Thermal Surface Properties, London Dispersive and Polar Surface Energy of Graphene and Carbon Materials Using Inverse Gas Chromatography at Infinite Dilution.

The thermal surface properties of graphenes and carbon materials are of crucial importance in the chemistry of materials, chemical engineering, and many industrial processes. BACKGROUND: The determination of these surface properties is carried out using inverse gas chromatography at infinite dilution, which leads to the retention volume of organic solvents adsorbed on solid surfaces. This experimental and fundamental parameter actually reflects the surface thermodynamic interactions between injected probes and solid substrates. METHODS: The London dispersion equation and the Hamieh thermal model are used to quantify the London dispersive and polar surface energy of graphenes and carbon fibers as well their Lewis acid-base constants by introducing the coupling amphoteric constant of materials. RESULTS: The London dispersive and polar acid-base surface energies, the free energy of adsorption, the polar enthalpy and entropy, and the Lewis acid-base constants of graphenes and carbon materials are determined. CONCLUSIONS: It is shown that graphene exhibited the highest values of London dispersive surface energy, polar surface energy, and Lewis acid-base constants. The highest characteristics of graphene justify its great potentiality and uses in many industrial applications.

Dietary Intake of Adolescents and Alignment with Recommendations for Healthy and Sustainable Diets: Results of the SI.Menu Study.

BACKGROUND: The SI.Menu study offers the latest data on the dietary intake of Slovenian adolescents aged 10 to 17. The purpose of this study is to comprehensively assess their dietary intake (energy and nutrients) and compare their food intakes with dietary recommendations for healthy and sustainable diets. METHODS: The cross-sectional epidemiological dietary study SI.Menu (March 2017-April 2018) was conducted on a representative sample of Slovenian adolescents aged 10 to 17 years (n = 468) (230 males and 238 females). Data on dietary intake were gathered through two non-consecutive 24 h recalls, in line with the European Food Safety Authority (EFSA) EU Menu methodology. The repeated 24 h Dietary Recall (HDR) and Food Propensity Questionnaire (FPQ) data were combined to determine the usual intakes of nutrients and food groups, using the Multiple Source Method (MSM) program. RESULTS: Adolescents' diets significantly deviate from dietary recommendations, lacking vegetables, milk and dairy products, nuts and seeds, legumes, and water, while containing excessive meat (especially red meat) and high-sugar foods. This results in insufficient intake of dietary fibre, and nutrients such as vitamin D, folate, and calcium. CONCLUSIONS: The dietary intake of Slovenian adolescents does not meet healthy and sustainable diet recommendations. This study provides an important insight into the dietary habits of Slovenian adolescents that could be useful for future public health strategies.

The Combination of Lactoferrin and Creatine Ameliorates Muscle Decay in a Sarcopenia Murine Model.

BACKGROUND: Sarcopenia is an age-related condition characterized by progressive loss of muscle mass, strength, and function. The occurrence of sarcopenia has a huge impact on physical, psychological, and social health. Therefore, the prevention and treatment of sarcopenia is becoming an important public health issue. METHOD: 35 six-week-old male C57BL/6 mice were randomly divided into five groups, one of which served as a control group, while the rest of the groups were constructed as a model of sarcopenia by intraperitoneal injection of D-galactose. The intervention with lactoferrin, creatine, and their mixtures, respectively, was carried out through gavage for 8 weeks. Muscle function was assessed based on their endurance, hanging time, and grip strength. The muscle tissues were weighed to assess the changes in mass, and the muscle RNA was extracted for myogenic factor expression and transcriptome sequencing to speculate on the potential mechanism of action by GO and KEGG enrichment analysis. RESULT: The muscle mass (lean mass, GAS index), and muscle function (endurance, hanging time, and grip strength) decreased, and the size and structure of myofiber was smaller in the model group compared to the control group. The intervention with lactoferrin and creatine, either alone or combination, improved muscle mass and function, restored muscle tissue, and increased the expression of myogenic regulators. The combined group demonstrated the most significant improvement in these indexes. The RNA-seq results revealed enrichment in the longevity-regulated pathway, MAPK pathway, focal adhesion, and ECM-receptor interaction pathway in the intervention group. The intervention group may influence muscle function by affecting the proliferation, differentiation, senescence of skeletal muscle cell, and contraction of muscle fiber. The combined group also enriched the mTOR-S6K/4E-BPs signaling pathway, PI3K-Akt signaling pathway, and energy metabolism-related pathways, including Apelin signaling, insulin resistance pathway, and adipocytokine signaling pathway, which affect energy metabolism in muscle. CONCLUSIONS: Lactoferrin and creatine, either alone or in combination, were found to inhibit the progression of sarcopenia by influencing the number and cross-sectional area of muscle fibers and muscle protein synthesis. The combined intervention appears to exert a more significant effect on energy metabolism.

