performance of the IMC-EH can be further improved by increasing the number of the endmost magnets. Experimental investigations reveal that the IMC-EH generates a maximum RMS output power density of 56.25 μW Oe−2 cm−3, surpassing existing magnetically coupled piezoelectric energy harvesters. Remarkably, even under an ambient magnetic field as low as 1 Oe, the proposed IMC-EH still yields a total output power of 185 μW, sufficient to continuously power 26 LEDs in real time. This demonstrates its potential as a promising solution for low-power consumption small electronics. Furthermore, the implications of this work extend beyond its immediate benefits, as it inspires the design of future self-powered wireless sensor networks in the context of the Internet of Things. [ABSTRACT FROM AUTHOR]

Clathrate hydrates continue to be the focus of active research efforts due to their use in energy resources, transportation, and storage-related applications. Therefore, it is crucial to define their essential characteristics from a molecular standpoint. Understanding molecular structure in particular is crucial because it aids in understanding the mechanisms that lead to the formation or dissociation of clathrate hydrates. In the past, a wide variety of order parameters have been employed to classify and evaluate hydrate structures. An alternative approach to inventing bespoke order parameters is to apply machine learning techniques to automatically generate effective order parameters. In earlier work, we suggested a method for automatically designing novel parameters for ice and liquid water structures with Graph Neural Networks (GNNs). In this work, we use a GNN to implement our method, which can independently produce feature representations of the molecular structures. By using the TeaNet-type model in our method, it is possible to directly learn the molecular geometry and topology. This enables us to build novel parameters without prior knowledge of suitable order parameters for the structure type, discover structural differences, and classify molecular structures with high accuracy. We use this approach to classify the structures of clathrate hydrate structures: sI, sII, and sH. This innovative approach provides an appealing and highly accurate replacement for the traditional order parameters. Furthermore, our method makes clear the process of automatically designing a universal parameter for liquid water, ice, and clathrate hydrate to analyze their structures and phases. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant. [ABSTRACT FROM AUTHOR]

This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem. [ABSTRACT FROM AUTHOR]

Time-of-use (TOU) tariff has been implemented in the manufacturing industry to improve energy efficiency by regulating the electricity imbalance between supply and demand. Besides, not-all-machine (NAM) option is another way of energy-saving by using only a subset of all the available machines. This study investigates a robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option. Only interval bounds on job processing times are known. The problem is first formulated into a min–max regret model to maximise the robustness. Based on problem properties, both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments on 240 randomly generated instances with up to 20 jobs are conducted to evaluate the performance of the developed methods. Besides, 900 large-sized randomly generated instances with up to 150 jobs are tested for sensitivity analysis and to identify managerial insights for achieving energy-efficient schedules. [ABSTRACT FROM AUTHOR]

To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid. View the NCST Project Webpage