Estimated Energy Expenditure Research Articles

Novel DETA-Isobutanol biphasic solvent for post-combustion CO2 capture: High cyclic capacity and low energy consumption

In recent years, research into biphasic solvents has become an important direction more efficient control of CO2 emissions. However, most biphasic absorbents still face challenges such as low cyclic capacity and high viscosity of the rich phase. To develop a novel solvent with enhanced cyclic capacity and reduced regeneration energy consumption, a biphasic solvent consisting of DETA/Isobutanol/H2O was proposed. The results demonstrate excellent absorption and desorption performance of the DETA/Isobutanol/H2O biphasic solvent, with an absorption capacity ranging from 1.13 to 1.35 mol/mol and a cyclic capacity ranging from 0.61 to 0.84 mol/mol. After conducting 5 times absorption-desorption experiments, the cyclic capacity of DETA/Isobutanol/H2O biphasic solvent remained at 0.8 mol/mol. The estimated energy consumption for regeneration, under lean phase load, was calculated to be 2.42 GJ/t CO2, which was 42.24 % lower than that of 30wt% MEA aqueous solution (4.19 GJ/t CO2). The DETA/Isobutanol/H2O solution exhibits promising potential in terms of absorption-desorption performance and regenerative energy consumption, making it an exceptional CO2 absorbent.

Identifying and Assessing Physiological Biomarkers for Food and Energy Intake: A Systematic Review with Meta-Analysis Protocol

Background Traditional dietary assessments are often inaccurate and prone to self-reporting biases. Tracking the physiological responses associated with eating and digestion events via wearable technologies may provide an effective approach for continuously monitoring food intake and estimating energy consumption. Eating and digestion are accompanied by a series of changes in the heart rate, skin temperature, blood oxygen saturation, and blood pressure. These changes can be tracked by wearable devices, such as smartwatches, which have been widely accepted in the market. This systematic review is the first to evaluate the effectiveness of tracking such physiological biomarkers in differentiating between high- and low-calorie meals, potentially paving the way for more accurate dietary monitoring. Methods Following the PRISMA-P guidelines, we will conduct a systematic literature search through MEDLINE, EMBASE, and PubMed for clinical trials that investigated physiological responses following meal intake in healthy subjects. Two independent reviewers will screen and select articles based on pre-defined eligibility criteria, with a third review to resolve any discrepancies. This will be followed by data extraction and quality assessment of the included studies. Statistical analyses, including meta-analyses, will be performed using R Studio software. Our primary outcome will be the comparison of physiological biomarkers before and after meal intake, while secondary outcomes will include comparisons of physiological biomarkers between high- and low-calorie meal consumption and the correlation between the caloric content of consumed meals and postprandial physiological changes. Discussion This systematic review and meta-analysis will identify physiological indicators for eating events and inform the design of wearable sensors that estimate food intake in healthy subjects. Systematic Review Registration PROSPERO Registration ID: CRD42024544353

AI-Driven Route Planning and Scheduling for Electric School Buses

This review paper explores the current state, recent advancements, challenges, and future perspectives of AI-driven approaches for route planning and scheduling of Electric School Buses (ESBs). The integration of artificial intelligence into energy management systems for electric vehicles has gained significant attention, particularly in optimizing school transportation. This study examines various AI techniques, including genetic algorithms, reinforcement learning, and game-theoretic approaches, applied to ESB management. Key focus areas include energy consumption estimation, battery capacity optimization, and Vehicle-to-Grid (V2G) strategies. The paper also addresses critical challenges such as data integration, security concerns, and operational constraints. Future perspectives highlight the potential of advanced AI techniques, smart grid integration, and personalized transportation solutions. By synthesizing current research and identifying key areas for future development, this review contributes to ongoing efforts to improve the efficiency, sustainability, and overall quality of school transportation systems through AI technologies.