Sky Image Classification Based on Transfer Learning Approaches.

Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

Experimental Demonstration of a Tunable Energy-Selective Gamma-Ray Imaging System Based on Recoil Electrons.

The domain of gamma-ray imaging necessitates technological advancements to surmount the challenge of energy-selective imaging. Conventional systems are constrained in their dynamic focus on specific energy ranges, a capability imperative for differentiating gamma-ray emissions from diverse sources. This investigation introduces an innovative imaging system predicated on the detection of recoil electrons, addressing the demand for adjustable energy selectivity. Our methodology encompasses the design of a gamma-ray imaging system that leverages recoil electron detection to execute energy-selective imaging. The system's efficacy was investigated experimentally, with emphasis on the adaptability of the energy selection window. The experimental outcomes underscore the system's adeptness at modulating the energy selection window, adeptly discriminating gamma rays across a stipulated energy spectrum. The results corroborate the system's adaptability, with an adjustable energy resolution that coincides with theoretical projections and satisfies the established criteria. This study affirms the viability and merits of utilizing recoil electrons for tunable energy-selective gamma-ray imaging. The system's conceptualization and empirical validation represent a notable progress in gamma-ray imaging technology, with prospective applications extending from medical imaging to astrophysics. This research sets a solid foundation for subsequent inquiries and advancements in this domain.

Elastic Self-Recovering Hybrid Nanogenerator for Water Wave Energy Harvesting and Marine Environmental Monitoring.

To achieve large-scale development of triboelectric nanogenerators (TENGs) for water wave energy harvesting and powering the colossal sensors widely distributed in the ocean, facile and scalable TENGs with high output are urgently required. Here, an elastic self-recovering hybrid nanogenerator (ES-HNG) is proposed for water wave energy harvesting and marine environmental monitoring. The elastic skeletal support of the ES-HNG is manufactured using three-dimensional (3D) printing technology, which is more conducive to the large-scale integration of the ES-HNG. Moreover, the combination of a TENG and an electromagnetic generator (EMG) optimizes the utilization of device space, leading to enhanced energy harvesting efficiency. Experimental results demonstrate that the TENG achieves a peak power output of 42.68 mW, and the EMG reaches a peak power output of 4.40 mW. Furthermore, various marine environment monitoring sensors, such as a self-powered wireless meteorological monitoring system, a wireless alarm system, and a water quality monitoring pen, have been successfully powered by the sophisticated ES-HNG. This work introduces an ES-HNG for water wave energy harvesting, which demonstrates potential in marine environment monitoring and offers a new solution for the sustainable development of the marine internet of things.

Embroidery Triboelectric Nanogenerator for Energy Harvesting.

Triboelectric nanogenerators (TENGs) are devices that efficiently transform mechanical energy into electrical energy by utilizing the triboelectric effect and electrostatic induction. Embroidery triboelectric nanogenerators (ETENGs) offer a distinct prospect to incorporate energy harvesting capabilities into textile-based products. This research work introduces an embroidered triboelectric nanogenerator that is made using polyester and nylon 66 yarn. The ETENG is developed by using different embroidery parameters and its characteristics are obtained using a specialized tapping and friction device. Nine ETENGs were made, each with different stitch lengths and line spacings for the polyester yarn. Friction and tapping tests were performed to assess the electrical outputs, which included measurements of short circuit current, open circuit voltage, and capacitor charging. One sample wearable embroidered energy harvester collected 307.5 µJ (24.8 V) of energy under a 1.5 Hz sliding motion over 300 s and 72 µJ (12 V) of energy through human walking over 120 s. Another ETENG sample generated 4.5 µJ (3 V) into a 1 µF capacitor using a tapping device with a 2 Hz frequency and a 50 mm separation distance over a duration of 520 s. Measurement of the current was also performed at different pressures to check the effect of pressure and validate the different options of the triboelectric/electrostatic characterization device. In summary, this research explains the influence of embroidery parameters on the performance of ETENG (Embroidery Triboelectric Nanogenerator) and provides valuable information for energy harvesting applications.

