Minimal Requirements Research Articles

Adaptive Arithmetic Coding-Based Encoding Method Toward High-Density DNA Storage.

With the rapid advancement of big data and artificial intelligence technologies, the limitations inherent in traditional storage media for accommodating vast amounts of data have become increasingly evident. DNA storage is an innovative approach harnessing DNA and other biomolecules as storage mediums, endowed with superior characteristics including expansive capacity, remarkable density, minimal energy requirements, and unparalleled longevity. Central to the efficient DNA storage is the process of DNA coding, whereby digital information is converted into sequences of DNA bases. A novel encoding method based on adaptive arithmetic coding (AAC) has been introduced, delineating the encoding process into three distinct phases: compression, error correction, and mapping. Prediction by Partial Matching (PPM)-based AAC in the compression phase serves to compress data and enhance storage density. Subsequently, the error correction phase relies on octal Hamming code to rectify errors and safeguard data integrity. The mapping phase employs a "3-2 code" mapping relationship to ensure adherence to biochemical constraints. The proposed method was verified by encoding different formats of files such as text, pictures, and audio. The results indicated that the average coding density of bases can be up to 3.25 per nucleotide, the GC content (which includes guanine [G] and cytosine [C]) can be stabilized at 50% and the homopolymer length is restricted to no more than 2. Simulation experimental results corroborate the method's efficacy in preserving data integrity during both reading and writing operations, augmenting storage density, and exhibiting robust error correction capabilities.

Consideration of the role of protein quality in determining dietary protein recommendations

The quality of a dietary protein refers to its ability to provide the EAAs necessary to meet dietary requirements. There are 9 dietary amino acids that cannot be metabolically produced in the body and therefore must be consumed as part of the diet to avoid adverse metabolic consequences. These essential amino acids (EAAs) serve a variety of roles in the body. The amount and profile of the dietary EAAs relative to the individual EAA requirements and the digestibility of the dietary protein are the key factors that determine its quality. Currently the Digestible Indispensable Amino Acid Score (DIAAS) is the best available approach to quantifying protein quality. The most prominent metabolic role of dietary EAAs is to stimulate protein synthesis by serving as signals to activate molecular mechanisms responsible for the initiation of protein synthesis and, most importantly, to provide the necessary precursors for the synthesis of complete proteins. Current dietary recommendations generally do not consider protein quality. Accounting for protein quality in dietary patterns can be accomplished while staying within established ranges for dietary protein consumption. Poor protein quality can be compensated for to some extent by eating more low-quality protein, but to be effective (“complementary”) the limiting EAA must differ between the low-quality protein and the base diet to which it is being supplemented. Adding a high-quality protein to a dietary pattern based on low-quality protein is more effective in meeting EAA goals than increasing the amount of low-quality protein, even if the low-quality proteins are complementary. Further, reliance entirely on low-quality protein food sources, particularly in circumstances that may benefit from a level of dietary EAAs greater than minimal requirements, is likely to include excessive caloric consumption. While protein consumption in high-income nations is generally perceived to be adequate or even excessive, assessment of dietary patterns indicates that a significant percentage of individuals may fall short of meeting optimal levels of EAA consumption, especially in circumstances such as aging in which the optimal EAA consumption is greater than basal values for healthy young individuals. The case is made that protein quality is an important consideration in meeting EAA requirements.

EXPERIMENTAL EVALUATION OF THE PERFORMANCE OF DYNAMIC VIBRATION ABSORBERS FOR VIBRATION MITIGATION IN BEAM STRUCTURES

In this study, experiments are used to evaluate the effectiveness of dynamic vibration absorbers (DVAs) in minimizing vibrations in beam structures. The dynamic vibration absorbers are modest additions to a structure that employ a mass-spring system tuned to the natural frequency of the structure to lower vibration levels. These absorbers were added to a beam construction as part of the experimental investigation, and the vibration levels under various conditions were measured. Under pinned-free boundary, the dynamic behavior of a beam is experimentally investigated with various combinations of the design parameters (mass and spring) and locations of the dynamic vibration absorbers. The beam is subjected to external vibrations, and both with and without the absorbers, its amplitude is measured. According to the results, adding DVAs to the beam structure significantly reduced vibration levels, particularly closer to the natural frequency of the beam. The dynamic response is greatly reduced by mass and stiffness (from, for example, 0.018m to 0.00052m). However, depending on the DVA location, this effect can change. The minimal requirements of the DVA parameters can better reduce the dynamic response if the DVA is positioned at the point of maximum displacement for each corresponding mode.

