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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]

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]

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]

The H2O and H2O2 molecules resemble each other in a multitude of ways as has been noted in the literature. Here, we present density functional theory (DFT) calculations for the H2O2(s) and H2O2·2H2O(s) crystals and make selected comparisons with ice polymorphs. The performance of a number of dispersion-corrected density functionals—both self-consistent and a posteriori ones—are assessed, and we give special attention to the D3 correction and its effects. The D3 correction to the lattice energies is large: for H2O2(s) the D3 correction constitutes about 25% of the lattice energy using PBE, much more for RPBE, much less for SCAN, and it primarily arises from non-H-bonded interactions out to about 5 Å.The large D3 corrections to the lattice energies are likely a consequence of several effects: correction for missing dispersion interaction, the ability of D3 to capture and correct various other kinds of limitations built into the underlying DFT functionals, and finally some degree of cell-contraction-induced polarization enhancement. We find that the overall best-performing functionals of the twelve examined are optPBEvdW and RPBE-D3. Comparisons with DFT assessments for ices in the literature show that where the same methods have been used, the assessments largely agree. [ABSTRACT FROM AUTHOR]

Many experimental and theoretical studies on CH4–CO2 hydrates have been performed aiming at the extraction of CH4 as a relatively clean energy resource and concurrent sequestration of CO2. However, vague or insufficient characterization of the environmental conditions prevents us from a comprehensive understanding of even equilibrium properties of CH4–CO2 hydrates for this substitution. We propose possible reaction schemes for the substitution, paying special attention to the coexisting phases, the aqueous and/or the fluid, where CO2 is supplied from and CH4 is transferred to. We address the two schemes for the substitution operating in three-phase and two-phase coexistence. Advantages and efficiencies of extracting CH4 in the individual scheme are estimated from the chemical potentials of all the components in all the phases involved in the substitution on the basis of a statistical mechanical theory developed recently. It is found that although substitution is feasible in the three-phase coexistence, its working window in temperature–pressure space is much narrower compared to the two-phase coexistence condition. Despite that the substitution normally generates only a small amount of heat, a large endothermic substitution is suggested in the medium pressure range, caused by the vaporization of liquid CO2 due to mixing with a small amount of the released CH4. This study provides the first theoretical framework toward the practical use of hydrates replacing CH4 with CO2 and serves as a basis for quantitative planning. [ABSTRACT FROM AUTHOR]

Bipolar gradual resistive switching was investigated in ITO/InGaZnO/ITO resistive switching devices. Controlled intrinsic oxygen vacancy formation inside the switching layer enabled the establishment of a stable multilevel memory state, allowing for RESET voltage control and non-degradable data endurance. The ITO/InGaZnO interface governs the migration of oxygen ions and redox reactions within the switching layer. Voltage–stress-induced electron trapping and oxygen vacancy formation were observed before conductive filament electroforming. This device mimicked biological synapses, demonstrating short- and long-term potentiation and depression through electrical pulse sequences. Modulation of post-synaptic currents and pulse frequency-dependent short-term potentiation were successfully emulated in the InGaZnO-based artificial synapse. The ITO/InGaZnO/ITO memristor exhibited spike–amplitude-dependent plasticity, spike–rate-dependent plasticity, and potentiation–depression synaptic learning with low energy consumption, making it a promising candidate for large-scale integration. [ABSTRACT FROM AUTHOR]