A Dual-Stream Fusion Network for Human Energy Expenditure Estimation with Wearable Sensor

With the increasing awareness of health, using wearable sensors to monitor individual activities and accurately estimate energy expenditure has become a current research focus. However, existing research encounters challenges including low estimation accuracy, a deficiency of frequency domain features, and difficulty in integrating time domain and frequency domain features. To address these issues, we propose an innovative framework called the Dual-Stream Fusion Network (DSFN). This framework combines the Time Domain Encoding (TDE) module, the Frequency Domain Hierarchical-Split Encoding (FDHSE) module, and a Two-Stage Feature Fusion (TSF) module. Specifically, the temporal stream of the framework employs the TDE module to capture deep temporal features that reflect the complex dynamic variations in time-series data. The frequency domain stream introduces the FDHSE module, which extracts frequency domain features using a multi-level, multi-scale approach, ensuring a comprehensive and diverse representation of frequency information. Through this dual-stream architecture, our model effectively learns both time and frequency domain features, addressing the limitations of frequency domain features observed in prior studies. Additionally, we propose the TSF module to fully integrate time and frequency domain features, effectively overcoming the challenge of fusing these two types of features. We conducted experiments on two public datasets, namely the GOTOV dataset (elderly people) and the JSI dataset (young people). Experimental results demonstrate that our method achieves excellent performance across different age groups. Compared to the baseline models, the proposed DSFN significantly improves the accuracy of human energy expenditure estimation.

A comparative study of vehicle powertrain efficiency: Data-driven analyzing energy consumption and environmental impact

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

Comparison of exercise training modalities and change in peak oxygen consumption in heart failure with preserved ejection fraction: a secondary analysis of the OptimEx-Clin trial.

Exercise training (ET) is an effective therapy in heart failure with preserved ejection fraction (HFpEF), but the influence of different ET characteristics is unclear. We aimed to evaluate the associations between ET frequency, duration, intensity [% heart rate reserve (%HRR)] and estimated energy expenditure (EEE) with the change in peak oxygen consumption (V̇O2) over 3 months of moderate continuous training (MCT, 5×/week) or high-intensity interval training (HIIT, 3×/week) in HFpEF. ET duration and heart rate (HR) were recorded with a smartphone application. EEE was calculated using the HR data during ET and the individual HR-V̇O2 relationships during cardiopulmonary exercise testing. Differences between groups and associations between ET characteristics and peak V̇O2 change were assessed with linear regression analyses. Peak V̇O2 improved by 9.2 ± 13.2% after MCT and 8.7 ± 15.9% after HIIT (P = 0.67). The average EEE of 1 HIIT session was equivalent to ∼1.42 MCT sessions and when adjusted for EEE, the mean difference between MCT and HIIT was -0.1% (P = 0.98). For both MCT and HIIT, peak V̇O2 change was positively associated with ET frequency (MCT: R2 = 0.103; HIIT: R2 = 0.149) and duration/week (MCT: R2 = 0.120; HIIT: R2 = 0.125; all P < 0.05). Average %HRR was negatively associated with peak V̇O2 change in MCT (R2 = 0.101; P = 0.034), whereas no significant association was found in HIIT (P = 0.234). Multiple regression analyses explained ∼1/3 of the variance in peak V̇O2 change. In HFpEF, isocaloric HIIT and MCT seem to be equally effective over 3 months. Within each mode, increasing ET frequency or duration/week may be more effective to improve peak V̇O2 than increasing ET intensity.

Innovative use of depth data to estimate energy intake and expenditure in Adélie penguins.

Energy governs species' life histories and pace of living, requiring individuals to make trade-offs. However, measuring energetic parameters in the wild is challenging, often resulting in data collected from heterogeneous sources. This complicates comprehensive analysis and hampers transferability within and across case studies. We present a novel framework, combining information obtained from eco-physiology and biologging techniques, to estimate both energy expended and acquired on 48 Adélie penguins (Pygoscelis adeliae) during the chick-rearing stage. We employ the machine learning algorithm random forest (RF) to predict accelerometry-derived metrics for feeding behaviour using depth data (our proxy for energy acquisition). We also build a time-activity model calibrated with doubly labelled water data to estimate energy expenditure. Using depth-derived time spent diving and amount of vertical movement in the sub-surface phase, we accurately predict energy expenditure (R2=0.68, RMSE=344.67). Movement metrics derived from the RF algorithm deployed on depth data were able to accurately (accuracy=0.82) detect the same feeding behaviour predicted from accelerometry. The RF predicted accelerometry-estimated time spent feeding more accurately (R2=0.81) compared to historical proxies like number of undulations (R2=0.51) or dive bottom duration (R2=0.31). The proposed framework is accurate, reliable, and simple to implement on data from biologging technology widely-used on marine species. It enables coupling energy intake and expenditure, which is crucial to further assess individual trade-offs. Our work allows us to revisit historical data, to study how long-term environmental changes affect animals' energetics.

