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Addressing the challenge of sluggish kinetics and limited stability in alkaline oxygen evolution reactions, recent exploration of novel electrochemical catalysts offers improved prospects. To expedite the assessment of these catalysts, a half-cell rotating disk electrode is often favored for its simplicity. However, the actual catalyst performance strongly depends on the fabricated catalyst layers, which encounter mass transport overpotentials. We systematically investigate the role and sequence of electrode drop-casting methods onto a glassy carbon electrode regarding the efficiency of the oxygen evolution reaction. The catalyst layer without Nafion experiences nearly 50% activity loss post stability test, while those with Nafion exhibit less than 5% activity loss. Additionally, the sequence of application of the catalyst and Nafion also shows a significant effect on catalyst stability. The catalyst activity increases by roughly 20% after the stability test when the catalyst layer is coated first with an ionomer layer, followed by drop-casting the catalysts. Based on the half-cell results, the Nafion ionomer not only acts as a binder in the catalyst layer but also enhances the interfacial interaction between the catalyst and electrolyte, promoting performance and stability. This study provides new insights into the efficient and accurate evaluation of electrocatalyst performance and stability as well as the role of Nafion ionomer in the catalyst layer.

Universal access to electricity is beneficial for the socio-economic development of a country and the development of smart communities. Unfortunately, the electrification of remote off-grid areas, especially in developing countries, is rather slow due to geographic and economic barriers. In the Philippines, specifically, many electrified off-grid areas are underserved, with access to electricity being limited to only a few hours a day. This is mainly due to the high dependence on diesel power plants (DPPs) for electrifying these areas. To address these problems, hybrid renewable energy systems (HRESs) have been considered good electrification alternatives and have been extensively studied for their techno-economic and financial feasibility for Philippine off-grid islands. In this work, articles published from 2012 to 2023 focusing on off-grid Philippine rural electrification were reviewed and classified based on their topic. The taxonomical analysis of collected studies shows that there is a saturation of works focusing on the technical and economic aspects of off-grid electrification. Meanwhile, studies focusing on environmental and socio-political factors affecting HRES off-grid electrification are lagging. A bibliographic analysis of the reviewed articles also showed that there is still a lack of a holistic approach in studying off-grid electrification in the Philippines. There are only a few works that extend beyond the typical techno-economic study. Research works focusing on environmental and socio-political factors are also mainly isolated and do not cross over with technical papers. The gap between topic clusters should be addressed in future works on off-grid electrification.

Hybrid renewable energy systems have garnered considerable attention as sustainable power sources for remote off-grid islands in the Philippines. Consequently, they have been the subject of numerous techno-economic investigations. However, comprehensive explorations into the viability of 100 % renewable energy (RE) systems for these areas have been limited due to their exorbitant initial outlays. In this work, we modelled the prospective transition of off-grid island mini-grids in the Philippines from the contemporary status quo in 2020 to a fully integrated 100 % RE system by 2050. Our approach involves the gradual shift towards RE adoption instead of the abrupt deployment of RE systems. This maneuver serves a twofold purpose: firstly, it mitigates the adverse economic repercussions stemming from the substantial upfront costs inherent to RE technologies; secondly, it leverages the declining future costs of these technologies. We collected an energy generation and consumption dataset across 208 mini-grids in Philippine off-grid islands. We then simulate the RE transition based on prevailing technology costs at five-year intervals from 2020 to 2050. Afterwards, different scenarios that may affect the energy transition are simulated. Our results indicate that an energy transition steered solely by market dynamics cannot achieve a 100 % RE penetration; hence, we analysed alternative scenarios promoting RE utilization. The immediate discontinuation of all diesel generators by 2050 will lead to a substantial cost uptick at the end of the transition. Integrating biodiesel yields a more measured progression of costs, although this relies upon a nascent market, rendering it susceptible to feedstock supply risks. Recognizing the intermittent nature of RE technologies, we posit that allowing for a certain degree of unmet load fosters greater RE penetration. Nevertheless, this approach compromises the reliability of the system. Our work demonstrates that a 100 % RE transition in Philippine off-grid islands is technically and economically feasible. However, the energy trilemma or the tradeoff between affordability, reliability, and sustainability encumbers the realization of this transition.

There are few studies focused on the epidemiology of hypertensive crisis at the population level in resource-constrained settings. This study aimed to determine the prevalence and trends over time of hypertensive crisis, as well as the factors associated with this condition among adults. A secondary data analysis was carried out using the Peruvian Demographic and Family Health Survey (ENDES). Hypertensive crisis was defined based on the presence of systolic (≥ 180mmHg) or diastolic (≥ 110mmHg) blood pressure, regardless of previous diagnosis or medication use. The factors associated with our outcome were evaluated using multinomial logistic regression, and the trend of hypertensive crisis was evaluated using the Cochrane-Armitage test. Data from 260,167 participants were analyzed, with a mean age of 44.2 (SD: 16.9) years and 55.5% were women. Hypertension prevalence was 23% (95%CI: 22.7-23.4) and, among them, 5.7% (95%CI: 5.4-5.9) had hypertensive crisis, with an overall prevalence of 1.5% (95%CI: 1.4-1.6). From 2014 to 2022, a significant decrease in the prevalence of hypertensive crisis was observed, from 1.7% in 2014 to 1.4% in 2022 (p = 0.001). In the multivariable model, males, increasing age, living in urban areas, high body mass index, and self-reported type 2 diabetes were positively associated with hypertensive crisis, whereas higher educational level, socioeconomic status, and high altitude were inversely associated. There is a need to improve strategies for the diagnosis, treatment, and control of hypertension, especially hypertensive crisis.