The quest for lowering energy consumption during thin film growth by magnetron sputtering techniques becomes of particular importance in view of sustainable development goals. As large fraction of the process energy is consumed in substrate heating for the purpose of providing high adatom mobility necessary to grow dense films, the most straightforward strategy toward more environment-friendly processing is to find alternatives to thermally activated surface diffusion. One possibility is offered by high mass metal ion irradiation of the growing film surface, which has been recently shown to be very effective in densification of transition metal nitride layers grown with no external heating, such that Zone 2 microstructures of the structure-zone model are obtained in the substrate temperature Ts range otherwise typical for Zone 1 growth. The large mass difference between the incident ion and the atoms constituting the film results in effective creation of low energy recoils, which leads to film densification at low Ts. Due to their high mass, metal ions become incorporated at lattice sites beyond the near-surface region of intense recoil generation leading to further densification, while preventing the buildup of residual stress. The practical implementation of this technique discussed in this Perspective employs heavy metal targets operating in the high-power impulse magnetron sputtering (HiPIMS) mode to provide periodic metal-ion fluxes that are accelerated in the electric field of the substrate to irradiate layers deposited from direct current magnetron sputtering (DCMS) sources. A key feature of this hybrid HiPIMS/DCMS configuration is the substrate bias that is synchronized with heavy metal ion fluxes for selective control of their energy and momentum. As a consequence, the major fraction of process energy is used at sputtering sources and for film densification, rather than for heating of the entire vacuum vessel. Model material systems include TiN and metastable NaCl-structure Ti1−yAlyN films, which are well-known for challenges in stoichiometry and phase stability control, respectively, and are of high relevance for industrial applications. This Perspective provides a comprehensive overview of the novel film growth method. After presenting basic concepts, time-resolved measurements of ion fluxes at the substrate plane, essential for selective control of metal ion energy and momentum, are discussed. The role of metal ion mass, energy, momentum, and concentration is described in more detail. As some applications require substrate rotation for conformal coating, a section is devoted to the related complexity in the implementation of metal-ion-synchronized growth under industrial conditions. [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]

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]

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]

A novel approach to rapidly converging high-level coupled-cluster (CC) energetics in an automated fashion is proposed. The key idea is an adaptive selection of excitation manifolds defining higher--than--two-body components of the cluster operator inspired by CC(P;Q) moment expansions. The usefulness of the resulting methodology is illustrated by molecular examples where the goal is to recover the electronic energies obtained using the CC method with a full treatment of singly, doubly, and triply excited clusters (CCSDT) when the noniterative triples corrections to CCSD fail. [ABSTRACT FROM AUTHOR]

Enhancing the utilization of absorbed light is essential for enhancing the efficiency of solar energy conversion via artificial photosynthesis. In this work, we report the successful incorporation of Rhodamine B (RhB) into the pore of ZIF-8 (ZIF = zeolitic imidazolate framework) and the efficient energy transfer process from RhB to Co-doped ZIF-8. Using transient absorption spectroscopy, we show that energy transfer only occurs from RhB (donor) to Co center (acceptor) when RhB is confined into the ZIF-8 structure, which is in stark contrast to the system based on the physical mixture of RhB with Co-doped ZIF-8, where negligible energy transfer was observed. In addition, energy transfer efficiency increases with the concentration of Co and reaches a plateau when the molar ratio of Co to RhB reaches 32. These results suggest that RhB confined in the ZIF-8 structure is essential for energy transfer to occur, and energy transfer efficiency can be controlled by tuning the concentration of acceptors. [ABSTRACT FROM AUTHOR]

A multivariant statistical approach was used to identify treatment conditions that improve the survivability of structural reactive material (SRM) projectiles upon launch and enhance energy release upon impact. The study included both mechanical testing of projectiles as well as their reactive characterization. The projectiles were launched in a high-velocity impact-ignition testing system and impacted an anvil for vented chamber calorimetry. This study examined a link between ultimate compressive stress and combustion performance. Two treatments were applied to consolidated aluminum projectiles including annealing and addition of silica (SiO2) inclusions. Results showed annealing at moderate temperatures resulted in intact SRM projectiles upon launch. Adding small concentrations (1–2 wt. %) of SiO2 to the SRM promoted fragmentation and combustion performance upon impact. Compared to the untreated projectiles, annealing with SiO2 inclusion processing treatments improved the energy conversion efficiency from 37–84% (for untreated projectiles) up to 54–98%. Increasing interparticle dislocation recovery by annealing while balancing inclusions promoting fragmentation upon impact was the key to optimizing combustion performance for SRM ballistic impact applications. [ABSTRACT FROM AUTHOR]

