Active Area Research Articles

Comparison of Reinforcement Learning Algorithms for Edge Computing Applications Deployed by Serverless Technologies

Edge computing is one of the technological areas currently considered among the most promising for the implementation of many types of applications. In particular, IoT-type applications can benefit from reduced latency and better data protection. However, the price typically to be paid in order to benefit from the offered opportunities includes the need to use a reduced amount of resources compared to the traditional cloud environment. Indeed, it may happen that only one computing node can be used. In these situations, it is essential to introduce computing and memory resource management techniques that allow resources to be optimized while still guaranteeing acceptable performance, in terms of latency and probability of rejection. For this reason, the use of serverless technologies, managed by reinforcement learning algorithms, is an active area of research. In this paper, we explore and compare the performance of some machine learning algorithms for managing horizontal function autoscaling in a serverless edge computing system. In particular, we make use of open serverless technologies, deployed in a Kubernetes cluster, to experimentally fine-tune the performance of the algorithms. The results obtained allow both the understanding of some basic mechanisms typical of edge computing systems and related technologies that determine system performance and the guiding of configuration choices for systems in operation.

Enhancing the Phase Crystallinity of Buried Layer Perovskites through Organic Salt Molecule‐Assisted Crystal Growth

The performance and stability of perovskite solar cells rely crucially on the purity of their active perovskite phase. While the two‐step method has emerged as a well‐known technique for fabricating high‐performance cells, it suffers from significant PbI2 phase impurities at the buried layer due to inefficient diffusion of cationic molecules into the preprepared PbI2 layer. Herein, a simple yet highly effective method is presented to boost phase purity within the buried layer by introducing formamidinium iodide (FAI) seed molecules into the underlying PbI2 layer. X‐Ray diffraction analysis result reveals that this process significantly reduces the PbI2 phase and enhances the purity of the perovskite's phase. It is also observed that this technique can produce perovskite layer with a remarkably smooth surface structure and large interconnected crystal grains, forming a continuous layer. These characteristics are subjected to further enhancement when hexamethylenetetramine molecules are concurrently introduced with FAI into the PbI2 layer. Solar cells fabricated using this method, with an active area of 0.1 cm2, achieve a remarkable power conversion efficiency of up to 24.52% with Voc as high as 1.18 V, representing a substantial improvement over cells produced using the standard two‐step method, which attains only 22.18% efficiency. With its simple yet impactful approach, the present method should find widespread adoption in the production of high‐performance perovskite solar cells.

Breathing Life Into Meta-Analytic Methods.

Meta-analyses have become indispensable in the behavioral sciences, combining and summarizing data from multiple studies. While they offer many advantages (e.g., increased power, higher generality, and resolving conflicting findings), they currently only provide a snapshot at a given point. In active research areas, frequent meta-analytic updates are necessary to incorporate new evidence. We propose guidelines for live, dynamic meta-analyses and introduce an accessible tool using the R environment. Our app, powered by the Shiny package, enables the meta-analyst to integrate evidence interactively as an update of an existing meta-analysis or from scratch (i.e., a new meta-analysis). By embracing dynamic meta-analyses and leveraging modern tools, researchers can ensure up-to-date meta-analyses in their respective fields.

Steering Geometric Reconstruction of Bismuth with Accelerated Dynamics for CO2 Electroreduction

AbstractBismuth‐based materials have emerged as promising catalysts in the electrocatalytic reduction of CO2 to formate. However, the reasons for the reconstruction of Bi‐based precursors to form bismuth nanosheets are still puzzling, especially the formation of defective bismuth sites. Herein, we prepare bismuth nanosheets with vacancy‐rich defects (V‐Bi NS) by rapidly reconstructing Bi19Cl3S27 under negative potential. Theoretical analysis reveals that the introduction of chlorine induces the generation of intrinsic electric field in the precursor, thereby increasing the electron transfer rate and further promoting the metallization of trivalent bismuth. Meanwhile, experimental tests verify that Bi19Cl3S27 has a faster reconstruction rate than Bi2S3. The formed V‐Bi NS exhibits up to 96 % HCOO− Faraday efficiency and 400 mA cm−2 HCOO− partial current densities, and its electrochemical active surface area normalized formate current density and yield are 2.2 times higher than those of intact bismuth nanosheets (I‐Bi NS). Density functional theory calculations indicate that bismuth vacancies with electron‐rich aggregation reduce the activation energy of CO2 to *CO2− radicals and stabilize the adsorption of the key intermediate *OCHO, thus facilitating the reaction kinetics of formate production.