Many people around the world rely on groundwater for drinking and sanitation, however, they are exposed to various health risks from the naturally occurring groundwater arsenic (As). Air-cathode Assisted Iron Electrocoagulation (ACAIE) using Carbon Black Pearls 2000® cathode was previously shown catalyse the removal of groundwater As by producing hydrogen peroxide (H2O2). This work explored Vulcan® XC-72, and Printex® L6 Carbon as alternative cathodes for iron electrocoagulation which are more selective towards the 2-electron oxygen reduction reaction into H2O2 compared to Carbon Black Pearls®. The cathodes were tested in an ACAIE set-up to treat synthetic groundwater spiked with 1,500 μg/L of As at different charge dosage rates (CDR) from 1.56 C/L-min to 100 C/L-min with a total charge dosage of 600 C/L for all set-ups. Although the electrocoagulation energies among the cathodes were similar, the use of Printex® cathode for ACAIE remediated the groundwater for all CDR with final As levels below 10 μg/L. This is in contrast with the less selective Carbon Black Pearls® at low CDR, and the less active Vulcan® Carbon at high CDR where the treated groundwater may still have As levels above 10 μg/L. Future research would explore modifications in the carbon materials and reactor configuration to further optimize ACAIE in removing groundwater As.

The increasing demand for batteries’ application in grid-balancing, electric vehicles, and portable electronics has prompted research efforts on improving their performance and safety features. The improvement of batteries involves the comparison of multiple battery designs and the determination of electrochemical and thermal property distributions at the continuum scale. This is achieved by using multiphysics modeling for investigatory battery research, as conventional experimental approaches would be costly and impractical. The fundamental electrochemical models for these batteries have been established, hence, new models are being developed for specific applications, such as thermal runaway and battery degradation in lithium-ion batteries, gas evolution in lead-acid batteries, and vanadium crossover in vanadium redox flow batteries. The inclusion of new concepts in multiphysics modeling, however, necessitates the consideration of phenomena beyond the continuum scale. This work presents a comprehensive review on the multiphysics models of lithium-ion, lead-acid, and vanadium redox flow batteries. The electrochemical models of these chemistries are discussed along with their physical interpretations and common applications. Modifications of these multiphysics models for adaptation and matching to end applications are outlined. Lastly, we comment on the direction of future work with regards to the interaction of multiphysics modeling with modeling techniques in other length and time scales. Molecular-scale models such as density functional theory and kinetic Monte Carlo can be used to create new multiphysics models and predict transport property correlations from first principles. Nanostructures and pore-level geometries can be optimized and integrated into continuum-scale models. The reduction of multiphysics models via machine learning, mathematical simplification, or regression enables their application in battery management systems and energy systems modeling.

Hybrid renewable energy systems (HRES) have emerged as a promising solution for delivering sustainable energy to off-grid communities. However, the vulnerability of specific regions to extreme weather events has raised concerns about the resilience of these systems. This study undertakes a techno-economic analysis to assess the impact and significance of incorporating storm hardening measures and insurance into an HRES designed for the Busuanga island cluster. Central to our study is introducing a novel cost metric, the probability-averaged levelized cost of electricity (LCOE), which adeptly captures the inherently probabilistic nature of climate-induced damages to HRES energy assets. This metric serves as the linchpin for comparing the economic viability of HRES configurations, considering both scenarios with and without storm hardening or insurance. Our findings unveil a clear trend: for a solar photovoltaic (PV) panel with an annual probability of damage at 1%, insurance emerges as a financially prudent choice, while storm hardening gains merit at a probability of 4%. The weighted average cost of capital (WACC) is pivotal in shaping investment decisions. HRES setups featuring non-hardened solar PV panels become more economically appealing than their insured or hardened counterparts under higher WACC conditions, under the condition that the solar PV panels can maintain functionality for 15 years without impairment. Our study demonstrates the importance of accounting for often overlooked factors such as storm hardening and insurance premiums for solar PV panels in climate-vulnerable areas, which are commonly disregarded in many techno-economic studies. Moreover, our findings and conclusions on the optimal balance between capital costs, insurance premiums, and storm-hardening markups can readily extend to other climate-vulnerable areas.

Density functional theory was used to investigate the effects of doping alkaline earth metal atoms (beryllium, magnesium, calcium and strontium) on graphene. Electron transfer from the dopant atom to the graphene substrate was observed and was further probed by a combined electron localization function/non-covalent interaction (ELF/NCI) approach. This approach demonstrates that predominantly ionic bonding occurs between the alkaline earth dopants and the substrate, with beryllium doping having a variant characteristic as a consequence of electronegativity equalization attributed to its lower atomic number relative to carbon. The ionic bonding induces spin-polarized electronic structures and lower workfunctions for Mg-, Ca-, and Sr-doped graphene systems as compared to the pristine graphene. However, due to its variant bonding characteristic, Be-doped graphene exhibits non-spin-polarized p-type semiconductor behavior, which is consistent with previous works, and an increase in workfunction relative to pristine graphene. Dirac half-metal-like behavior was predicted for magnesium doped graphene while calcium doped and strontium doped graphene were predicted to have bipolar magnetic semiconductor behavior. These changes in the electronic and magnetic properties of alkaline earth doped graphene may be of importance for spintronic and other electronic device applications.

