Dead-zone-compensated design as general method of flow field optimization for redox flow batteries.

One essential element of redox flow batteries (RFBs) is the flow field. Certain dead zones that cause local overpotentials and side effects are present in all conventional designs. To lessen the detrimental effects, a dead-zone-compensated design of flow field optimization is proposed. The proposed architecture allows for the detection of dead zones and their compensation on existing flow fields. Higher reactant concentrations and uniformity factors can be revealed in the 3D multiphysical simulation. The experiments also demonstrate that at an energy efficiency (EE) of 80%, the maximum current density of the novel flow field is 205 mA cm-2, which is much higher than the values for the previous ones (165 mA cm-2) and typical serpentine flow field (153 mA cm-2). Extensions of the design have successfully increased system EE (2.7 to 4.3%) for a variety of flow patterns. As a result, the proposed design is demonstrated to be a general method to support the functionality and application of RFBs.

Multilayered optofluidics for sustainable buildings.

Indoor climate control is among the most energy-intensive activities conducted by humans. A building facade that can achieve versatile climate control directly, through independent and multifunctional optical reconfigurations, could significantly reduce this energy footprint, and its development represents a pertinent unmet challenge toward global sustainability. Drawing from optically adaptive multilayer skins within biological organisms, we report a multilayered millifluidic interface for achieving a comprehensive suite of independent optical responses in buildings. We digitally control the flow of aqueous solutions within confined milliscale channels, demonstrating independent command over total transmitted light intensity (95% modulation between 250 and 2,500 nm), near-infrared-selective absorption (70% modulation between 740 and 2,500 nm), and dispersion (scattering). This combinatorial optical tunability enables configurable optimization of the amount, wavelength, and position of transmitted solar radiation within buildings over time, resulting in annual modeled energy reductions of more than 43% over existing technologies. Our scalable "optofluidic" platform, leveraging a versatile range of aqueous chemistries, may represent a general solution for the climate control of buildings.

Energy-efficient network activity from disparate circuit parameters.

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances.

Asymmetric Electrode Design for High-Area Capacity and High-Energy Efficiency Hybrid Zn Batteries.

Compared to Zn-air batteries, by integrating Zn-transition metal compound reactions and oxygen redox reactions at the cell level, hybrid Zn batteries are proposed to achieve higher energy density and energy efficiency. However, attaining relatively higher energy efficiency relies on controlling the discharge capacity. At high area capacities, the proportion of the high voltage section can be neglected, resulting in a lower energy efficiency similar to that of Zn-air batteries. Here, a high-loading integrated electrode with an asymmetric structure and asymmetric wettability is fabricated, which consists of a thick nickel hydroxide (Ni(OH)2) electrode layer with vertical array channels achieving high capacity and high utilization, and a thin NiCo2O4 nanopartical-decorated N-doped graphene nanosheets (NiCo2O4/N-G) catalyst layer with superior oxygen catalytic activity. The asymmetric wettability satisfies the wettability requirements for both Zn-Ni and Zn-air reactions. The hybrid Zn battery with the integrated electrode exhibits a remarkable peak power density of 141.9 mW cm-2, superior rate performance with an energy efficiency of 71.4% even at 20 mA cm-2, and exceptional cycling stability maintaining a stable energy efficiency of ≈84% at 2 mA cm-2 over 100 cycles (400 h).

Robust, Narrow-Band Nanorods LEDs with Luminous Efficacy > 200 lm/W: Next-Generation of Efficient Solid-State Lighting.