Emerging Trends in Integrated Digital Microfluidic Platforms for Next-Generation Immunoassays

Technologies based on digital microfluidics (DMF) have made significant advancements in the automated manipulation of microscale liquids and complex multistep processes. Due to their numerous benefits, such as automation, speed, cost-effectiveness, and minimal sample volume requirements, these systems are particularly well suited for immunoassays. In this review, an overview is provided of diverse DMF manipulation platforms and their applications in immunological analysis. Initially, droplet-driven DMF platforms based on electrowetting on dielectric (EWOD), magnetic manipulation, surface acoustic wave (SAW), and other related technologies are briefly introduced. The preparation of DMF is then described, including material selection, fabrication techniques and droplet generation. Subsequently, a comprehensive account of advancements in the integration of DMF with various immunoassay techniques is offered, encompassing colorimetric, direct chemiluminescence, enzymatic chemiluminescence, electrosensory, and other immunoassays. Ultimately, the potential challenges and future perspectives in this burgeoning field are delved into.

Maggot kinase: A novel and cost-effective fibrinolytic enzyme from maggots

Maggot kinase, a novel fibrinolytic enzyme source, has been isolated from fly maggots and comprehensively characterized. Using CM-52 ion exchange chromatography and affinity chromatography, we obtained a highly purified and active form of the enzyme. In particular, the fibrinolytic activity of maggot kinase, evaluated using the fibrin plate method, was found to be 8.98 ± 0.08 × 105 U/mg, demonstrating significant efficacy. Further structural analysis using mass spectrometry revealed that maggot kinase consists of a primary sequence of 226 amino acid residues with a molecular weight of 22.91 kDa. In vitro thrombolytic assays demonstrated the enzyme's remarkable ability to degrade the essential fibrinogen subunits (α, β, and γ), thereby facilitating clot lysis. Notably, our studies in a mouse model underscored the significant in vivo thrombolytic activity of maggot kinase, demonstrating its potential to inhibit thrombosis. The finding is particularly significant considering the widespread use of fly maggots in agriculture and animal husbandry due to their rapid growth cycle and minimal nutritional requirements. Our research highlights the untapped potential of fly maggots as a source for maggot kinase development for antithrombotic drug and functional food applications.

Incorporating tiny forests in the neighbourhood of Singapore - Opportunities and Challenges

‘Tiny forests’ have gained traction across the globe as a promising solution for restoring natural ecosystems and enriching biodiversity in highly urbanized areas. In land scarce cities, planting forests could mitigate the impacts of climate change and other urban socio-ecological challenges. This article investigates the potential for tiny forests to be part of the greening strategy in Singapore – a compact city that is already championing greening initiatives. Using a case study of the Jurong West neighbourhood, we conducted a survey aiming to understand people's perceptions and use of urban nature. We complement these results with a rapid assessment of land availability confirming the feasibility of implementing tiny forests in Singapore. From the survey, we found that residents were receptive to the idea of having nature closer to their residential areas despite highlighting disadvantages such as undesirable insects and animals. Factors such as minimal spatial requirements and planting methods that enhance trees growth make tiny forest a useful part of the landscaping portfolio to achieve national greening principles. Overall, we find that tiny forests have the potential to become part of the city-state's greening strategies.

High-efficiency PMS activation by difunctional Co-Fe PBA/g-C3N4 S-scheme heterojunction for oxytetracycline degradation: Performance evaluation and mechanism insight