In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids.

Designing and manufacturing transformers often involves variations in heights and thicknesses of windings. However, such geometric asymmetry introduces a significant impact on the magnitude of stray transformer losses. This study examines the effects of asymmetric coils on the generation of stray losses within core clamps and transformer tank walls. A model has been introduced to ascertain the dispersion magnetic field’s value at a specific distance from the coil. The analysis extends to characterising the dispersion magnetic field reaching the tank walls by using electromagnetic simulation by a finite element method. It explores strategies to diminish stray losses, including the placement of magnetic shunts as protective shields for the tank walls. It delves into the efficacy of employing a transformer shell-type configuration to mitigate the magnetic dispersion field. The findings revealed that achieving greater symmetry in transformer coils can minimise stray losses. Specifically, the incorporation of magnetic shunts has the potential to reduce additional losses by 40%, while the adoption of a shell-type configuration alone can lead to a 14% reduction. This work provides valuable insights into optimising transformer designs, contributes a user-friendly tool for estimating additional tank losses, thereby enhancing the knowledge base for transformer manufacturers.

An increasing acceptance of microgrid systems is primarily driven by emission reduction, resilience, reliability, and stability of energy systems. This work proposes a utility-scale grid-connected microgrid generation and network planning for a distribution network based on its available local resources and potential for distributed energy resource (DER) generator installation. Enabling distribution utilities to operate as a microgrid allows their systems to function as stand-alone in worst-case transmission grid scenarios. The main steps include identifying planning objectives, generation planning, and network planning. Results of a case study in Kalinga, Philippines show that the distribution utility can lower the levelized cost of electricity (LCOE) by 0.032 USD/kWh (21.38 %) by adding additional renewable energy capacities into the mix to meet its load demand. Results also show resilience and stability improvements with microgrid operation. Additional requirements for the existing distribution network to operate as a microgrid include additional network switches and line reinforcement upgrades.

As climate change is slowly materialising, the importance of alternative fuels has been magnified to aid in reducing carbon emissions. One of the most promising clean alternative fuels is green hydrogen which is produced via water electrolysis powered by renewable energy sources. The solar and wind power potential of the Philippines has been explored in published studies; however, the economic viability of green hydrogen production has not yet been evaluated. In this work, an assessment of the levelized cost of hydrogen (LCOH) produced from solar and wind power in the Philippines was performed. Representative locations within a province were selected based on the annual average resource availability for solar global horizontal irradiance (GHI) and wind speed. LCOH calculations identified the favourable locations for green H2 production by calculating the net present cost (NPC) of solar- and wind-powered electrolysis plants with deionised water as feedstock. Uncertainty analysis was performed via Monte Carlo simulations by perturbing component costs, feedstock water price, and discount rate. Additionally, LCOH calculations and Monte Carlo simulations showed the disparity of resource availability between solar and wind in the Philippines – supported by the calculated capacity factors. It was also shown that favourable conditions such as high solar GHI and high wind speed tend to have a narrower distribution and lower LCOH values – implying less economic risks in developing over these locations. Calculations identified that policy instruments such as subsidies and tax exemptions are still vital in commissioning green hydrogen plants, as LCOH values are generally higher than the cost of grey hydrogen.

The continued increase in national energy demand pushes oil and petroleum price prediction efforts for the net oil-importing Philippines to ensure adequate supply. These prices are commonly modeled by data-driven Machine Learning (ML) methods to encompass their extrinsic and volatile nature. However, recent studies have found that new features specific to COVID-19 and the Russia-Ukraine geopolitical conflict have significantly contributed to overseas oil price ML prediction. This work investigated the impact of these factors on seven pump prices in Manila, Philippines, from December 2019 until July 2022. Three ML regression models were chosen to extend the existing literature and ensure price model accuracy: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Random Forest Regression (RFR). Features were listed based on related literature and underwent data preprocessing using p-value testing and Principal Component Analysis. Models were then trained, tested, and optimized using nested splits and hyperparameter tuning. Mean Absolute Percent Error (MAPE) was used to evaluate accuracy. Generated models had MAPE values within the range 3.13 % - 12.67 %, which is within the range of MAPE values in oil and petroleum price prediction literature, 0.131 % - 19.2 %. MLR and SVR models generally exhibited the highest accuracy for each pump price. This study proves that period-specific features may be used for local pump price modeling. Future works may explore other ML models and geographic location effects and investigate newly identified period-specific features.