Energy-efficient white light-emitting diodes (LEDs) are in high demand across the society. Despite the significant advancements in the modern lighting industry based on solid-state electronics and inorganic phosphor, solid-state lighting (SSL) continues to pursue improved efficiency, saturated color performance, and longer lifetime. Here in this article, robust, narrow emission band nanorods (NRs) are disclosed with tailored wavelengths, aiming to enhance the color rendering index (CRI) and luminous efficacy (LE). The fabricated lighting device consists of NRs of configuration CdSe/ZnxCd1-xS/ZnS, which can independently tune CRI R1-R9 values and maximize the luminous efficacy. For general lighting, NRs with quantum yield (QY) up to 96% and 99% are developed, resulting in ultra-efficient LEDs reaching a record high luminous efficacy of 214 lm W-1 (certified by the National Accreditation Service). Furthermore, NRs are deployed onto mid-power (0.3 W@ 50 mA) LEDs, showing significantly enhanced long-term stability (T95 = 400 h @ 50 mA). With these astonishing properties, the proposed NRs can pave the way for efficient lighting with desired optical spectrum.

Ni Single-Atom Bual Catalytic Electrodes for Long Life and High Energy Efficiency Zinc-Iodine Batteries.

Zinc-iodine batteries (Zn-I2) are extremely attractive as the safe and cost-effective scalable energy storage system in the stationary applications. However, the inefficient redox kinetics and "shuttling effect" of iodine species result in unsatisfactory energy efficiency and short cycle life, hindering their commercialization. In this work, Ni single atoms highly dispersed on carbon fibers is designed and synthesized as iodine anchoring sites and dual catalysts for Zn-I2 batteries, and successfully inhibit the iodine species shuttling and boost dual reaction kinetics. Theoretical calculations indicate that the reinforced d-p orbital hybridization and charge interaction between Ni single-atoms and iodine species effectively enhance the confinement of iodine species. Ni single-atoms also accelerate the iodine conversion reactions with tailored bonding structure of I─I bonds and reduced energy barrier for the dual conversion of iodine species. Consequently, the high-rate performance (180 mAh g-1 at 3 A g-1), cycling stability (capacity retention of 74% after 5900 cycles) and high energy efficiency (90% at 3 A g-1) are achieved. The work provides an effective strategy for the development of iodine hosts with high catalytic activity for Zn-I2 batteries.

Cu Based Dilute Alloys for Tuning the C2+ Selectivity of Electrochemical CO2 Reduction.

Electrochemical CO2 reduction is a promising technology for replacing fossil fuel feedstocks in the chemical industry but further improvements in catalyst selectivity need to be made. So far, only copper-based catalysts have shown efficient conversion of CO2 into the desired multi-carbon (C2+) products. This work explores Cu-based dilute alloys to systematically tune the energy landscape of CO2 electrolysis toward C2+ products. Selection of the dilute alloy components is guided by grand canonical density functional theory simulations using the calculated binding energies of the reaction intermediates CO*, CHO*, and OCCO* dimer as descriptors for the selectivity toward C2+ products. A physical vapor deposition catalyst testing platform is employed to isolate the effect of alloy composition on the C2+/C1 product branching ratio without interference from catalyst morphology or catalyst integration. Six dilute alloy catalysts are prepared and tested with respect to their C2+/C1 product ratio using different electrolyzer environments including selected tests in a 100-cm2 electrolyzer. Consistent with theory, CuAl, CuB, CuGa and especially CuSc show increased selectivity toward C2+ products by making CO dimerization energetically more favorable on the dominant Cu facets, demonstrating the power of using the dilute alloy approach to tune the selectivity of CO2 electrolysis.

Target for lipid-to-carbohydrate intake minimizes cost of growth.

Many theoretical treatments of foraging use energy as currency, with carbohydrates and lipids considered interchangeable as energy sources. However, herbivores must often synthesize lipids from carbohydrates since they are in short supply in plants, theoretically increasing the cost of growth. We tested whether a generalist insect herbivore (Locusta migratoria) can improve its growth efficiency by consuming lipids, and whether these locusts have a preferred caloric intake ratio of carbohydrate to lipid (C : L). Locusts fed pairs of isocaloric, isoprotein diets differing in C and L consistently selected a 2C : 1L target. Locusts reared on isocaloric, isoprotein 3C : 0L diets attained similar final body masses and lipid contents to locusts fed the 2C : 1L diet, but they ate more and had a ~12% higher metabolic rate, indicating an energetic cost for lipogenesis. These results demonstrate that some animals can selectively regulate carbohydrate-to-lipid intake and that consumption of dietary lipids can improve growth efficiency.

