Large-scale Deployment Research Articles

Optimizing NVMe Storage for Large-Scale Deployment: Key Technologies and Strategies in Alibaba Cloud

Optimizing NVMe Storage for Large-Scale Deployment: Key Technologies and Strategies in Alibaba Cloud

Potential and climate effects of large-scale rooftop photovoltaic energy deployment in northwest China’s capital cities

China's pursuit of photovoltaic (PV) power, particularly rooftop installations, addresses energy and ecological challenges, aiming to reduce basic energy consumption by 50% by 2030. The northwest region, with its solar potential, is a focal point for distributed PV growth, which has already exceeded 50% of the energy mix by 2021. This study assesses the rooftop PV potential in five northwestern capitals, finding favorable conditions such as ample space, dense populations, and high sunlight exposure. Cities like Yinchuan and Xining are highlighted for their high projected annual PV yields. Although there is a negative correlation between surface temperature and vegetation coverage, the Weather Research and Forecasting (WRF) numerical simulation found that large-scale deployment of rooftop photovoltaics will not have a significant impact on the vegetation ecology of the area. The research underscores the significant role of rooftop PV in achieving China's energy and climate goals in its northwestern urban centers.

Secure pairing-free certificateless aggregate signcryption scheme for IoT

The widespread application of wireless sensor technology in the Internet of Things (IoT) industry significantly enhances productivity. However, the large scale deployment of IoT and the inherent vulnerabilities of wireless communication methods to attacks present significant new challenges. Consequently, there is a need to address the efficiency and security of information transfer in IoT. To effectively solve these issues, this paper presents a secure and efficient pairing-free certificateless aggregated signcryption (CL-ASC) scheme for IoT based on the elliptic curve cryptosystem. Our scheme avoids the complex certificate management issues associated with Public Key Cryptography (PKC) and the key escrow problem found in identity-based cryptography, while maintaining the storage and communication efficiency benefits of aggregated signcryption. The use of secure signcryption and aggregation techniques effectively resists a variety of potential attacks. Both formal and informal security analyses demonstrate that our scheme meets the expected security requirements. Specifically, our scheme shows significant improvements in computational and communication overheads. Compared to other state-of-the-art protocols, our scheme achieves signcryption computation cost of 0.691 ms, unsigncryption computation cost of 3.917 ms for 5 messages, and a total cost of 4.608 ms for 5 messages. Additionally, it provides a signcryption communication overhead of 128 bytes and aggregated communication overhead of 580 bytes for 5 messages.

Efficient distributed architecture and optimized subarray control strategy to facilitate large-scale coherent beam combination

Traditional coherent beam combination (CBC) system architecture has revealed inadequacies in meeting the concurrent demands of large-scale deployment and high-bandwidth requirements. Addressing this challenge, we propose a distributed CBC system architecture based on the optimized stochastic parallel gradient descent (SPGD) algorithm. Our strategy segments the large-scale laser array into multiple independent smaller-scale subarrays, ensuring their efficient phase convergence through the introduction of corresponding reference lasers while avoiding interference when integrating different subarrays. Moreover, the piecewise SPGD algorithm is proposed and the intensity of the reference laser is modulated to further improve the convergence speed and accuracy within subarrays, enhancing the algorithm's compatibility across laser arrays of varying scales. We have validated the feasibility of the distributed CBC architecture through numerical analysis and assessed the strategy's performance in both static and dynamic environments using simulation software. The simulation findings indicate that, compared to traditional CBC systems, distributed architecture with 3, 7, and 19 subarrays and utilizing the piecewise SPGD algorithm, has experienced phase control bandwidth enhancements by factors of approximately 3.6, 10.4, and 32.5 respectively, maintaining superior average power output in dynamic noise environments. The proposed architecture and strategy also accommodate subarrays of variable scales and obviates the necessity for large-aperture optical components on the emitted plane, demonstrating exceptional scalability and adaptability.