With the growing popularity of electric vehicles, longer lasting batteries are necessary for better mileage. Battery chemistries with larger energy densities compared to Lithium-Ion Batteries (LIB) need to be developed. Lithium-Sulfur Battery (LSB) is a good alternative due to its high theoretical and practical energy densities. However, commercialization of LSBs has yet to be realized due to shorter lifetimes caused by non-ideal battery processes such as polysulfide shuttling. Through multiphysics modeling, the effects of shuttling can be observed. Additionally, cost analysis can determine the feasibility of LSBs for mass production and its competitiveness against LIBs. In this work, energy density optimization and LSB cost estimation were done using combined multiphysics modeling and cost modeling approaches. Using COMSOL Multiphysics®, the energy densities of the batteries with and without shuttling were optimized by changing the thickness and porosity of the cathode and separator. Then, a bottom-up cost analysis of the optimized battery cell was conducted using the BatPaC model. It was observed that shuttling leads to a lower optimized energy density. To compensate for the shuttling-induced capacity loss, the model’s optimized battery dimensions had a separator porosity increase by 22.08 % and a cathode thickness and porosity decrease by 23.72 % and 16.21 %. Additionally, shuttling increases battery material costs by 13.35 % because of the larger and more expensive current collectors must compensate for the size of the smaller electrodes. Future works may explore various C-rates and use cases to identify optimal battery parameters for each.

Abstract The feeding habits of Chironomidae in hill streams in the pampean region have not been described. In this study, we analyzed the gut contents of this insect family with an aim at establishing their diet in an intermittent stream in the Ventania-Hill system. We sampled three sites with different habitat characteristics (i. e., altitude, substrate type, and current velocity). Of the total of nine taxa were recorded, the only one present at all sites was Corynoneura sp. 1. The food items found were detritus, diatoms, filamentous chlorophytes, euglenophytes, vascular plants, fungi, and animal remains. Most of the taxa could be classified as gathering collectors, with those having a higher proportion of detritus in the gut contents although the guts of Pentaneura nr. cinerea, Cricotopus sp. 1, and Onconeura analiae included high proportions of animal remains. No differences in the feeding habits were attributable to the location in the stream. This research contributes to our understanding of the trophic habits of Chironomidae in an intermittent hill stream system of South America.

Abstract Background Recent reports have suggested that air pollution may impact thyroid function, although the evidence is still scarce and inconclusive. In this study we evaluated the association of exposure to air pollutants to thyroid function parameters in a nationwide sample representative of the adult population of Spain. Methods The Di@bet.es study is a national, cross-sectional, population-based survey which was conducted in 2008-2010 using a random cluster sampling of the Spanish population. The present analyses included 3859 individuals, without a previous thyroid disease diagnosis, and with negative thyroid peroxidase antibodies (TPO Abs) and thyroid-stimulating hormone (TSH) levels of 0.1-20 mIU/L. Participants were assigned air pollution concentrations for particulate matter

Traditional understanding of electrocatalytic reactions generally focuses on either covalent interactions between adsorbates and the reaction interface (i.e., electrical double layer, EDL) or electrostatic interactions between electrolyte ions. Here, our work provides valuable insights into interfacial structure and ionic interactions during alkaline oxygen evolution reaction (OER). The importance of inner-sphere OH- adsorption is demonstrated as the IrOx activity in 4.0 M KOH is 6.5 times higher than that in 0.1 M KOH. Adding NaNO3 as a supporting electrolyte, which is found to be inert for long-term stability, complicates the electrocatalytic reaction in a half cell. The nonspecially adsorbed Na+ in the outer compact interfacial layer is suggested to form a stronger noncovalent interaction with OH- through hydrogen bond than adsorbed K+, leading to the decrease of interfacial OH- mobility. This hypothesis highlights the importance of outer-sphere adsorption for the OER, which is generally recognized as a pure inner-sphere process. Meanwhile, based on our experimental observations, the pseudocapacitive behavior of solid-state redox might be more reliable in quantifying active sites for OER than that measured from the conventional EDL charging capacitive process. The interfacial oxygen transport is observed to improve with increasing electrolyte conductivity, ascribing to the increased accessible active sites. The durability results in a liquid alkaline electrolyzer which shows that adding NaNO3 into KOH solution leads to additional degradation of OER activity and long-term stability. These findings provide an improved understanding of the mechanistic details and structural motifs required for efficient and robust electrocatalysis.

Nitric oxide (NO) is a proangiogenic factor acting through the soluble guanylate cyclase (sGC) pathway. However, angiogenic growth increases energy demand, which may be hampered by NO inhibition of cytochrome c oxidase (CcO). Then, NO activity would be the balanced result of sGC activation (pro-angiogenic) and CcO inhibition (anti-angiogenic). NO activity in a rat and eNOS−/− mice aortic ring angiogenic model and in a tube formation assay (human aortic endothelial cells) were analyzed in parallel with mitochondrial O2 consumption. Studies were performed with NO donor (DETA-NO), sGC inhibitor (ODQ), and NOS or nNOS inhibitors (L-NAME or SMTC, respectively). Experiments were performed under different O2 concentrations (0–21%). Key findings were: (i) eNOS-derived NO inhibits angiogenic growth by a mechanism independent on sGC pathway and related to inhibition of mitochondrial O2 consumption; (ii) NO inhibition of the angiogenic growth is more evident in hypoxic vessels; (iii) in the absence of eNOS-derived NO, the modulation of angiogenic growth, related to hypoxia, disappears. Therefore, NO, but not lower O2 levels, decreases the angiogenic response in hypoxia through competitive inhibition of CcO. This anti-angiogenic activity could be a promising target to impair pathological angiogenesis in hypoxic conditions, as it occurs in tumors or ischemic diseases.