Air Quality and Health Implications of Coal Power Retirements Attributed to Industrial Electricity Savings in China.

The coal-dominated electricity system, alongside increasing industrial electricity demand, places China into a dilemma between industrialization and environmental impacts. A practical solution is to exploit air quality and health cobenefits of industrial energy efficiency measures, which has not yet been integrated into China's energy transition strategy. This research examines the pivotal role of industrial electricity savings in accelerating coal plant retirements and assesses the nexus of energy-pollution-health by modeling nationwide coal-fired plants at individual unit level. It shows that minimizing electricity needs by implementing more efficient technologies leads to the phaseout of 1279 hyper-polluting units (subcritical, <300 MW) by 2040, advancing the retirement of these units by an average of 7 years (3-16 years). The retirements at different locations yield varying levels of air quality improvements (9-17%), across six power grids. Reduced exposure to PM2.5 could avoid 123,100 pollution-related cumulative deaths over the next 20 years from 2020, of which ∼75% occur in the Central, East, and North grids, particularly coal-intensive and populous provinces (e.g., Shandong and Jiangsu). These findings provide key indicators to support geographically specific policymaking and lay out a rationale for decision-makers to incorporate multiple benefits into early coal phaseout strategies to avoid lock-in risk.

A 3D atlas of functional human brain energetic connectome based on neuropil distribution.

The human brain is energetically expensive, yet the key factors governing its heterogeneous energy distributions across cortical regions to support its diversity of functions remain unexplored. Here, we built up a 3D digital cortical energy atlas based on the energetic costs of all neuropil activities into a high-resolution stereological map of the human cortex with cellular and synaptic densities derived, respectively, from ex vivo histological staining and in vivo PET imaging. The atlas was validated with PET-measured glucose oxidation at the voxel level. A 3D cortical activity map was calculated to predict the heterogeneous activity rates across all cortical regions, which revealed that resting brain is indeed active with heterogeneous neuronal activity rates averaging around 1.2 Hz, comprising around 70% of the glucose oxidation of the cortex. Additionally, synaptic density dominates spatial patterns of energetics, suggesting that the cortical energetics rely heavily on the distribution of synaptic connections. Recent evidence from functional imaging studies suggests that some cortical areas act as hubs (i.e., interconnecting distinct and functionally active regions). An inverse allometric relationship was observed between hub metabolic rates versus hub volumes. Hubs with smaller volumes have higher synapse density, metabolic rate, and activity rates compared to nonhubs. The open-source BrainEnergyAtlas provides a granular framework for exploring revealing design principles in energy-constrained human cortical circuits across multiple spatial scales.

Prefeasibility analysis of biomass gasification and electrolysis for hydrogen production.

Hydrogen is a key energy vector to accomplishing energy transition and decarbonization goals proposed in the transport and industrial sectors worldwide. In recent years, research has focused on analyzing, designing, and optimizing hydrogen production, searching to improve economic prefeasibility with minimal emissions of polluting gases. Therefore, the techno-economic analysis of hydrogen production by electrolytic and gasification processes becomes relevant since these processes could compete commercially with industrial technologies such as SMR - Steam methane reforming. This work aims to analyze hydrogen production in stand-alone processes and energy-driven biorefineries. The gasification and electrolysis technologies were evaluated experimentally, and the yields obtained were input data for scaling up the processes through simulation tools. Biomass gasification is more cost-effective than electrolytic schemes since the hydrogen production costs were 4.57 USD/kg and 8.30 USD/kg at an annual production rate of 491.6 tons and 38.96 tons, respectively. Instead, the electrolysis process feasibility is strongly influenced by the recycled water rate and the electricity cost. A sensitivity analysis was performed to evaluate the temperature, pressure, and current density variability on the hydrogen production rate. The increase in pressure and current density induces parasitic currents while the temperature increases hydrogen production. Although higher hydrogen production rates from gasification, the syngas composition decreases the possibility of being implemented in applications where purity is critical.