The many-body expansion (MBE) is promising for the efficient, parallel computation of lattice energies in organic crystals. Very high accuracy should be achievable by employing coupled-cluster singles, doubles, and perturbative triples at the complete basis set limit [CCSD(T)/CBS] for the dimers, trimers, and potentially tetramers resulting from the MBE, but such a brute-force approach seems impractical for crystals of all but the smallest molecules. Here, we investigate hybrid or multi-level approaches that employ CCSD(T)/CBS only for the closest dimers and trimers and utilize much faster methods like Møller–Plesset perturbation theory (MP2) for more distant dimers and trimers. For trimers, MP2 is supplemented with the Axilrod–Teller–Muto (ATM) model of three-body dispersion. MP2(+ATM) is shown to be a very effective replacement for CCSD(T)/CBS for all but the closest dimers and trimers. A limited investigation of tetramers using CCSD(T)/CBS suggests that the four-body contribution is entirely negligible. The large set of CCSD(T)/CBS dimer and trimer data should be valuable in benchmarking approximate methods for molecular crystals and allows us to see that a literature estimate of the core-valence contribution of the closest dimers to the lattice energy using just MP2 was overbinding by 0.5 kJ mol−1, and an estimate of the three-body contribution from the closest trimers using the T0 approximation in local CCSD(T) was underbinding by 0.7 kJ mol−1. Our CCSD(T)/CBS best estimate of the 0 K lattice energy is −54.01 kJ mol−1, compared to an estimated experimental value of −55.3 ± 2.2 kJ mol−1. [ABSTRACT FROM AUTHOR]

In this paper, we use the previously introduced Canonical Polyadic (CP)-Multiple Shift Block Inverse Iteration (MSBII) eigensolver [S. D. Kallullathil and T. Carrington, J. Chem. Phys. 155, 234105 (2021)] in conjunction with a contraction tree to compute vibrational spectra. The CP-MSBII eigensolver uses the CP format. The memory cost scales linearly with the number of coordinates. A tensor in CP format represents a wavefunction constrained to be a sum of products (SOP). An SOP wavefunction can be made more accurate by increasing the number of terms, the rank. When the required rank is large, the runtime of a calculation in CP format is long, although the memory cost is small. To make the method more efficient, we break the full problem into pieces using a contraction tree. The required rank for each of the sub-problems is small. To demonstrate the effectiveness of the ideas, we computed vibrational energy levels of acetonitrile (12-D) and ethylene oxide (15-D). [ABSTRACT FROM AUTHOR]

Smart manufacturing has boosted the wide application of mobile robots in robotic cells for automated material delivery. However, the mismatching between machine production process and robot movement process causes extensive energy waste. Nevertheless, most existing robotic job-shop scheduling (RJSP) studies mainly focus on minimising makespan but overlook the low energy efficiency problem faced by robotic cells. Motivated by the importance of green smart manufacturing, in this study, we innovatively propose to achieve robotic cell energy saving through coordinating the machine production process and robot movement process. Specifically, both machines and the mobile robot can flexibly adjust operating speeds with a V-scale speed framework. Two novel energy-efficient RJSP approaches (i.e. the RJSP-E and the RJSP-EM) are thus proposed. The RJSP-E focuses on minimising energy consumption, while the RJSP-EM simultaneously considers makespan (i.e. productivity) and energy consumption. Through computational experiments, the RJSP-E demonstrates superior performances in reducing energy consumption (15% on average), at a loss of productivity (20% on average). On the other hand, the RJSP-EM can select the most suitable energy-saving operating speeds without much sacrifice in productivity. Notably, the RJSP-EM can reduce energy consumption by a mean of 10% even without increasing makespan. The RJSP-EM also demonstrates higher solution efficiency. [ABSTRACT FROM AUTHOR]