Abstract The aim of this study is to evaluate the preemptive analgesic effects of dexamethasone (DEX) alone or combined with non-steroidal anti-inflammatory drugs (NSAIDs) in third molar surgeries. The subjects were divided into five groups (n = 20 teeth/group); subjects received only 8 mg of dexamethasone 1 h before the surgical procedure (DEX group), or in combination with etodolac (DEX + ETO), ketorolac (DEX + KET), ibuprofen (DEX + IBU), loxoprofen (DEX + LOX). Paracetamol 750 mg was provided as the number of rescue analgesics (NRA). Salivary PGE2 expression was measured preoperatively and at 48 h. Edema and Maximum mouth opening (MMO) were measured postoperatively at 48 h and 7 days. A visual analog scale (VAS) was performed postoperatively at 6, 12, 24, 48, 72 h, and 7 days. Salivary expression of PGE2 showed a decrease only for the DEX group. Edema and MMO and NRA consumption showed no significant differences among the groups (P > 0.05). The VAS showed a significantly lower pain perception at 6 h after the surgery for the DEX + ETO and DEX + KET groups (P

This data article contains the location, energy consumption, renewable energy potential, techno-economics, and profitability of hybrid renewable energy systems (HRES) in 634 Philippine off-grid islands. The HRES under consideration consists of solar photovoltaics, wind turbines, lithium-ion batteries, and diesel generators. The islands were identified from Google Maps™, Bing Maps™, and the study of Meschede and Ocon et al. (2019) [1]. The peak loads of these islands were acquired from National Power Corporation – Small Power Utilities Group (NPC-SPUG), if available, or estimated from the island population otherwise. Hourly-resolution load profiles were synthesized using the normalized profiles reported by Bertheau and Blechinger (2018) [2]. Existing diesel generators in the islands were compiled from reports by NPC-SPUG, while monthly average global horizontal irradiance and wind speeds were taken from the Phil-LIDAR 2 database. Islands that are electrically interconnected were lumped into one microgrid, so the 634 islands were grouped into 616 microgrids. The HRES were optimized using Island System LCOEmin Algorithm (ISLA), our in-house energy systems modeling tool, which sized the energy components to minimize the net present cost. The component sizes and corresponding techno-economic metrics of the optimized HRES in each microgrid are included in the dataset. In addition, the net present value, internal rate of return, payback period, and subsidy requirements of the microgrid are reported at five different electricity rates. This data is valuable for researchers, policymakers, and stakeholders who are working to provide sustainable energy access to off-grid communities. A comprehensive analysis of the data can be found in our article “Techno-economic and Financial Analyses of Hybrid Renewable Energy System Microgrids in 634 Philippine Off-grid Islands: Policy Implications on Public Subsidies and Private Investments” [3].

In this work, we propose an immersion corrosion measurement method with E10 bioethanol-gasoline blend on austenitic steel (SS304) and aluminum (Al6061). Immersion tests were designed to simulate a weekly refuel cycle and were performed over 4, 8, and 12-week durations. Polarization resistance, surface morphology, and corrosion products were then observed after the immersion tests. Corrosion rates were estimated from polarization resistance measurements via electrochemical impedance spectroscopy. Afterwards, the surface morphology of exposed samples was studied using scanning electron microscopy. Lastly, the corrosion products were characterized using scanning electron microscopy with energy dispersive X-ray analysis. Surface staining was observed in both metal substrates. It was observed that the corrosion rate of SS304 remained relatively constant throughout the immersion time. In contrast, an increment in the corrosion rate of Al6061 was observed after four weeks of immersion due to the peeling of the protective oxide layer.

Sodium-sulfur (NaS) batteries are a promising energy storage technology that features high energy density, high cycle life, and no self-discharge. One of the cell design considerations that can affect the performance and construction of NaS batteries is cell geometry. While the planar geometry has advantages in power output, cell packing, ease of assembly, and thermal management, it has thermo-mechanical issues in sealing, which makes it less commercialized than the tubular geometry. In this work, the first multiphysics model of a NaS cell with a planar geometry was developed by extending an existing multiphysics model for a NaS cell with a tubular geometry. A previously developed multiphysics model for the tubular NaS cell was first adapted into COMSOL Multiphysics®. The model’s results were then validated against experimental discharge profiles and surface temperatures. After this, a planar NaS cell was simulated using the dimensions of an experimental planar cell, with the mass, heat, and charge transfer equations and parameters previously used in the tubular multiphysics model. The results from the planar model were then validated through comparison to experimental discharge profiles for a planar cell. The developed model can be used in future research and development of NaS batteries by comparing performance parameters of the planar geometry such as discharge profiles, energy densities, and temperatures with a multiphysics model of the tubular NaS battery.