Physiological Responses of the Green Shore Crab, Carcinus maenas, During Acute and Chronic Low Temperature Exposure.

The green shore crab (Carcinus maenas) is native to Western Europe but has spread around the globe and is described as one of the top 100 worst invasive species. On the east coast of North America, their northern-most limit is the island of Newfoundland, Canada, where they can experience water temperatures as low as -1 °C. We investigated the physiological responses of C. maenas to a temperature reduction regime as well as to long-term acclimation to temperatures representative of winter (2 °C) and summer (12 °C) in Newfoundland. Heart rate, oxygen consumption and estimated energy expenditure declined steadily with decreasing temperature, but a marked change was observed between 6 and 4 °C, with lowest levels recorded in 2 °C. After long-term acclimation to 2 °C there was a sustained reduction in physiological parameters. Even though these physiological parameters were very low in 2 °C, the crabs still exhibited intermittent activity. This supports the presence of a dormancy, rather than true torpor/hibernation below 5 °C, in which crabs will continue to actively move and feed, albeit much more slowly. The population in Newfoundland contains haplotypes from both the invasive northern and southern lineages, and they appear to retain a similar low temperature response compared with most other populations of green crab from both their native and expanded range.

Reversible Hydrogen Acceptor–Donor Enables Relay Mechanism for Nitrate‐to‐Ammonia Electrocatalysis

Electrocatalytic nitrate reduction is a crucial process for sustainable ammonia production. However, to maximize ammonia yield efficiency, this technology inevitably operates at the potentials more negative than 0 V vs. RHE, leading to high energy consumption and competitive hydrogen evolution. To eradicate this issue, hydrogen tungsten bronze (HxWO3) as reversible hydrogen donor‐acceptor is partnered with copper (Cu) to enable a relay mechanism at potentials positive than 0 V vs. RHE, which involves rapid intercalation of H into HxWO3 lattice, prompt de‐intercalation of the lattice H and transfer onto Cu, and spontaneous H‐mediated nitrate‐to‐ammonia conversion on Cu. The resulting catalysts demonstrated a high ammonia yield rate of 3332.9±34.1 mmol gcat−1 h−1 and a Faraday efficiency of ~100 % at 0.10 V vs. RHE, displaying a record‐low estimated energy consumption of 17.6 kWh kgammonia−1. Using these catalysts, we achieve continuous ammonia production in an enlarged flow cell at a real energy consumption of 17.0 kWh kgammonia−1.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Comparing cadence-based energy expenditure and oxygen cost indices in able-bodied male people.

Walking is a fundamental aspect of daily life and exercise, with clinical benefits for cardiovascular health and muscle strength. However, accurately measuring energy efficiency during walking poses challenges due to equipment and spatial constraints. In this study, we proposed the cadence-based energy expenditure index (cEEI) and analyzed its correlation with the previously proposed index for measuring energy expenditure under various gait conditions. We enrolled 15 healthy participants and conducted an experimental protocol on a treadmill to measure the following energy expenditure-related indices: oxygen cost index (OCI), energy expenditure index (EEI), and cEEI. The participants underwent stages of walking at different speeds and inclinations that comply with the modified Bruce protocol while their heart rate, oxygen uptake, and cadence were recorded. Participants showed significant increases in heart rate, oxygen uptake, and cadence with higher walking speeds and inclinations. Correlation analysis revealed strong associations between cEEI and OCI, especially during walking conditions. Bland-Altman plots and interclass correlation coefficient analysis demonstrated a favorable agreement between cEEI and OCI, outperforming EEI. In conclusion, this study proposes cEEI as a reliable metric for estimating energy expenditure during walking by proving a strong correlation and agreement with OCI across various gait conditions. This suggests the potential for cEEI to provide real-time, individualized feedback on energy expenditure during walking, facilitating more personalized exercise prescriptions.

Modulators of Energy Expenditure Accuracy in Adults with Overweight or Obesity: E-MECHANIC Secondary Analyses.