Enhanced hydrogen evolution reaction activity through samarium-doped nickel phosphide (Ni2P) electrocatalyst

Hydrogen evolution reaction (HER) stands out among conventional hydrogen production processes by featuring excellent advantages. However, the uncompetitive production cost due to the low energy efficiency has hindered its development, necessitating the introduction of cost-effective electrocatalysts. In this study, we introduced samarium doping as a high-potential approach to improve the electrocatalytic properties of nickel phosphide (Ni2P) for efficient HER. Samarium-doped Ni2P was synthesized via a facile two-step vapor–solid reaction technique. Different physical and electrochemical analyses showed that samarium doping significantly improved pure Ni2P characteristics, such as particle size, specific surface area, electrochemical hydrogen adsorption, intrinsic activity, electrochemical active surface area, and charge transfer ability in favor of HER. Namely, Ni2P doped with 3%mol of samarium (Sm0.03Ni2P) with a Tafel slope of 67.8 mV/dec. and overpotential of 130.6 mV at a current density of 10 mA/cm2 in 1.0 M KOH solution exhibited a notable performance, suggesting Sm0.03Ni2P and samarium doping as a remarkable electrocatalyst and promising promoter for efficient HER process, respectively.

Stretchable Sweat Lactate Sensor with Dual-Signal Read-outs.

Innovations in wearable sweat sensors hold great promise to provide deeper insights into molecular level health information non-invasively. Lactate, a key metabolite present in sweat, holds immense significance in assessing physiological conditions and performance in sports physiology and health sensing. This paper presents the development and characterization of stretchable electrodes with ultrahigh active surface area of 648% for lactate sensing. The as-printed stretchable electrodes were functionalized with an electron transfer layer comprising of Toluidine Blue O and multi-walled carbon nanotubes (MWCNTs), and an enzymatic layer consisting of lactate dehydrogenase with β-Nicotinamide adenine dinucleotide as the cofactor for lactate selectivity. This sensor achieves a dual-signal read-out in which both electrochemical and fluorescence signals were obtained during lactate detection, demonstrating promising sensor performance in terms of sensitivity and reliability. We demonstrate the robustness of the dual-signal sensor under simulated conditions of physical deformation and shifted excitation. Under these compromised conditions, the performance of the stretchable electrodes remained largely unaffected, showcasing their potential for robust and adaptable sensing platforms in wearable health monitoring applications.

Large-Area Perovskite Solar Modules Based on Uniformity Engineering of Perovskite Films: The Critical Role of Methyldiphenylphosphine Oxide Additive.

Perovskite solar cells (PSCs) have led to distinguished achievements and become one of the state-of-the-art photovoltaic technologies. Undoubtedly, reliable preparation of large area high-quality perovskite (PVK) films with uniform optoelectronic properties has become a critical and challenging task to transition PSCs from lab to market. Here, methyldiphenylphosphine oxide (MDPPO) is employed as an additive in a PVK precursor solution to promote uniform conductivity and carrier transport of PVK films. More important, to check its compatibility with the upscaling process, the MDPPO additive strategy was further applied to doctor-blade large-area PVK films. As a result, benefit from the favorable role of MDPPO additive, the power conversion efficiencies (PCEs) of small-area PSCs reach 23.85% with superb open circuit voltage (Voc) of 1.15 V and fill factor of 81.21%, while an impressive PCE of 19.22% was achieved for the large-area PSC minimodules with active area of 61.48 cm2. Remarkably, the MDPPO modified device exhibits significantly improved operational stability, maintaining an initial efficiency of 68% even after 750 h under continuous 1-sun illumination. Our achievements will provide profound insight and further guidance for the scale-up process of PSCs from lab to large-scale modules.

Simultaneous removal of heavy metals and electricity generation from wastewater in constructed wetland-microbial fuel cells

Constructed wetlands (CWs) are environmentally friendly and cost-effective methods for wastewater treatment. Recently, there have been innumerable efforts to integrate CWs with microbial fuel cells (MFCs) to enhance the removal of pollutants while generating electricity. Constructed Wetland-Microbial Fuel Cells (CW-MFCs) are engineered systems incorporating physical, chemical, and biological processes for wastewater treatment. However, the application of CW-MFCs to remove multiple heavy metals from wastewater is still an active area of research. The present study explores for the first time the utilization of different types of CW-MFCs, including CW-MFC-planted, CW-MFC-unplanted, and CW-sand-filter for simultaneous removal of different concentrations of organic pollutants and multiple heavy metals such as zinc (Zn), cadmium (Cd), copper (Cu), and lead (Pb) from wastewater while generating electricity. The study employed nine CW-MFC microcosms, some of which were planted with Phragmites australis. The initial concentrations of Cu, Pb, Zn, and Cd were maintained at 2, 10, and 30 mg/l, respectively, while initial COD concentrations were 120, 500, and 1000 mg/l. Remarkably, the maximum removal rates achieved were 98.83 %, 95.74 %, 92.91 %, and 90.75 % for Cu, Pb, Zn, and Cd, respectively, and the maximum removal rate of COD was 97.96 % at 10 mg/l of Cu, Pb, Zn, Cd, and 500 mg/l of COD in CW-MFC-planted. The attained highest voltage, current, and power densities reached 687 mV, 4000 mA/m3, and 785.86 mW/m3, respectively, in CW-MFC-planted. This study demonstrates the significant impact of CW-MFC-planted systems on wastewater treatment and electricity generation.