Accurately estimating the capacity degradation of lithium-ion (Li-ion) batteries is vital in ensuring their safety and reliability in electric vehicles and portable electronics. Future capacity estimation using machine learning (ML) models allow battery lifetime predictions with minimal cycling data in the train set, well before capacity degradation occurs within the cell. The use of ML methods removes the need for prior knowledge of cell chemistry and the physical and chemical behaviors of batteries. In this paper, the data-driven ML models Gaussian process regression (GPR) and recurrent neural network – long short-term memory (RNN-LSTM) estimated the charge capacity of Li-ion batteries from the Oxford Battery Dataset, using only the battery's cycle index and capacity as input. With only 15 % of the battery’s lifetime as training data, the GPR model achieved a mean average percent error (MAPE) of 8.335 % and an R2 of 0.9755, while the LSTM model achieved a MAPE of 9.984 % and an R2 of 0.9898. These results indicate the goodness of fit and are comparable to results from similar models in the literature (MAPE = 9.1 to 15 %). The methodology may be applied to different features to help establish the relationship between health indicators and capacity fade and can be used in applications that require early capacity prediction such as in space technologies where lifetime and capacity are crucial in ensuring success and safety. This successful estimation highlights the promise and potential of accurately predicting Li-ion battery capacity degradation using a single-feature approach.

The global mission to reduce fossil fuel consumption has led to the escalating demand for electrochemical energy storage (EES) devices such as fuel cells and batteries. Computational techniques like Density Functional Theory (DFT) have recently been coupled with Machine Learning (ML) for high-throughput material screening and discovery. Transition metal surfaces are popular electrocatalyst candidates, but predictive ML regression models have only been applied to select metals such as Pt and Cu. Additionally, characterizing the contributions of each feature is challenging, especially on black-box models. In this work, regression models were trained to predict the adsorption energies of carbon, hydrogen, and oxygen on 27 fcc (111) monometallic surfaces and applied model-agnostic interpretation methods to evaluate feature importance. Over 200 adsorption energies on transition metal surfaces were collected from Catalysis-hub.org, a surface reaction database. A dataset was constructed for each adsorbate, and was composed of ten surface atomic, surface electronic, and adsorbate properties collected from online databases and DFT calculations on adsorbate-free surfaces. Then, the fine-tuned random forest regression, Gaussian process regression, and artificial neural network models predicted atomic adsorption energies while permutation feature importance calculated feature contributions. All ML model accuracies were found to be competitive with those from literature, with Gaussian process regression reporting the lowest errors of the three models. Coordination number was also found to have the largest contributions for all models. ML-DFT methodologies such as this can be expanded to accommodate alloys and more adsorbates for a wider screening of potential EES materials.

The global transition towards net-zero greenhouse gas emissions establishes a need for cleaner energy technologies. Hydrogen is a promising energy carrier whose global demand is steadily increasing and is conventionally produced through steam-methane reforming with carbon capture, or blue H2. Hydrogen production supplied by renewable energy (green H2) is an emerging process, but developing countries are not yet ready for a full transition. Augmenting blue H2 with green H2 production will allow a smoother transition until green H2 costs significantly decrease by 2050. In this work, a novel, low-cost teal hydrogen (teal H2) plant, a mixture of blue and green H2 technologies, located in the Philippines which combines steam-methane reforming, rice husk gasification, and carbon capture by monoethanolamine absorption, is proposed. Setting a production rate of 9,000 kg H2/h, the techno-economic potential of five cases with varying natural gas to rice husk contribution ratios were evaluated using AspenPlus. The levelized cost of the 25:75 teal H2 case at 1.06 USD/kg is cheaper than blue H2 and green H2 by 4.37 and 2.34 USD/kg, respectively. Moreover, the CO2-equivalent emissions of the 25:75 teal H2 case at 0.002 t CO2 -eq/1,000 Nm3 H2 is 57.10 % and 39.25 % lower than those from blue H2 and green H2. As green H2 becomes more economical, rice husk feed to the gasification process can be gradually increased to favor biomass- over petroleum-derived H2. This case study is a successful proof of concept that teal H2 may help transition the energy sector to carbon neutrality.

Ethylene glycol dimethacrylate (EGDMA) – dithiadiamide (DTDA) copolymer was synthesized by condensation reaction between monomeric mercaptoacetamide and ?-dibromoalkanes. This previously designed DTDA was complexed with Pt2+ before direct copolymerization with EGDMA. Pt2+-template was eluted with dilute HCl. The EGDMA-DTDA copolymer was evaluated for Pt2+ adsorption in terms of adsorption isotherm and selectivity. The copolymer exhibited monolayer Langmuir-type adsorption isotherm with qm = 177 mg g-1 at optimum pH = 1. It is selective to Pt2+ in a simulated solution containing the predominant cations present in an acid-leached spent 3-way, gasoline automobile catalytic converter sample (i.e., Al3+, Ba2+, Ce3+, Fe2+, Mg2+, and Zn2+).

Different energy storage technologies have particular applications with advantageous techno-economic characteristics. For this reason, the present and future Levelised Costs Of Storage (LCOS) of commercially mature energy storage technologies have been analysed in the current literature. Emerging energy storage technologies, such as long-duration flywheels, are also vying to capture the energy storage market, but uncertainties linger as to which applications they can capture due to limited and reliable publicly available data. In this work, we determined the future LCOS of a typical 1 MW installation of stationary electrochemical energy storage (lead-acid, sodium-sulphur, and lithium-ion battery) and mechanical energy storage technologies (short-duration flywheel and long-duration flywheel) under different applications from 2020 to 2050 using updated relevant techno-economic parameters. Based on the present costs of energy storage, lithium-ion batteries yield the lowest LCOE across different energy storage applications, corroborating with previous outlooks from different scholarly works. The cost advantage of lithium-ion batteries compared to other storage technologies continues to rise over the years due to their rapid cost decline. In the absence of lithium-ion batteries, long-duration flywheels initially provide the lowest cost for a wide range of applications, but they face stiff competition with sodium-sulphur batteries. By 2040, sodium-sulphur batteries are projected to have a lower LCOS than long-duration flywheels. Promoters and manufacturers of emerging energy storage technologies must find ways to rapidly decrease storage costs to secure their niche in the energy storage market.