Comprehensive Characterisation of a Low-Frequency-Vibration Energy Harvester.

In this paper, we describe a measurement procedure to fully characterise a novel vibration energy harvester operating in the ultra-low-frequency range. The procedure, which is more thorough than those usually found in the literature, comprises three main stages: modelling, experimental characterisation and parameter identification. Modelling is accomplished in two alternative ways, a physical model (white box) and a mixed one (black box), which model the magnetic interaction via Fourier series. The experimental measurements include not only the input (acceleration)-output (energy) response but also the (internal) dynamic behaviour of the system, making use of a synchronised image processing and signal acquisition system. The identification procedure, based on maximum likelihood, estimates all the relevant parameters to characterise the system to simulate its behaviour and helps to optimise its performance. While the method is custom-designed for a particular harvester, the comprehensive approach and most of its procedures can be applied to similar harvesters.

Evaluation Method for Underwater Ultrasonic Energy Radiation Performance Based on the Spatial Distribution Characteristics of Acoustic Power.

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance-density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system.

Study on the Application of Fluorinated Polyimide in the Acidic Corrosion Protection of 3-nitro-1,2,4-trizole-5-one (NTO)-Based Explosive Formulations.

3-nitro-1,2,4-triazol-5-one (NTO) has been widely used as a kind of insensitive single-compound explosive owing to its excellent balance between safety and explosive energy. To reduce its possible acid corrosion and extend its application to insensitive ammunition, acid protection research on NTO-based explosives is significant. Traditionally, the acid protection effect was evaluated by metal corrosion, which is time-consuming and qualitative. An efficient and quantitative method is desirable for evaluating the acid protection effect and exploring novel protection materials. Herein, a polyimide of 4,4'-(hexafluoroisopropene)diphthalic anhydride (6FDA)/2,2-bis(trifluoromethyl)-4,4-diaminobiphenyl (TFMB) was synthesized by replacing the 4,4'-diaminodiphenyl ether (ODA) monomer with a TFMB monomer to act as an acid-protective coating material for NTO-based explosives. Compared with three other coating materials, polyvinylidene fluoride (PVDF), polyetherimide (PEI), and copolyimide (P84), the fluorinated polyimide exhibits the best acid protection effect. Moreover, a new method was constructed to obtain the pH time-dependent curve in order to evaluate efficiently the acid protection effect of the polymer materials. By the virtue of molecular dynamic simulation (Materials Studio 2023), the interfacial effects of the coating materials with NTO-based explosives were obtained. The study provides an interpretation of the acid protection effect on the molecular level, suggesting that the higher content of fluorine atoms is beneficial for stabilizing the active hydrogen atom of the NTO by forming intermolecular hydrogen bonds.

Damage Resistance of Kevlar® Fabric, UHMWPE, PVB Multilayers Subjected to Concentrated Drop-Weight Impact.

The impact resistance of layered polymer structures using polyvinyl butyral (PVB) in combination with Kevlar® fabric and ultra-high molecular weight polyethylene (UHMWPE) were fabricated and tested. Methods of wet impregnation and hot-press impregnation and consolidation of fabric with PVB and UHMWPE were used to manufacture multilayer constructs. All sandwich constructs were fixed to the surface of ballistic clay and subject to a free drop-weight test with a conical impactor having a small contact area. All tests were made at the same impact energy of 9.3 J and velocity of 2.85 m/s. The change in the resistance force was recorded using a piezoelectric force sensor at the time intervals of 40 µs. Using experimental force-time history, the change in the impactor's velocity, the depth of impactor penetration, the energy transformation at various stages of impactor interaction with the sample, and other parameters were obtained. Three indicators were considered as the main criteria for the effectiveness of a sample's resistance to impact: (1) minimum deformation, bulging, of the panel backside at the moment of impact, (2) minimum absorption of impact energy per areal density, and (3) minimal or, better yet, no destruction of structural integrity. Under the tested conditions, the rigid Kevlar-PVB-Kevlar sandwich at the frontside and relatively soft but flexible UHMWPE-Kevlar-UHMWPE layers in the middle helped to localize and absorb impact energy, while the backside Kevlar-PVB-Kevlar sandwich minimized local bulging providing the best overall performance. The front layer damage area was very shallow and less than two impactor tip diameters. The backside bulging was also less than in any other tested configurations.