Performance of simplified methods for quantification of [18F]NaF uptake in fibrodysplasia ossificans progressiva

BackgroundFibrodysplasia Ossificans Progressiva (FOP) is a rare, genetic disease in which heterotopic bone is formed in muscles, tendons and ligaments throughout the body. Disease progression is variable over time and between individuals. 18F-fluoride uptake in newly formed bone can be evaluated using [18F]NaF (i.e., sodiumfluoride) PET/CT, identifying active areas of bone formation in FOP. The purpose of this study was to assess the performance of various semi-quantitative methods with full kinetic analysis.ResultsSeven patients (age range: 20–31 years) with FOP underwent dynamic [18F]NaF scans at baseline and after one year. [18F]NaF uptake was measured in aorta descendens, vertebrae, heterotopic bone lesions and metabolically active regions on PET, and quantified using nonlinear regression (NLR) analysis together with standardized uptake value (SUV) and target-to-blood ratio (TBR). SUV was on measured the 40–45 min frame of the dynamic sequence (SUV40–45) and on the subsequent static sweep (SUVStatic). Correlations between and SUV40–45 and NLR-derived Ki were comparable when normalized to body weight (r = 0.81, 95% CI 0.64–0.90), lean body mass (r = 0.79, 95% CI 0.61–0.89) and body surface area (r = 0.84, 95% CI 0.70–0.92). Correlation between TBR40–45 and NLR-derived Ki (r = 0.92, 95% CI 0.85–0.96) was higher than for SUV40–45. Correlation between TBR40–45 and NLR-derived Ki was similar at baseline and after one year (r = 0.93 and 0.94). The change in TBR40–45 between baseline measurement and after one year correlated best with the change in NLR-derived Ki in the PET-active lesions (r = 0.87).ConclusionThe present data supports the use of TBR for assessing fluoride uptake in PET-active lesions in FOP.Clinical trial registrationSub-study of the Lumina-1 trial (clinicaltrials.gov, NCT03188666, registered 13-06-2017).

Parametric Study of a Large Active Area PEM Fuel Cell using a MatLab-Simulink Model

A Matlab-Simulink based mathematical model and simulation method is developed by applying appropriate physics and electro-chemistry that adequately describes a proton exchange membrane fuel cell (PEMFC) system. The mathematical model is then simulated for a single low temperature PEMFC with a large, 350cm2, active area. The single cell’s performance was evaluated using different operating conditions and varying values of various crucial system parameters. The Matlab-Simulink model presented in this investigation is used to study various parameters’ effect such as the cell temperature from 50℃ to 90℃, inlet pressure, relative humidity (RH) from 70% to 90%, and the reactant stoichiometry ratio, on the cell performance. Comparison of polarization and power density curves were conducted between the model simulation and experimental data at 60℃ and at 65℃. Except for the higher current densities range (≥1.6 A/cm2), the simulation results match with the experimental results very well. The parametric study was used to determine the optimum value of various parameters. The fuel cell performance with the optimized parameter values show a good improvement using the developed MatLab-Simulink model.

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15-3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL-1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15-3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15-3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics.

Analysis of Neuropeptides in the Intestinal Mucus of Patients with Ulcerative Colitis Using RNA Sequencing.

Inflammation in ulcerative colitis (UC) originates in the colorectal mucosa. Transcriptome sequencing analysis of the colorectal mucosa allows the identification of potential neuropeptides related to local neurotransmission. The intestinal mucus lining the surface of the mucosa may harbor biomarkers of mucosal inflammation; however, this has not been sufficiently investigated, given the difficulty in obtaining human samples. We previously reported the feasibility of obtaining mucin samples for proteomic analysis by brushing during colonoscopy. Herein, we aimed to investigate the composition of the intestinal mucus and detect neuropeptides characteristic of UC. Mucus and mucosal samples were collected from patients with UC from the colorectum in areas showing remission or active UC using a brush catheter and biopsy forceps during colonoscopy. RNA sequencing findings of mucus samples of active and remission areas were compared. RNA and protein expression levels of significantly upregulated neuropeptides were analyzed. Of the neuropeptides associated with UC, somatostatin (SST) was significantly elevated in areas of remission, according to RNA sequencing results of mucus and expression levels in mucus RNA and proteins. Conversely, SST expression in the mucosa was increased in the inflamed areas. Flow cytometry revealed that the fluorescence intensity of SST-positive cells in the remission zone was higher in the mucus than in the mucosa. SST expression in the mucus is considered to be an important factor associated with UC activity.