A novel offshore energy station with poly-generation of power, fresh water, heat, cold and ice

Energy storage system with large capacity, high efficiency, low cost and long time is major bottleneck, limiting the large-scale deployments of offshore wind power. To improve energy recovery efficiency and energy storage density, here underwater compressed air energy storage (CAES) with isobaric operation is proposed. At about 5 MPa storage pressure, energy recovery efficiency and energy storage density for traditional CAES are respectively 59.45 % and 2.94 kWh/m3, while they increase to 70.35 % and 6.59 kWh/m3 respectively for underwater CAES. The frictional pressure loss of underwater pipeline for air delivery has a remarkable effect on the performance of underwater CAES, and energy recovery efficiency and energy storage density increase from 51.92 % to 67.97 % and from 4.85 kWh/m3 to 6.28 kWh/m3 respectively when the pipeline diameter increases from 0.35 m to 0.45 m. To improve economic profit and utilize interstage compression heat and high frequency electrical energy more efficiently, an offshore energy station with poly-generation is proposed, which has the energy recovery efficiency of 58.26 % and energy storage density of 7.16 kWh/m3, and generates 358.22 MWh of electrical energy, 252.75 GJ of thermal energy, 66.10 GJ of cold energy, 123.23 tons of ice and 257.25 tons of fresh water each day, opening new ways of deploying offshore wind power and offshore energy storage and also providing strong support for developing and utilizing sea resources and conquering the oceans.

A portable and efficient dementia screening tool using eye tracking machine learning and virtual reality

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer’s Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA’s potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments.

Revolution in Renewables: Integration of Green Hydrogen for a Sustainable Future

In recent years, global efforts towards a future with sustainable energy have intensified the development of renewable energy sources (RESs) such as offshore wind, solar photovoltaics (PVs), hydro, and geothermal. Concurrently, green hydrogen, produced via water electrolysis using these RESs, has been recognized as a promising solution to decarbonizing traditionally hard-to-abate sectors. Furthermore, hydrogen storage provides a long-duration energy storage approach to managing the intermittency of RESs, which ensures a reliable and stable electricity supply and supports electric grid operations with ancillary services like frequency and voltage regulation. Despite significant progress, the hydrogen economy remains nascent, with ongoing developments and persistent uncertainties in economic, technological, and regulatory aspects. This paper provides a comprehensive review of the green hydrogen value chain, encompassing production, transportation logistics, storage methodologies, and end-use applications, while identifying key research gaps. Particular emphasis is placed on the integration of green hydrogen into both grid-connected and islanded systems, with a focus on operational strategies to enhance grid resilience and efficiency over both the long and short terms. Moreover, this paper draws on global case studies from pioneering green hydrogen projects to inform strategies that can accelerate the adoption and large-scale deployment of green hydrogen technologies across diverse sectors and geographies.

Improving land-use efficiency of solar power in China and policy implications

Improving the power output of solar photovoltaic (PV) farms is critical to maximize the potential of PV power and reduce extensive land use in the context of large-scale deployment of renewable energy. In this paper we developed an integrated solar power potential assessment framework to quantify the gap between technical potential and actual generation of solar PV farms on national, provincial, and plant scales, and identify the key factors that cause the underperformance of PV farms. Specifically, to assess technical potential, hourly meteorological reanalysis data is adopted and the power generation-related parameters are calculated (i.e. optimal panel tilt, array spacing, packing factor etc.) with consideration of their spatial variability. For actual power generation, a detailed plant-level dataset is first established by this study which integrates technical, operational, and geospatial information from 145 solar farms across seven provinces in China. Our results show that the actual PV power generation per square meter is only 1/3 of the estimated technical potential. Technological factor is the primary factor, accounting for 48.43% of the underperformance, followed by engineering and management factors, accounting for 38.55% and 13.02%, respectively. The novelty of our study is revealing the underperformance level of solar farms in reality, identifying the causing factors, and highlighting the importance of integrating land-use efficiency indicators into solar power planning and development.