Preparation and Evaluation of PVDF-HFP-Based Gel Electrolyte for Ge-Sensitized Thermal Cell.

The semiconductor-sensitized thermal cell (STC) is a new thermoelectric conversion technology. The development of nonliquid electrolytes is the top priority for the practical application of the STC. In this study, a novel gel polymer electrolyte (PH-based GPE) composed of poly(vinylidenefluoride-co-hexafluoropropylene) (PH), 1-Methyl-2-pyrrolidone (NMP), and Cu ions was synthesized and applied to the STC system. The PH-based GPE synthesized at 45 °C showed higher open-circuit voltage (-0.3 V), short-circuit current density (59 µA cm-2) and diffusion coefficient (7.82 × 10-12 m2 s-1), indicating that a well-balanced structure among the NMP molecules was formed to generate a high-efficiency conduction path of the Cu ions. Moreover, the ion diffusion lengths decreased with decreasing content rates of NMP for the PH-based GPEs, indicating that the NMP plays an important role in the diffusion of Cu ions. Furthermore, the activation energy was calculated to be 107 kJ mol-1, and that was smaller compared to 150 kJ mol-1 for the poly(ethylene glycol)-based liquid electrolyte. These results play an important reference role in the development of electrolytes for STC systems. At the same time, they also provide a new avenue and reference indicator for the synthesis of high-performance and safe GPEs.

Flash Pyrolysis of Waste Tires in an Entrained Flow Reactor-An Experimental Study.

In this study, a flash pyrolysis process is developed using an entrained flow reactor for recycling of waste tires. The flash pyrolysis system is tested for process stability and reproducibility of the products under similar operating conditions when operated continuously. The study is performed with two different feedstock materials, i.e., passenger car (PCT) and truck tire (TT) granulates, to understand the influence of feedstock on the yield and properties of the pyrolysis products. The different pyrolytic products i.e., pyrolytic carbon black (pCB), oil, and pyro-gas, are analyzed, and their key properties are discussed. The potential applications for the obtained pyrolytic products are discussed. Finally, a mass and energy balance analysis has been performed for the developed pyrolysis process. The study provides insight into the governing mechanisms of the flash pyrolysis process for waste tires, which is useful to optimize the process depending on the desired applications for the pyrolysis products, and also to scale up the pyrolysis process.

Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes.

Unravelling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. Individual physiology and metabolism are often studied to understand the response of individual animals to environmental change. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behaviour in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.

Modeling and design of solar + storage-powered community resilience hubs across California.

Distributed clean, reliable energy resources like solar plus battery storage (solar + storage) can reduce harmful emissions while supporting resilience. Solar + storage-powered resilience hubs provide energy for critical services during disasters while increasing human adaptive capacity year round. We studied where utility rates, local climate, and historical injustice make solar + storage resilience hubs more valuable and more challenging. We modeled the economic and climate impacts of outfitting candidate hub sites across California with solar + storage for everyday operations and identified designs and costs required to withstand a range of outages considering weather impacts on energy needs and availability. We integrated sociodemographic data to prioritize the siting of resilience hubs, to focus potential policy and funding priorities on regions where solar + storage for resilience hubs is hard or expensive, and where populations are most in need. We identified almost 20,000 candidate buildings with more than 8 GW of total rooftop solar potential capable of reducing CO2 emissions by 5 million tons per year while providing energy for community resilience. Hub capacity for one of the most challenging missions-providing emergency shelter during a power outage and smoke event-could have a statewide average lifetime cost of less than $2000 per seat. We identified regional challenges including insufficient rooftop solar capacity in cities, low sunlight in northern coastal California, and high costs driven by utility rate structures in Sacramento and the Imperial Valley. Results show that rates and net metering rules that incentivize solar + storage during everyday operations decrease resilience costs.

Molecular copper(I)-sensitized photoanodes for alcohol oxidation under ambient conditions.