American College of Sports Medicine (ACSM) metabolic equations are used to estimate energy expenditure (EE) of physical activity and prescribe aerobic exercise to meet EE requirements. Limited evidence supports their accuracy in sedentary adults with overweight or obesity during controlled exercise interventions. The purpose of this study was to compare EE estimated by the ACSM walking equation versus EE measured by indirect calorimetry during a 24-week aerobic exercise intervention, and identify potential modulators for their accuracy. Data from the exercising groups (8 or 20 kcal·kg body weight-1·week-1) of the E-MECHANIC study were utilized in this ancillary analysis (N = 103). Every 2 weeks for the initial 8 weeks and monthly thereafter, EE was measured via indirect calorimetry during absolute (2 mph, 0% grade) and relative (65-85% VO2peak) workload exercise. Resting metabolic rate, VO2peak, and body composition were assessed at baseline and follow-up. An EE offset factor (EOF) was calculated to express measured EE as a percentage of the estimated EE at each workload (EOF < 100% represents an overestimation of ACSM estimated EE). The accuracy of the equation decreased with increasing exercise workload (0.44%, 9.2%, and 20.3% overestimation at absolute, relative, and maximal workloads, respectively, at baseline) and overestimation of EE was greater after the exercise intervention. Furthermore, race, sex, age, fat mass, and VO2peak were identified as modulators for equation accuracy. Greater overestimation of EE was observed in Black compared to white females, particularly at lower exercise workloads. These findings support future efforts to improve the accuracy of metabolic equations, especially in diverse populations. Researchers should account for exercise efficiency adaptations when using metabolic equations to prescribe exercise precisely.

24-h Energy Expenditure Estimation From Accelerometry Assessments: A Cross-validation At Low Levels Of Physical Activity

24-h Energy Expenditure Estimation From Accelerometry Assessments: A Cross-validation At Low Levels Of Physical Activity

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

Predictive Artificial Intelligence Models for Energy Efficiency in Hybrid and Electric Vehicles: Analysis for Enna, Sicily

Developments in artificial intelligence techniques allow for an improvement in sustainable mobility strategies with particular reference to energy consumption estimates of electric vehicles (EVs). This research proposes a vehicle energy model developed on the basis of deep neural network (DNN) technology. This study also explores the potential application of the model developed for the movement data of new vehicles in the province of Enna, Sicily, Italy, which are characterized by numerous attractors and the increasing number of hybrid and electric cars circulating. The energy model for electric vehicles shows high accuracy and versatility, requiring vehicle velocity and acceleration as input data to predict energy consumption. This research article also provides recommendations for the energy modeling of electric vehicles and outlines additional steps for model development. The implemented methodological approach and its results can be used by transport decision-makers to plan new transport policies in Italian cities aimed at optimizing vehicle charging infrastructure. They can also help vehicle users accurately estimate energy consumption, generate maps, and identify locations with the highest energy consumption.

Time-energy budgets outperform dynamic body acceleration in predicting daily energy expenditure in kittiwakes, and estimate a very low cost of gliding flight relative to flapping flight.

Energy is a common currency for any living organism, yet estimating energy expenditure in wild animals is challenging. Accelerometers are commonly used to estimate energy expenditure, via a dynamic body acceleration (DBA) or time-energy budget approach. The dynamic body acceleration approach estimates energy expenditure directly from acceleration but may lead to erroneous estimates during inactivity when acceleration is zero but energy expenditure is not. The time-energy budget approach uses accelerometers and other data streams to assign a behaviour to each time step, and then calculates energy expenditure based on activity-specific metabolic rates assigned to each behaviour. Here, used GPS-accelerometry in breeding black-legged kittiwakes (Rissa tridactyla, n=80) to calculate DBA and time-energy budgets derived from simple biologging metrics (speed, wing beat frequency, GPS position). We then compared these two approaches to estimates of energy expenditure from doubly-labelled water. Energy expenditure estimated from DLW correlated with DBA, but the best model to estimate energy expenditure was based on time-energy budgets. Energy costs of flapping flight were higher than all other kittiwake behaviours (5.54 x basal metabolic rate, BMR). Energetic costs of gliding flight (0.80 x BMR) were the lowest of all behaviours, and equivalent to the cost of resting at the colony. DEE for our birds estimated from our calibration coefficients are similar to DEE for our birds estimated with the model coefficient published using different methods. We conclude that once calibrated with DLW, GPS-accelerometry provides a simple method for measuring energy expenditure in wild kittiwakes based on time-energy budgets.