Global analysis of geological CO2 storage by pressure-limited injection sites

Limiting global warming to a 2 °C rise may require large-scale deployment of carbon capture and storage (CCS). Due to the key role CCS plays in integrated assessment models of climate change mitigation, it is important that fundamental physical constraints are accounted for. We produce a global estimate of CO2 storage resource that accounts for pressure-limits within basin-scale reservoir systems. We use a dynamic physics model of reservoir pressurisation that is sufficiently simple to be incorporated into energy systems models. Our estimates address regionally inconsistent methodologies and the general lack of consideration for pressure limitations in global storage resource estimates. We estimate a maximum pressure-limited resource base and explore scenarios with different injection patterns, and scenarios where the extent of CCS deployment is limited by the history of regional hydrocarbon exploration and the readiness of countries for deployment. The maximum pressure-limited global storage achievable after thirty years of injection is 3640GtCO2 (121GtCO2yr-1), increasing to 5630GtCO2 (70 GtCO2yr-1) at the end of the century. These represent an update to volumetric-based estimates that suggest in excess of 10,000Gt of storage resource available. When CCS deployment is limited to the top ten countries ranked by the GCCSI Storage Readiness Index, our maximum storage estimate decreases to 780GtCO2 (26GtCO2yr-1) at the mid-century and 1177GtCO2 (15GtCO2yr-1) at the end of the century. These latter results fall within the range of projected deployment by the IPCC and IEA and suggest that reservoir pressurisation will limit CCS deployment if development does not rapidly expand beyond the current implementation.

Automatic heliostat learning for in situ concentrating solar power plant metrology with differentiable ray tracing

Concentrating solar power plants are a clean energy source capable of competitive electricity generation even during night time, as well as the production of carbon-neutral fuels, offering a complementary role alongside photovoltaic plants. In these power plants, thousands of mirrors (heliostats) redirect sunlight onto a receiver, potentially generating temperatures exceeding 1000°C. Practically, such efficient temperatures are never attained. Several unknown, yet operationally crucial parameters, e.g., misalignment in sun-tracking and surface deformations can cause dangerous temperature spikes, necessitating high safety margins. For competitive levelized cost of energy and large-scale deployment, in-situ error measurements are an essential, yet unattained factor. To tackle this, we introduce a differentiable ray tracing machine learning approach that can derive the irradiance distribution of heliostats in a data-driven manner from a small number of calibration images already collected in most solar towers. By applying gradient-based optimization and a learning non-uniform rational B-spline heliostat model, our approach is able to determine sub-millimeter imperfections in a real-world setting and predict heliostat-specific irradiance profiles, exceeding the precision of the state-of-the-art and establishing full automatization. The new optimization pipeline enables concurrent training of physical and data-driven models, representing a pioneering effort in unifying both paradigms for concentrating solar power plants and can be a blueprint for other domains.

Soft, Multifunctional MXene-Coated Fiber Microelectrodes for Biointerfacing.

Flexible fiber-based microelectrodes allow safe and chronic investigation and modulation of electrically active cells and tissues. Compared to planar electrodes, they enhance targeting precision while minimizing side effects from the device-tissue mechanical mismatch. However, the current manufacturing methods face scalability, reproducibility, and handling challenges, hindering large-scale deployment. Furthermore, only a few designs can record electrical and biochemical signals necessary for understanding and interacting with complex biological systems. In this study, we present a method that utilizes the electrical conductivity and easy processability of MXenes, a diverse family of two-dimensional nanomaterials, to apply a thin layer of MXene coating continuously to commercial nylon filaments (30-300 μm in diameter) at a rapid speed (up to 15 mm/s), achieving a linear resistance below 10 Ω/cm. The MXene-coated filaments are then batch-processed into free-standing fiber microelectrodes with excellent flexibility, durability, and consistent performance even when knotted. We demonstrate the electrochemical properties of these fiber electrodes and their hydrogen peroxide (H2O2) sensing capability and showcase their applications in vivo (rodent) and ex vivo (bladder tissue). This scalable process fabricates high-performance microfiber electrodes that can be easily customized and deployed in diverse bioelectronic monitoring and stimulation studies, contributing to a deeper understanding of health and disease.

Maturity quantification and evaluation of R&I needs of the smart grid

The European Technology and Innovation Platform Smart Networks for Energy Transition (ETIP SNET) roadmap has proposed the development of a set of high-level use cases serving the Power Grids of the future as a decisive need calling for research, small to large-scale demonstrations and deployment of services and products leading to replication and scaling-up to realize real-world implementation. R&I Projects orchestrate these developments and thus the evolution of technologies, solutions and systems are their valuable deliverable that respond to calls for contribution. As such in this publication we use this linking chain of evolution to develop a sound methodology starting from the evaluation of project results and leading to sound quantification of research needs. Specifically, by keeping track of technologies / solutions / systems advancement, the quantification of the maturity of high-level use cases can be induced following a universal approach and thus the whole smart grid system readiness can be evaluated as a valuable input to gap analysis and identification of R&I needs enabling appropriate policy decisions.