Dye-sensitized photoelectrochemical cells can enable the production of molecules currently accessible through energetically demanding syntheses. Copper(I)-based dyes represent electronically tunable charge transfer and separation systems. Herein, we report a Cu(I)-bisdiimine donor-chromophore-acceptor dye with an absorbance in the visible part of the solar spectrum composed of a phenothiazine electron donor, and dipyrido[3,2-a:2',3'-c]phenazine electron acceptor. This complex is incorporated onto a zinc oxide nanowire semiconductor surface effectively forming a photoanode that is characterized spectroscopically and electrochemically. We investigate the photo-oxidation of hydroquinone, and the photosensitization of 2,2,6,6-tetramethylpiperidine-1-oxyl and N-hydroxyphthalimide for the oxidation of furfuryl alcohol to furfuraldehyde, resulting in near quantitative conversions, with poor selectivity to the alcohol.

Unlocking the benefit of active stretch: The Eccentric muscle action not the preload maximizes muscle-tendon unit stretch-shortening cycle performance.

Stretch-shortening cycles (SSCs) outperform shortening contractions preceded by isometric contractions in terms of enhanced force/torque, work, and power production during shortening. This so-called SSC-effect is presumably related to the active muscle stretch before shortening in SSCs. However, it remains unclear whether the stretch-induced higher preload level or stretch-induced history dependence effects maximize the SSC-effect. Therefore, we analyzed fascicle behavior, MTU shortening work, and torque/force (N=12 participants) via ultrasound and dynamometry during electrically stimulated submaximal plantar flexion contractions from 10° plantarflexion to 15° dorsiflexion. To elucidate the effects of preload level and preload modality (i.e. contraction type) on shortening performance, muscle-tendon unit shortening was preceded by fixed-end (SHO), active stretch (SSC), and preload-matched fixed-end (MATCHED) contractions. Prior to shortening MATCHED and SCC had the same preload level (1% torque difference), similar joint position and muscle fascicle lengths. Compared with SHO, shortening work was significantly (p<.001, partial η2=.749) increased by 85% and 55% for SSC and MATCHED, respectively, with SSC shortening work being significantly higher than MATCHED (p=.016). This indicates that preload contributes by 65% to the overall SSC-effect, so that 35 % need to be referred to stretched-induced history dependent mechanisms. Additionally, SSC showed larger fascicle forces at the end of shortening (p<.001) and 20% less depressed isometric torque following shortening compared with MATCHED (p<.001). Since potential decoupling effects by the series elastic element were controlled by matching the preload levels, we conclude that the difference between SSC and MATCHED is related to stretch-induced long-lasting history dependent effects.

Corrigendum: Applied insight: studying reducing the carbon footprint of the drying process and its environmental impact and financial return.

[This corrects the article DOI: 10.3389/fbioe.2024.1355133.].

High-Entropy Materials in Electrocatalysis: Understanding, Design, and Development.

Electrocatalysis is a crucial method for achieving global carbon neutrality, serving as an essential means of energy conversion, and electrocatalyst is crucial in the process of electrocatalysis. Because of the abundant active sites, the multi-component synergistic effect of high-entropy materials has a wide application prospect in the field of electrocatalysis. Moreover, due to the special structure of high-entropy materials, it is possible to obtain almost continuous adsorption energy distribution by regulating the composition, which has attracted extensive attention of researchers. This paper reviews the properties and types of high-entropy materials, including alloys and compounds. The synthesis strategies of high-entropy materials are systematically introduced, and the solid phase synthesis, liquid-phase synthesis, and gas-phase synthesis are classified and summarized. The application of high-entropy materials in electrocatalysis is summarized, and the promotion effect of high-entropy strategy in various catalytic reaction processes is summarized. Finally, the current progress of high-entropy materials, the problems encountered, and the future development direction are reviewed. It is emphasized that the strategy of high flux density functional theory calculation guiding high-entropy catalyst design will be of great significance to electrocatalysis.

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices.

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices (EESDs). However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of our developed tunable graphene-based electrodes with controllable structure, we successfully unite experiments with machine learning to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The results observed in this work also indicate the call for comprehensive performance data reporting in the electrochemical energy storage field to enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices. This article is protected by copyright. All rights reserved.