Primary alkaline batteries have been widely used in portable electronics due to their low cost and safety. The consumption and disposal of these batteries has prompted notable research on their recycling. Another approach to reducing alkaline battery disposal is to extend their lifetime by increasing their energy density. In this work, the energy density of an AA primary alkaline battery was maximized by determining the optimum amount of electrode materials through multiphysics modeling. An electrochemical model of the alkaline battery is developed in COMSOL Multiphysics® and validated with discharge curves (i.e., voltage vs. time) obtained under constant resistance loads. The electrode thicknesses are then optimized to maximize the energy density of the battery while maintaining its exterior dimensions. The sensitivity of the energy density with respect to the electrode porosities and interfacial areas is then analyzed. The electrochemical model was able to replicate the discharge curves obtained under a 250 mA constant current discharge. The energy density is maximized by decreasing the thickness of the zinc anode. However, this results in anode dissolution near the current collectors and could compromise the electrical continuity in the battery. Increasing the anode thickness prevents dissolution at the current collectors but increases unused mass in the battery. The results of this study can be used to develop longer-lasting alkaline batteries. Furthermore, the model can be improved by considering thermal effects or modified to aid the development of rechargeable alkaline batteries.

Many people around the world rely on groundwater for drinking and sanitation, however, they are exposed to various health risks from the naturally occurring groundwater arsenic (As). Air-cathode Assisted Iron Electrocoagulation (ACAIE) using Carbon Black Pearls 2000® cathode was previously shown catalyse the removal of groundwater As by producing hydrogen peroxide (H2O2). This work explored Vulcan® XC-72, and Printex® L6 Carbon as alternative cathodes for iron electrocoagulation which are more selective towards the 2-electron oxygen reduction reaction into H2O2 compared to Carbon Black Pearls®. The cathodes were tested in an ACAIE set-up to treat synthetic groundwater spiked with 1,500 µg/L of As at different charge dosage rates (CDR) from 1.56 C/L-min to 100 C/L-min with a total charge dosage of 600 C/L for all set-ups. Although the electrocoagulation energies among the cathodes were similar, the use of Printex® cathode for ACAIE remediated the groundwater for all CDR with final As levels below 10 µg/L. This is in contrast with the less selective Carbon Black Pearls® at low CDR, and the less active Vulcan® Carbon at high CDR where the treated groundwater may still have As levels above 10 µg/L. Future research would explore modifications in the carbon materials and reactor configuration to further optimize ACAIE in removing groundwater As.

Acid-alkaline electrolyzers utilize an acidic catholyte and alkaline anolyte, which lower the thermodynamic voltage requirement for water splitting. Experiments have demonstrated the feasibility of acid-alkaline electrolyzers with proton exchange membranes, but a mathematical model has yet to be developed to understand their operation. This work developed a multiphysics model of a batch acid-alkaline electrolyzer with a H2SO4 catholyte, a NaOH anolyte, and a proton exchange membrane. The model was formulated in COMSOL Multiphysics® and validated using experimental current vs. voltage data in literature. The electrolyzer’s reactions and ion transport were analyzed based on the electrolyte potential, concentration profiles, and ion fluxes calculated by the model. The charge imbalance due to the consumption of H+ and OH- in the catholyte and anolyte, respectively, is addressed by Na+ transport from the anolyte to the catholyte. This contradicts the prevailing hypothesis that electroneutrality in a proton exchange membrane acid-alkaline electrolyzers is preserved by the Second Wien Effect, or water splitting in high electric fields. H+ is transported from the catholyte to the anolyte, which results in undesired acid-base neutralization. This is minimized by increasing the applied voltage, which shows a tradeoff between power and reactant consumption. Na+-selective membranes also hinder the neutralization reaction, but their realization is challenging due to the smaller Stokes radius of H+. The proposed model can be used to optimize the parameters of a batch electrolyzer and aid in the design of a continuous electrolyzer stack.

With the rising energy demand, safe and efficient energy storage technologies have been increasing in importance. Lithium Polymer (LiPo) batteries use a gel polymer to act as both separator and electrolyte, which is thermally and electrochemically more stable and safer than conventional liquid electrolytes. Besides exploring new materials, engineering a reliable multiphysics model is vital to exploiting and optimizing existing LiPo batteries' potential. This study developed a multiphysics model for a Lithium Cobalt Oxide (LCO)-graphite- Poly(vinylidene fluoride - hexafluoropropylene) (PVdF-HFP) pouch-type mobile phone LiPo. A pseudo-2-dimensional electrochemical model was coupled with a 3D thermal model using COMSOL Multiphysics® to determine the working voltage and temperature during discharge and was compared with experimental data from a commercial LiPo battery and evaluated using Root Mean Square Error (RMSE). The simulated discharge curve agrees remarkably well with the experimental results. The simulated temperature profile has shown appreciable discrepancies primarily due to the generated entropic change coefficient values that significantly affect the battery's heat generation. Overall, the models can be employed as a design tool to evaluate the component design and estimate the system performance of LiPo batteries for commercial applications. Furthermore, researchers can expand the study to investigate more advanced electrochemical phenomena and performances of state-of-the-art lithium and post-lithium chemistries.