System-level optimisation of hybrid energy powered irrigation system

Renewable energy-powered irrigation systems have emerged as sustainable solutions, particularly for farmers in off-grid areas. While existing research often highlights tank storage-based systems as the most cost-effective option, large-scale deployment of water tanks incurs significant costs and maintenance challenges. Additionally, there is limited research on the feasibility and optimisation of battery-based irrigation systems, which are often deemed costly despite their potential benefits. This study addresses this gap by identifying the optimal storage solution for hybrid energy-powered irrigation systems through a system-level optimisation model. The model evaluates the suitability of three storage options: direct-coupled water tank storage, battery-coupled storage, and a hybrid battery-tank storage system. Optimisation criteria include life cycle cost (LCC), loss of power supply probability (LPSP), and loss of load probability (LOLP), ensuring a comprehensive assessment of both cost and reliability. Results indicate that the hybrid battery-tank storage system is the most reliable, followed by battery-only storage, while tank-only storage, despite its lower initial cost, poses scalability and maintenance challenges. The LCC over a 25-year project lifetime is £31 k for battery-tank, £26 k for battery-only, and £23.3 k for tank-only systems. Despite the lower cost of tank storage, its complexity and maintenance make it the least preferred option for large-scale systems.

(Energy Technology Division Graduate Student Award sponsored by BioLogic) In-Situ Diagnostic Tools and Analysis to Identify Root Causes for Voltage Losses in Anion Exchange Membrane Water Electrolyzers

Water electrolysis has been getting significant attention worldwide as a leading technology that allows for the storage of renewable energy in the form of hydrogen. Anion exchange membrane water electrolyzers (AEMWEs) have emerged in recent years as a possible low-cost and high performance alternative to traditional alkaline electrolyzers (AWEs) and proton exchange membrane electrolyzers (PEMWEs). AEMWEs can potentially offer the complimentary benefits of the two developed technologies, namely high current density, pressurized cell operation, the use of platinum group metal (PGM) free electrocatalysts, and the ability to use earth abundant cheap materials for other cell components [1-2]. AEMWEs are currently in an early research and development stage. Most of the attention has been focused on the development of new polymers, electrocatalysts and other component materials [3-4]. Such materials are generally reported to offer high performance and durability, evaluated using standard ex-situ electrochemical techniques. Integration of these materials in actual water electrolyzer system often does not translate linearly into long life and high performance, and understanding material behavior in the real operating conditions is essential for their large-scale deployment. In-situ performance evaluation and diagnostic tools help analyze the contributions from individual components at membrane electrode assembly (MEA) level [5-7]. Differentiating kinetic, ohmic, transport as well as the catalyst layer overpotential contributions may lead a way for enhancing overall device performance and degradation. For instance, high IEC, water uptake and conductivity of polymer membranes and ionomers are desirable, however, may not linearly translate in high in-situ performance. Excessive swelling of the membrane and/or ionomer may dominate, increasing the charge transfer resistance, subsequently lowering the overall device performance [8]. In this presentation, a combination of methods – electrochemical impedance spectroscopy (EIS), cyclic voltammograms (CVs), I-V scans and constant current / voltage hold – are used to evaluate device performance and separate individual voltage losses caused by each MEA component. These tools help pinpoint the shortcomings of individual components that lead to losses in cell performance or degradation, allowing for new control strategies to be developed.