The utilization of sulfate radical-based advanced oxidation processes (SR-AOPs) has captivated the academic community due to their minimal energy requirements and superior efficacy in peroxomonosulfate (PMS) activation for pollutant decomposition. Notwithstanding these advantages, engineering an effective and economical catalyst for PMS activation presents a considerable hurdle. In the present study, a metal-organic framework of CoFe PBA is ingeniously anchored onto g-C3N4 nanosheets, resulting in the formation of an innovative CoFe PBA/g-C3N4 S-scheme heterojunction that demonstrates remarkable efficiency in PMS activation. Intriguingly, the catalytic efficiency of CoFe PBA/g-C3N4 surpasses that of g-C3N4 and CoFe PBA by 7-fold and 2.33-fold, respectively. The heightened activity of CoFe PBA/g-C3N4 heterojunction is attributed to the enhanced charge transfer efficiency, a consequence of the successful heterojunction formation. Concurrently, the ability of photoexcited electrons to reduce Co3+/Fe3+ to Co2+/Fe2+ bolsters PMS activation. Significantly, this heterojunction retains unparalleled stability in degrading oxytetracycline without discernible performance attenuation, heralding its commendable prospects in real-world applications. Besides, mechanism exploration indicates that SO4−, h+, and electron transfer contribute to oxytetracycline degradation in the CoFe PBA/g-C3N4 system. This investigation serves as a beacon for the strategic development of highly active and stable catalysts for PMS activation, aiming at environmental decontamination.

Successful use of ultrasound guided pecto-intercostal fascial plane block to provide intraoperative analgesia in four dogs undergoing median sternotomy.

To evaluate the impact of pecto-intercostal fascial plane block on providing intraoperative analgesia in dogs undergoing median sternotomy. 4 dogs. The dogs were presented with a history of inappetence, lethargy and respiratory distress. Thoracic radiographs, point of care ultrasound, thoracocentesis, bronchoscopy and computed tomography was performed to characterize the disease. 4 male castrated, 5.3 ± 3 years old dogs weighing 19.7 ± 13.5 kg and belonging to Dalmatian, Beagle, Siberian Husky and Rottweiler breeds were included. Three dogs were diagnosed with suppurative pleural effusions because of pulmonary abscesses and one dog with spontaneous pneumothorax due to the presence of pulmonary bullae. All dogs underwent median sternotomy under general anesthesia to explore the thorax. A pecto-intercostal fascial plane block was performed by injecting local anesthetic bupivacaine in the parasternal fascial plane between the deep pectoral and external intercostal muscles to provide antinociception by anesthetizing ventral cutaneous branches of intercostal nerves second through sixth. Analgesia from the block resulted in reduced requirement of inhalant anesthesia and minimal requirement for opioid to augment analgesia intraoperatively. Median sternotomy is required to perform thoracic surgery in dogs with various thoracic pathologies. Pecto-intercostal fascial plane block is a locoregional technique that can blunt nociception arising from the ventral thorax and can significantly improve perioperative patient care in dogs undergoing median sternotomy by providing effective intraoperative and potentially postoperative analgesia.

Early Detection of Wildlife Disease Pathogens Using CRISPR-Cas System Methods.

Wildlife diseases are a considerable threat to human health, conservation, and the economy. Surveillance is a critical component to mitigate the impact of animal diseases in these sectors. To monitor human diseases, CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated protein) biosensors have proven instrumental as diagnostic tools capable of detecting unique DNA and RNA sequences related to their associated pathogens. However, despite the significant advances in the general development of CRISPR-Cas biosensors, their use to support wildlife disease management is lagging. In some cases, wildlife diseases of concern could be rapidly surveyed using these tools with minimal technical, operational, or cost requirements to end users. This review explores the potential to further leverage this technology to advance wildlife disease monitoring and highlights how concerted standardization of protocols can help to ensure data reliability.

Point-of-care test of blood Plasmodium RNA within a Pasteur pipette using a novel isothermal amplification without nucleic acid purification