Data-driven energy consumption prediction of a university office building using machine learning algorithms

Redundant consumption of energy in buildings is an important issue that causes increasing problems of climate change and global warming in the world. Therefore, it is necessary to develop efficient energy management approaches in buildings. Accurate prediction of energy consumption plays an important role to obtain energy-efficient buildings. Data-driven methods gained attention for estimation of energy consumption in buildings which would provide more accurate prediction results. In this study, hourly energy consumption prediction is performed on a university office building to increase energy efficiency in the building using machine learning algorithms. A new parameter is proposed, air conditioning demand, to improve accuracy of the algorithms. Moreover, temporal parameters, i.e. day of week, month of year, and hour of day, were used along with meteorological parameters to improve prediction performance of the algorithms. Experimental results show that hourly energy consumption of the building could be predicted using machine learning algorithms with high performance. When the results were analysed, Deep Neural Network (DNN) achieved better performance among other alternative algorithms. The average values of R2, RMSE and MAPE for DNN were 0.959, 4.796 kWh, and 5.738 %, respectively. Also, the addition of proposed air conditioning demand parameter provided improved performance to the algorithms.

Validity of a wrist-worn consumer-grade wearable for estimating energy expenditure, sedentary behaviour, and physical activity in manual wheelchair users with spinal cord injury

Purpose To evaluate the validity of a consumer-grade wearable for estimating energy expenditure, sedentary behaviour, and physical activity in manual wheelchair users with spinal cord injury (SCI). Materials and methods Fifteen manual wheelchair users with SCI (C5-L1, four female) completed activities of daily living and wheelchair propulsion (2–8 km·h−1). Wrist-worn accelerometry data were collected using consumer-grade (z-Track) and research-grade (ActiGraph GT9X) devices. Energy expenditure was measured via indirect calorimetry. Linear regression was used to evaluate the prediction of criterion metabolic equivalent of task (MET) by each accelerometer’s vector magnitude (VM). Area under the receiver operating characteristic curve (ROC-AUC) evaluated the accuracy of VM for discriminating between physical activity intensities and for identifying accelerometer cut-points. Results Standardised β-coefficients for the association between z-Track and ActiGraph VM for criterion MET were 0.791 (p < 0.001) and 0.774 (p < 0.001), respectively. The z-Track had excellent accuracy for classifying time in sedentary behaviour (ROC-AUC = 0.95) and moderate-to-vigorous physical activity (ROC-AUC = 0.93); similar values to the ActiGraph (ROC-AUC = 0.96 and 0.88, respectively). Cut-points for the z-Track were ≤37 g·min−1 for sedentary behaviour and ≥222 g·min−1 for moderate-to-vigorous physical activity. Conclusions This study supports the validity of a consumer-grade wearable to measure sedentary time and physical activity in manual wheelchair users with SCI.

A Cross-Layer Approach to Analyzing Energy Consumption and Lifetime of a Wireless Sensor Node

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

Improved Nitrate-to-Ammonia Electrocatalysis through Hydrogen Poisoning Effects.

Electrochemical conversion from nitrate to ammonia is a key step in sustainable ammonia production. However, it suffers from low productive efficiency or high energy consumption due to a lack of desired electrocatalysts. Here we report nickel cobalt phosphide (NiCoP) catalysts for nitrate-to-ammonia electrocatalysis that display a record-high catalytic current density of -702±7 mA cm-2, ammonia production rate of 5415±26 mmol gcat-1 h-1 and Faraday efficiency of 99.7±0.2 % at -0.3 V vs. RHE, affording the estimated energy consumption as low as 22.7 kWh kgammonia-1. Theoretical and experimental results reveal that these catalysts benefit from hydrogen poisoning effects under low overpotentials, which leave behind catalytically inert adsorbed hydrogen species (HI*) at Co-hollow sites and thereupon enable ideally reactive HII* at secondary Co-P sites. The dimerization between HI* and HII* for H2 evolution is blocked due to the catalytic inertia of HI* thereby the HII* drives nitrate hydrogenation timely. With these catalysts, the continuous ammonia production is further shown in an electrolyser with a real energy consumption of 18.9 kWh kgammonia-1.