Enhancing Efficiency and Durability of PEM Water Electrolysis with Low Iridium Loading through Nanofiber-Modified Porous Transport Electrodes

Hydrogen plays a significant role in the transition of the global energy system towards achieving net-zero emissions. According to the “Hydrogen for Net-Zero” report by Hydrogen Council and McKinsey & Company, the demand for clean hydrogen could reach 140 metric tons (MT) by 2030, with a further increase to 660 MT by 2050. For future high-volume production of green hydrogen, Proton Exchange Membrane Water Electrolysis (PEMWE) is a crucial technology due to its advantages of high-purity hydrogen, large operational current densities, and great energy conversion efficiency [1]. However, the use of precious metal-based catalysts for the sluggish oxygen evolution reaction (OER) on the anode side hinders the high-volume manufacturability of PEMWE. To ensure cost-effectiveness in large-scale PEMWE deployment for hydrogen production, developing strategies to reduce Iridium loading to 0.1 - 0.2 mg Ir/cm2 without compromising overall performance is essential. The challenge of reducing Ir loading also involves improving or maintaining the electrical contact between the catalyst layers and the substrate [2-3].Smoltek Hydrogen has made significant strides in achieving low iridium loading through our innovative Porous Transport Electrode (PTE) concept. This approach involves modifying a Porous Transport Layer (PTL) by growing vertically aligned Carbon Nanofibers (CNFs) using our in-house patented technology, efficiently increasing surface area [4]. Following this modification, a protective layer of corrosion-resistant and conductive material (e.g., Platinum) is applied to the CNFs. Subsequently, an OER catalyst layer is deposited utilizing a three-electrode cathodic electrodeposition method (Fig. 1a and 1b). The electrodeposited Ir catalyst exhibits strong adhesion to the CNF PTEs, resulting in a high electrical conductivity [5].In this work, we successfully reduced Ir loadings to 0.1 mg/cm2 in the PTE, demonstrating excellent mass activity in the half-cell tests (Fig. 1c). In the PEMWE tests, the polarization curves of electrodes with low Ir loading exhibited superior performance compared to cells with significantly higher Ir loading (Fig. 1d). The next step of our research involves conducting constant current durability tests for over 600 hours on PEMWE operation using low Ir loaded PTEs. The aim is to minimize degradation and overcome mass transport limitations, particularly at higher current densities.These unique PTEs developed by Smoltek Hydrogen, with CNF technology, maximize the electrode surface area and pave a new way to increase the utilization ratio of catalyst surface at a low loading amount, which makes it a competitive anode material in today’s PEMWE market. References https://www.iea.org/reports/hydrogen-2156#dashboardMöckl et al. J. Electrochem. Soc., 2022, 169, 064505.Bernt et al. Chem. Ing. Tech., 2020, 92, 31-39.https://www.smoltek.com/applications/electrolyzers/Krivina et al. Adv.Mater., 2022, 34, 2203033.https://publications.jrc.ec.europa.eu/repository/handle/JRC104045 Figure 1

Elucidating Fundamental Structure-Function Relationships for Iridium Oxide Catalysts in PEM Water Electrolyzers

Proton exchange membrane water electrolysis (PEMWE) will contribute substantially towards decarbonization efforts and is expected to make up 40% of the green hydrogen market.1 A primary barrier for PEMWE scale-up is the detrimentally high usage of iridium for the oxygen evolution reaction catalyst. To achieve large-scale PEMWE deployment, loading reductions from 2-3 mgIr cm-2 to 0.10 mgIr cm-2 must occur.2 Such a feat can only be achieved through significant improvements in activity and durability. However, there exists a lack of fundamental understanding in how catalyst properties correlate to performance, thus hindering optimization efforts. The phenomena of catalyst activity/durability are frequently treated with purely empirical approaches, and thus the foundational knowledge needed for improved catalyst design—such as understanding how crystallinity and oxidation states impact performance in a membrane electrode assembly (MEA)—has not been well investigated.3 This work serves to bridge this gap in knowledge by correlating extensive structural characterization data with MEA activity and durability for six commercial IrOx catalysts. Properties such as oxidation states, chemical composition, local atomic/electronic configuration, crystallinity, surface area, and particle size distribution were elucidated for each catalyst in powder and/or MEA form. Following MEA conditioning, catalyst activity was benchmarked via polarization curves and Tafel analysis. Lastly, durability was evaluated by performing a 500h accelerated stress test (adapted from the H2New consortium), with electrochemical characterization conducted every 100h. The presence of amorphous character and the Ir3+ oxidation state was the best determinant of activity. Durability, on the other hand, was associated with greater crystalline character and more redox-stable cyclic voltammogram profiles. Correlations regarding chemical composition, local atomic/electronic configurations, particle size, and surface area will be discussed as well. Smolinka, T.; Wiebe, N.; Sterchele, P.; Palzer, A.; Lehner, F.; Jansen, M.; Kiemel, S.; Miehe, R.; Wahren, S.; Zimmermann, F. Studie IndWEDe: Industrialisierung Der Wasserelektrolyse in Deutschland: Chancen Und Herausforderungen Für Nachhaltigen Wasserstoff Für Verkehr, Strom Und Wärme.; 2018.Clapp, M.; Zalitis, C. M.; Ryan, M. Perspectives on Current and Future Iridium Demand and Iridium Oxide Catalysts for PEM Water Electrolysis. Catal Today 2023, 420 (March), 114140. https://doi.org/10.1016/j.cattod.2023.114140.Lazaridis, T.; Stühmeier, B. M.; Gasteiger, H. A.; El-Sayed, H. A. Capabilities and Limitations of Rotating Disk Electrodes versus Membrane Electrode Assemblies in the Investigation of Electrocatalysts. Nat Catal 2022, 5 (5), 363–373. https://doi.org/10.1038/s41929-022-00776-5.