BackgroundResource-limited regions face a greater burden of infectious diseases due to limited access to molecular tests, complicating timely diagnosis and management. Current molecular point-of-care tests (POCTs) either come with high costs or lack adequate sensitivity and specificity. To facilitate better prevention and control of infectious diseases in underserved areas, we seek to address the need for molecular POCTs that better align with the World Health Organization (WHO)’s ASSURED criteria—Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable to end users.MethodsA novel molecular POCT, Pasteur Pipette-assisted isothermal probe amplification (pp-IPA), was developed for malaria detection. Without any microfluidics, this method captures Plasmodium 18S rRNA in a modified Pasteur pipette using tailed genus-specific probes. After washing, the bound tailed probes are ligated to form a template for subsequent novel isothermal probe amplification using a pair of generic primers, bypassing nucleic acid extraction and reverse transcription. The method was assessed using cultured Plasmodium and compared with real-time quantitative reverse transcription PCR (RT-qPCR) or reverse transcription loop-mediated isothermal amplification (RT-LAMP) in clinical blood samples.ResultsThe entire assay is completed in 60–80 min with minimal hands-on time, using only a Pasteur pipette and a water bath. The pp-IPA’s analytical sensitivity is 1.28 × 10–4 parasites/μl, with 100% specificity against various blood-borne pathogens causing malaria-like symptoms. Additionally, pp-IPA needs only liquid-transfer skill for operation and the cost is around USD 0.25 per test, making it at least 300 times lower than mainstream POCT platforms.ConclusionsDesigned to improve the accessibility of molecular detection in resource-limited settings, pp-IPA’s simplicity, affordability, high sensitivity/specificity, and minimal equipment requirements make it a promising point-of-care pathogen identification tool in resource-constrained regions.Graphical

Illuminating Substrate Preferences of Promiscuous F420H2-Dependent Dehydroamino Acid Reductases with 4-Track mRNA Display.

Stereoselective reduction of dehydroamino acids is a common biosynthetic strategy to introduce d-amino acids into peptidic natural products. The reduction, often observed during the biosynthesis of lanthipeptides, is performed by dedicated dehydroamino acid reductases (dhAARs). Enzymes from the three known dhAAR families utilize nicotinamide, flavin, or F420H2 coenzymes as hydride donors, and little is known about the catalysis performed by the latter family proteins. Here, we perform a bioinformatics-guided identification and large-scale in vitro characterization of five F420H2-dependent dhAARs. We construct an mRNA display-based pipeline for ultrahigh throughput substrate specificity profiling of the enzymes. The pipeline relies on a 4-track selection strategy to deliver large quantities of clean data, which were leveraged to build accurate substrate fitness models. Our results identify a remarkably promiscuous enzyme, referred to as MaeJC, that is capable of installing d-Ala residues into arbitrary substrates with minimal recognition requirements. We integrate MaeJC into a thiopeptide biosynthetic pathway to produce d-amino acids-containing thiopeptides, demonstrating the utility of MaeJC for the programmable installation of d-amino acids in ribosomal peptides.

Developing an Evidence-Based Primary Cancer Prevention Repository: insights from the PIECES project

Abstract Introduction Up to 40% of cancer cases could be avoided through effective primary prevention. However, cancer prevention tends to be fragmented across Europe, leading to geographical health differences. The dissemination and scale-up of effective primary cancer prevention interventions may address this. Although effective cancer prevention programs exist, finding them and their materials (especially regarding implementation) can be challenging. Repositories of evidence-based interventions provide insight into “what is out there” and can support dissemination within and across national borders. The PIECES project aims to develop and disseminate a repository of evidence-based primary cancer prevention programs adaptable to European contexts, with information to aid adaptation and implementation across countries. Methods The development of the PIECES repository has been informed by established repositories such as NCI’s Evidence-Based Cancer Control Programs. The project team developed a scoring sheet with a selection of items that could be included in the repository. All participants rated each item on relevance and applicability and provided comments and suggestions. A final framework, including a set of minimal requirement items, was agreed upon during three online consensus building meetings. Results We created a repository of evidence-based primary cancer prevention interventions targeting modifiable risk factors suitable for implementation in European healthcare settings. This 22-item repository provides information about an intervention’s logic model, implementation practices, and appraisal of published evidence of effectiveness. Conclusions This repository is innovative as it includes practical information and tools to facilitate implementation. While initially built to serve the PIECES project, the repository is designed to be a valuable tool for European policymakers, researchers, and implementers. Lessons learned from this process will inform future developers. Key messages • A comprehensive cancer prevention intervention repository avoids “reinventing the wheel” and allows for more efficient use of existing interventions by building upon the best available evidence. • The ongoing efforts of the PIECES project represent a significant contribution to reduce cancer prevention disparities and to facilitate the adoption of evidence-based interventions in Europe.