Designing energy-efficient buildings in urban centers through machine learning and enhanced clean water managements.

Rainwater Harvesting (RWH) is increasingly recognized as a vital sustainable practice in urban environments, aimed at enhancing water conservation and reducing energy consumption. This study introduces an innovative integration of nano-composite materials as Silver Nanoparticles (AgNPs) into RWH systems to elevate water treatment efficiency and assess the resulting environmental and energy-saving benefits. Utilizing a regression analysis approach with Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), this study will reach the study objective. In this study, the inputs are building attributes, environmental parameters, sociodemographic factors, and the algorithms SVM and KNN. At the same time, the outputs are predicted energy consumption, visual comfort outcomes, ROC-AUC values, and Kappa Indices. The integration of AgNPs into RWH systems demonstrated substantial environmental and operational benefits, achieving a 57% reduction in microbial content and 20% reductions in both chemical usage and energy consumption. These improvements highlight the potential of AgNPs to enhance water safety and reduce the environmental impact of traditional water treatments, making them a viable alternative for sustainable water management. Additionally, the use of a hybrid SVM-KNN model effectively predicted building energy usage and visual comfort, with high accuracy and precision, underscoring its utility in optimizing urban building environments for sustainability and comfort.

The promise of energy-efficient battery-powered urban aircraft.

Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 mi of range with payload equivalents of up to seven passengers. Novel UAM aircraft consume between 130 Wh/passenger-mi and ∼ 1,200 Wh/passenger-mi depending on the design and utilization, compared to an expected consumption of over 220 Wh/passenger-mi and 1,000 Wh/passenger-mi for terrestrial electric vehicles and combustion engine vehicles, respectively. We also find that several UAM aircraft designs are approaching technological viability with current Li-ion batteries, based on the specific power and energy, while rechargeability and lifetime performance remain uncertain. These aspects highlight the technological readiness of a new segment of transportation.

An energy efficiency assessment of Yttrium-aluminum-garnet laser in vitro.

To study the effect range of the Nd:YAG laser through various levels of cloudy medium for targets with varying grayscale values in vitro. The coated paper cards with grayscale values of 0, 50, 100, and 150 were used as the laser's targets, which were struck straightly with varying energies using three burst modes (single pulse, double pulse, and triple pulse). Six filters (transmittances of 40, 50, 60, 70, 80, and 90) were applied to simulate various levels of cloudy refractive medium. Image J software was used to measure the diameters and regions of the laser spots. The ranges of the Nd:YAG laser spots increased with energy in the same burst mode (P < 0.05). Under the same amount of energy, the ranges of the Nd:YAG laser spot increased with the grayscale value of the targets (P < 0.05). The greater the transmittance of the filters employed, the larger the range of the Nd: YAG laser spots produced. Assuming that the total pulse energy is identical, the effect ranges of multi-pulse burst modes were significantly larger than those of single-pulse burst mode (P < 0.05). The effect range of a Nd:YAG laser grows with increasing energy and the target's grayscale value. A cloudy refractive medium has a negative impact on the effect range of the Nd: YAG laser. The single pulse mode has the narrowest and safest efficiency range.

Energy-Efficient BWP Configuration for Multi-Slice Users.

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service.

Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel's dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

Clustered Routing Using Chaotic Genetic Algorithm with Grey Wolf Optimization to Enhance Energy Efficiency in Sensor Networks.

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm-grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing.

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks.

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Enhancing Occupant Comfort and Building Sustainability: Lessons from an Internet of Things-Based Study on Centrally Controlled Indoor Shared Spaces in Hot Climatic Conditions.

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases-both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50-100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.