(Invited) Affordable Green Hydrogen from Alkaline Water Electrolysis: An Industrial Perspective

Electrolysers is a novel component in the energy system and is expected to play a key role in the transition to a fossil free energy system and supply Green Hydrogen to a number of small- and large-scale applications within a number of industries e.g. transportation, industry etc. with several hundreds of GW is projected to be installed towards 2030.Modularity and mass production are key factors for the large scale deployment of electrolysis as envisioned in Hydrogen Strategies across the World. However, a number of different design strategies and modularities can be chosen in order to achieve this.This talk focuses on fundamental aspects of alkaline electrolysis including industrial requirements for catalysts and diaphragms, how to develop an electrolyser product and the development of multi-MW alkaline electrolysers plants with factory assembled modules allowing rapid on-site installation in order to keep up with the pace needed to reach deployment targets.

Semi-Analytical Model for Hybrid and Redox Targeting Flow Battery Systems

Redox flow battery represents an economically viable energy storage technology that can integrate intermittent renewable energies from solar and wind power into existing electric grids. Besides the conventional all-liquid redox flow battery, there are two different architectures of flow batteries: hybrid and redox targeting flow batteries. Because the reactive solid phase is involved in the system, the energy storage density increases significantly. Therefore, they are considered as ones of the potential solutions for the future large-scale deployment of long-duration energy storage systems (LDES). A digital twin (DT) has been demonstrated as a promising numerical framework and platform to assist in the design, deployment, and operation of LDES. DTs usually require a fast-response model to provide an estimation of the flow battery's performance, which can be a fast-forward physics-based model or a machine learning-based regression model. Many pioneering works and our previous studies have introduced several fast-forward physics-based models for the conventional all-liquid redox flow battery in both inorganic and organic systems. In this study, a semi-analytical model, which can adapt to DTs, is proposed to provide an estimation of the performance of the hybrid and the redox targeting flow battery at different operating conditions.

Development of a Highly Sensitive Electrochemical Detection of Salivary Glucose and pH Sensors Based on Glucose Oxidase Using Interdigitated Microelectrodes