Pyrene‐Based Self‐Assembled Monolayer with Improved Surface Coverage and Energy Level Alignment for Perovskite Solar Cells

AbstractRecently, the efficiency of p‐i‐n perovskite solar cells drastically increased, a pivotal factor being the incorporation of self‐assembled monolayers (SAMs) as a hole‐transporting layer (HTL). SAMs offer many advantages over conventional HTLs, including minimal material requirements, low cost, and facile processing. Current research is mainly focused on the development of carbazole‐derived SAMs. However, the versatility of organic chemistry allows for the design of SAMs with alternative organic cores that may possess specific benefits. In this study, three novel SAMs are incorporated in p‐i‐n perovskite solar cells, each based on an aromatic core commonly used in organic semiconductors. The novel SAMs vary in their energy level alignment with the perovskite active layer. Optimal alignment is achieved with a pyrene‐based SAM (4PAPyr), resulting in solar cells that outperform the commercially available 2PACz. Moreover, due to improved surface coverage, the use of 4PAPyr leads to a significantly higher number of working solar cell devices when compared to 2PACz, which is of particular interest with regard to upscaling. After device optimization, a power conversion efficiency of 22.2% is achieved with 4PAPyr. This research underlines the importance of diversifying SAMs to unlock further advancements in perovskite solar cell efficiency and scalability.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

Bias mitigation for fair automation of classification tasks

AbstractThe incorporation of machine learning algorithms into high‐risk decision‐making tasks has raised some alarms in the scientific community. Research shows that machine learning‐based technologies can contain biases that cause unfair decisions for certain population groups. The fundamental danger of ignoring this problem is that machine learning methods can not only reflect the biases present in our society but could also amplify them. This article presents the design and validation of a technology to assist the fair automation of classification problems. In essence, the proposal is based on taking advantage of the intermediate solutions generated during the resolution of classification problems through using Auto‐ML tools, in particular, AutoGOAL, to create unbiased/fair classifiers. The technology employs a multi‐objective optimization search to find the collection of models with the best trade‐offs between performance and fairness. To solve the optimization problem, we introduce a combination of Probabilistic Grammatical Evolution Search and NSGA‐II. The technology was evaluated using the Adult dataset from the UCI repository, a common benchmark in related research. Results were compared with other published results in scenarios with single and multiple fairness definitions. Our experiments demonstrate the technology's ability to automate classification tasks while incorporating fairness constraints. Additionally, our method achieves competitive results against other bias mitigation techniques. A notable advantage of our approach is its minimal requirement for machine learning expertise, thanks to its Auto‐ML foundation. This makes the technology accessible and valuable for advancing fairness in machine learning applications. The source code is available online for the research community.

Inductive Frequency-Coded Sensor for Non-Destructive Structural Strain Monitoring of Composite Materials

Structural composite materials have gained significant appeal because of their ability to be customized for specific mechanical qualities for various applications, including avionics, wind turbines, transportation, and medical equipment. Therefore, there is a growing demand for effective and non-invasive structural health monitoring (SHM) devices to supervise the integrity of materials. This work introduces a novel sensor design, consisting of three spiral resonators optimized to operate at distinct frequencies and excited by a feeding strip line, capable of performing non-destructive structural strain monitoring via frequency coding. The initial discussion focuses on the analytical modeling of the sensor, which is based on a circuital approach. A numerical test case is developed to operate across the frequency range of 100 to 400 MHz, selected to achieve a balance between penetration depth and the sensitivity of the system. The encouraging findings from electromagnetic full-wave simulations have been confirmed by experimental measurements conducted on printed circuit board (PCB) prototypes embedded in a fiberglass-based composite sample. The sensor shows exceptional sensitivity and cost-effectiveness, and may be easily integrated into composite layers due to its minimal cabling requirements and extremely small profile. The particular frequency-coded configuration enables the suggested sensor to accurately detect and distinguish various structural deformations based on their severity and location.

Characterizing tandem repeat complexities across long-read sequencing platforms with TREAT and otter.