A new class of technology known as transient electronics has the potential to reduce electronic waste with its unique ability to dissolve under a specific condition. Biodegradable batteries are essential to realizing fully integrated and self-sufficient systems. Pure magnesium-based materials are among the widely explored transient electrodes due to their superior mechanical properties. However, such materials have rapid degradation rates. AZ31, an alloy of Mg containing 3 % aluminum (Al) and 1 % zinc (Zn) by weight, has more stable corrosion product layers, making them advantageous as anode materials. In this work, the electrochemical performance of the two full cell combinations of Mg and AZ31 anodes each paired with copper oxide (CuO) cathode were compared in a seawater electrolyte. To produce the cathodes, pristine copper foils were galvanostatically anodized at a current density of 2.0 mA/cm2 for 90 min. Results showed that AZ31-based cell has a higher specific energy of 1.63 J/cm2 compared to pure Mg-based cell which has 1.08 J/cm2. The observed nominal operating voltages for an AZ31-based cell were 1.1 V for the first 0.6 h and remained at 0.8 V for the next 5.6 h when discharged at a current density of 0.25 mA/cm2. The AZ31-based cell also yields a better specific capacity of 2.17 mAh/cm2 and a discharge time of 8.68 h, which is twice the capacity and the discharge life of a pure Mg-based cell. The reported increase in performance is attributed to the presence of alloying components in the anode which limits the parasitic corrosion inherent in a pure Mg anode.

The renewable energy transition requires energy storage technologies for grid-balancing and transportation. Lithium-ion batteries have been widely adopted for these applications, but supply risks due to geopolitical tensions have motivated the search for alternative chemistries less dependent on critical raw materials. Sodium-ion batteries have garnered notable attention as promising post-lithium chemistry due to the relative abundance of sodium and its similar manufacturing process to lithium-ion batteries. This work estimated the cost of producing sodium-ion battery packs from cells optimized via multiphysics modeling for energy or power-based applications. This study replicated a multiphysics model of a pouch format sodium-ion battery from literature in COMSOL Multiphysics®. This model determined the optimal active material used in batteries under 0.1C to 10C discharge rates to maximize the energy density. The cost of battery packs produced from the optimized cells was then determined using the Battery Performance and Cost (BatPaC) model of Argonne National Laboratory, which considers material and manufacturing costs. The optimization results reveal that energy cells have thicker electrodes and lower porosities (217 µm thick 0.11 porosity anode, 237 µm thick 0.10 porosity cathode for 0.1C), which maximize the amount of active material per unit mass. Power cells have thinner electrodes and larger porosities to minimize electrical resistance (58 µm thick 0.32 porosity anode, 63 µm thick 0.31 porosity cathode for 10C), reducing energy losses at high currents. Moreover, we compared the calculated production cost for energy and power applications for sodium-ion batteries, highlighting essential parameters affecting the price. The model observed a 26.42% increase in total material cost per kWh when transitioning from energy to power cells. The model may also be refined by considering sodium-ion batteries with different cathode and anode chemistries in different formats and their applications in different use cases.

Imidazoline receptors (IR) are classified into three receptor subtypes (I1R, I2R, and I3R) and previous studies showed that regulation of I2R signaling has neuroprotective potential. In order to know if I2R has a role in modulating vascular tone in health and disease, we evaluated the putative vasoactive effects of two recently synthesized I2R ligands, diethyl (1RS,3aSR,6aSR)-5-(3-chloro-4-fluorophenyl)-4,6-dioxo-1-phenyl-1,3a,4,5,6,6a-hexahydropyrrolo[3,4-c]pyrrole -1-phosphonate (B06) and diethyl [(1-(3-chloro-4-fluorobenzyl)-5,5-dimethyl-4-phenyl-4,5-dihydro-1H-imidazol-4-yl]phosphonate] (MCR5). Thoracic aortas from Oncins France 1 (3- to 4-months-old) and C57BL/6 (3- to 4- and 16- to 17-months-old mice) were mounted in tissue baths to measure isometric tension. In young mice of both strains, MCR5 induced greater relaxations than either B06 or the high-affinity I2R selective ligand 2-(2-benzofuranyl)-2-imidazoline (2-BFI), which evoked marginal responses. MCR5 relaxations were independent of I2R, as IR ligands did not significantly affect them, involved activation of smooth muscle KATP channels and inhibition of L-type voltage-gated Ca2+ channels, and were only slightly modulated by endothelium-derived nitric oxide (negatively) and prostacyclin (positively). Notably, despite the presence of endothelial dysfunction in old mice, MCR5 relaxations were preserved. In conclusion, the present study provides evidence against a functional contribution of I2R in the modulation of vascular tone in the mouse aorta. Moreover, the I2R ligand MCR5 is an endothelium-independent vasodilator that acts largely via I2R-independent pathways and is resistant to aging. We propose MCR5 as a candidate drug for the management of vascular disease in the elderly.