The use of electrochemical nanosensors has seen a significant uptick in recent years, largely due to the improved chemical and physical properties that result from the modification of nano or microelectrode devices with nanomaterials.[1] These electrodes play a crucial role in enhancing electrochemical-based nanosensors, offering benefits such as a higher signal-to-noise ratio, lower detection limits, cost-effectiveness, ease of fabrication, and the ability to achieve steady-state currents. The interest in electrochemical nanosensors is considerable, given their broad application spectrum, which includes areas like animal health, human health, environmental monitoring, and bioprocess monitoring.[2] The focus of this research is the development of on-chip salivary glucose and pH sensors that are integrated on silicon wafers. These sensors are specifically designed to identify two frequently occurring bovine diseases: bovine respiratory disease (BRD) and scour. Both of these diseases are highly contagious, often resulting in fatal outcomes for calves, and cause economic losses amounting to millions of euros across Europe. Therefore, early detection of BRD and scour is essential to prevent, oversee, or delay the progression of the disease and its related complications. However, the current methods of blood glucose testing for calves have a significant disadvantage, as they require regular invasive blood sampling. Encouragingly, the analysis of saliva has surfaced as a non-invasive and convenient alternative for monitoring glucose levels in calves with above-mentioned disease.[3] Thus, it is necessary to develop a highly sensitive electrochemical nanosensor for early detection of BRD and scour by using biomarkers (glucose, pH) in a calf's saliva. The proposed electrochemical nanosensor is based on a micro fabricated device that integrates a silicon microelectrode, reference electrode, and counter electrode with six individual sensor that can be used for the detection of six different biomarkers i.e. glucose, pH, lactate, electrolytes, etc. The sensing mechanism is based on the measurement of changes in current that occur because of glucose and pH interactions with the modified silicon surface. The sensors are designed to detect small changes in glucose and pH concentrations that can be indicative of the onset of BRD and scour. The main advantage of the proposed electrochemical nanosensor is its ability to provide rapid and accurate detection of BRD and scour in calves. Early detection of these diseases can significantly improve treatment outcomes and reduce the risk of transmission to other animals. In addition, the low cost and ease of fabrication of these sensors make them an attractive option for large-scale deployment in agricultural settings. In conclusion, the development of on-chip electrochemical nanosensors for the early detection of bovine respiratory disease and scour by using saliva as a medium is a promising approach for improving animal health and reducing economic losses. The proposed sensors offer high sensitivity, low cost, and easy fabrication, making them an attractive option for widespread deployment in agricultural settings. Future research can focus on optimizing the sensor design for improved performance and expanding the range of biomarkers that can be detected.This work represents the microfabrication and characterization of gold interdigitated electrodes (IDE) on silicon using standard microfabrication methods i.e., lithography and etching techniques. The sensor have six individual working electrodes (WEs) along with on-chip gold counter and platinum reference electrodes and can be modified individually for glucose and pH sensor. A two-step electrodeposition process was carried out for platinum platting thus incorporating glucose oxidase (GOx) onto a platinum-modified gold IDE with an o-phenylenediamine (o-PD). The enzymatic based nanosensor demonstrated a linear reduction response to wider range of glucose from 0.02-7mM using chronoamperometric measurements in artificial saliva (Figure 1a,b). In addition, real calf samples have been used to detect the glucose level in calf saliva and compared with commercial glucometer (Figure 1c). Furthermore, at gold IDE, the sensor exhibits linear responses for pH ranging from pH 5-8 using cyclic voltammetric analysis (Figure 1d,f).

Performance Optimization of Alkaline Water Electrolysis Via Catalyst Morphology Tuning and Novel Cell Design

Hydrogen is a basic energy carrier for numerous valuable industrial products. Green hydrogen produced using renewable electricity and water electrolysis is pivotal in advancing a decarbonized sustainable energy ecosystem. Two primary electrolysis technologies, alkaline water electrolysis (AWE) and polymer electrolyte membrane water electrolysis (PEMWE), have demonstrated a viable commercial perspective. AWE is particularly well-suited for large-scale deployment, especially at the gigawatt scale, due to low capital cost and long lifetime. [1]. The performance of AWE is largely determined by the effectiveness of nickel-based oxygen evolution reaction (OER) catalysts and PGM-based or PGM-free hydrogen evolution reaction (HER) catalysts. To enhance the catalyst activity and cell performance, we meticulously control morphology and chemical composition of the catalysts. Cell testing was conducted in cells of various active areas (5, 25, and 50cm2) to demonstrate the result consistency and scalability. An innovative cell design, incorporating a reference hydrogen electrode in both cathode and anode, enabled the real-time acquisition of half-cell polarization curves during tests. This configuration proved valuable information to identify performance limitations in the single cells, thus guiding effective cell design and optimization. Furthermore, a cell comprising Ni-based anode and PGM cathode demonstrated remarkable stability.1. Schalenbach, Aleksandar R. Zeradjanin, Olga Kasian, Serhiy Cherevko, and Karl J.J. Mayrhofer, Int. J. Electrochem. Sci., 1173–1226 (2018).