Tandem repeats (TRs) play important roles in genomic variation and disease risk in humans. Long-read sequencing allows for the accurate characterization of TRs; however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterization, visualization, and analysis across multiple genomes. In a comparison with existing tools based on long-read sequencing data from both Oxford Nanopore Technology (ONT, Simplex and Duplex) and Pacific Bioscience (PacBio, Sequel II and Revio), otter and TREAT achieve state-of-the-art genotyping and motif characterization accuracy. Applied to clinically relevant TRs, TREAT/otter significantly identify individuals with pathogenic TR expansions. When applied to a case-control setting, we replicate previously reported associations of TRs with Alzheimer's disease, including those near or within APOC1 (P = 2.63 × 10-9), SPI1 (P = 6.5 × 10-3), and ABCA7 (P = 0.04) genes. Finally, we use TREAT/otter to systematically evaluate potential biases when genotyping TRs using diverse ONT and PacBio long-read sequencing data sets. We show that, in rare cases (0.06%), long-read sequencing from coverage drops in TRs, including the disease-associated TRs in ABCA7 and RFC1 genes. Such coverage drops can lead to TR misgenotyping, hampering the accurate characterization of TR alleles. Taken together, our tools can accurately genotype TRs across different sequencing technologies and with minimal requirements, allowing end-to-end analysis and comparisons of TRs in human genomes, with broad applications in research and clinical fields.

Comparison of deep and conventional machine learning models for prediction of one supply chain management distribution cost

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of supply chain management distribution cost (SCMDC) is one branch of SCM and it’s essential for organizations striving to optimize performance and attain their goals. For this purpose, four machine learning algorithms, including random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and decision tree (DT), along with deep learning using convolutional neural network (CNN), was used to predict and analyze SCMDC. A comprehensive dataset consisting of 180,519 open-source data points was used for analyze and make the structure of each algorithm. Evaluation based on Root Mean Square Error (RMSE) and Correlation coefficient (R2) show the CNN model has high accuracy in SCMDC prediction than other models. The CNN algorithm demonstrated exceptional accuracy on the test dataset, with an RMSE of RMSE of 0.528 and an R2 value of 0.953. Notable advantages of CNNs include automatic learning of hierarchical features, proficiency in capturing spatial and temporal patterns, computational efficiency, robustness to data variations, minimal preprocessing requirements, end-to-end training capability, scalability, and widespread adoption supported by extensive research. These attributes position the CNN algorithm as the preferred choice for precise and reliable SCMDC predictions, especially in scenarios requiring rapid responses and limited computational resources.

A new optimization framework for piezoelectric actuators location identification

The study addresses piezoelectric actuator placement for active vibration control using an open-access framework that couples ANSYS and Python. It outlines a three-stage procedure: creating a finite element model in ANSYS APDL, using Python to determine the objective function, and applying a genetic algorithm optimization to find optimal actuator locations. Numerical simulations on plates and thin-walled conical structures demonstrate the effectiveness of the approach, showing it efficiently finds optimal placements with minimal search time and energy requirements. The study provides open-access codes for the framework, enhancing its accessibility and reproducibility.

Dahlia ball shape bimetallic phosphide-tungsten phosphide composite for anion-exchange membrane water electrolysis

Revolutionizing Water Splitting: Introducing a Breakthrough Single Heterostructure Catalyst for Highly Efficient Hydrogen and Oxygen Evolution Reactions. In this study, we present an innovative method for water splitting that introduces a remarkable single heterostructure catalyst capable of efficiently catalyzing both the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Our research utilizes a highly effective hydrothermal synthesis technique to create a unique nanosphere composed of iron cobalt phosphide and tungsten phosphide (FCPWP). This FCPWP nanosphere exhibits finely tuned electronic structures and incorporates multiple active sites, resulting in significantly reduced overpotentials. When operating at current densities of 50 and 100 mA cm−2, the HER overpotentials are as low as 220 mV and 280 mV, respectively, while the OER overpotentials are only 270 mV and 350 mV. By employing the FCPWP nanosphere in a two-electrode electrolyzer configuration, we achieve exceptional results with minimal power requirements. At an operating current density of 10 mA cm−2, the electrolyzer functions at an impressively low voltage of 1.44 V, and at 1.85 V, it attains a high current density of 425 mA cm−2, showcasing an outstanding cell efficiency of 71.63% in an anion exchange membrane electrolyzer. These experimental findings are further supported by computational study. The FCPWP nanosphere emerges as an extraordinary electrocatalyst with dual-functionality for water splitting, offering great promise in the efficient production of hydrogen through electrochemical means. Our research not only introduces a fresh perspective but also paves the way for significant advancements in renewable energy technologies.