Heterobimetallic Synergism in Triple-Redox 2D Framework for Largely Boosted Water Oxidation and Flanked Carboxylic-Acid-Triggered Unconventional Tandem Catalysis.

A fish-bone-shaped and thermochemically stable 2D metal-organic framework (MOF) with multimodal active center-decked pore-wall is devised. Redox-active [Co2(COO)4] node and thiazolo[5,4-d]thiazole functionalization benefit this mixed-ligand MOF exhibiting electrochemical water oxidation with 375mV overpotential at 10mA cm-2 current density and 78mV per dec Tafel slope in alkaline medium. Pair of oppositely oriented carboxylic acids aids postmetalation with transition metal ions to engineer heterobimetallic materials. Notably, overpotential of Ni2+ grafted triple-redox composite reduces to 270mV with twofold declined Tafel slope than the parent MOF, ranking among the best-reported values, and outperforming majority of related catalysts. Significantly, turnover frequency and charge transfer resistance display 35.5 and 1.4-fold upsurge, respectively, with much uplifted chronopotentiometric stability and increase active surface area owing to synergistic Co(II)-Ni(II) coupling. The simultaneous presence of ─COOH and nitrogen-rich moieties renders this hydrogen-bonded MOF as acid-base synergistic catalyst for recyclable deacetalization-Knoevenagel reaction with >99% product yield under solvent-free mild condition. Besides control experiments, unique role of ─COOH as hydrogen-bond donor site in substrate activation is validated from comparing the performances of molecular-shearing approach-derived structurally similar unfunctionalized MOF, and the heterobimetallic composite. To the best of tandem Knoevenagel condensation, larger-sized acetal exhibits poor yield of α,β-unsaturated dicyanides, and demonstrates pore-fitting-mediated size-selectivity.

“Unveiling marigold assembled micro flowers of tungsten oxide towards solid-state flexible pouch and coin cell supercapacitors”

Marigold analogues micro flowers of tungsten oxide (WO3) have been grown in thin film form through simple and cost-effective solution chemistry approach on stainless steel substrate. Aqueous precursor involving WO4-2 ions agglomerated as self-sacrificing template growing initially into the nano-petal, followed by self-assembly; leading to marigold analogues micro flower surface architecture. This enthralling morphology motivated us not only to fabricate supercapacitive electrode but also to design complete solid-state supercapacitor devices in dual configurations: flexible pouch cell and coin cell. Interestingly, both devices even in symmetric configuration yields remarkable potential window of 1.82 V when sandwiched by gel inclusive of Li+ ions dispersed in non-conducting polyvinyl alcohol matrix. Solid-state flexible pouch cell and coin cell delivered specific capacitances of 103.98 ± 3.59 and 30.09 ± 1.03 F/g respectively at a scan rate of 5 mV/s. Assembled electrode, coin-cell and flexible pouch-cells have been well assessed in-depth through specific capacitances using cyclic voltammetry and galvanostatic charge discharge, diffusive and capacitive contributions, mechanical bending tests, electrochemical active surface area, and electrochemical impedance analysis. Practical applicability has been demonstrated for designed flexible pouch cell to run small fan and light emitting diode panel whereas coin cell to run light emitting diode panel.

0D/2D CuCo2O4/Ti3C2 hybrid nanostructure as anodic nanocatalyst for direct power generation in fuel cells: Investigating annealing temperature and synergetic effect

In the present work, CuCo2O4 nanoparticles, CuCo2O4/Ti3C2, CuO/Ti3C2, and Co3O4/Ti3C2 hybrid nanostructures were synthesized. Various characterization and electrochemical methods were used to investigate the effect of annealing temperature on the catalytic properties. Based on the results, the hybrid of Ti3C2 MXene nanosheets with CuCo2O4 nanoparticles improved the active surface area and kinetics of electron transfer. The annealing temperature through affecting the crystalline structure, leads to a change in the electrocatalytic performance of nanostructures. On the other, the CuCo2O4/Ti3C2 hybrid nanostructure shows better performance than the single metal oxides (CuO and Co3O4) due to the synergistic effect of Cu and Co. Therefore, CuCo2O4/Ti3C2(200) hybrid nanostructure can be used as an efficient nanocatalyst in the electrooxidation process of alcohols in fuel cells due to its higher electrochemical surface area, higher current density (42 mA cm−2) and better stability (83.3 %) compared to other structures.

Alkaline electrochemical oxygen reduction boosted by trimetallic palladium–copper–gold nanoparticles

A Pd-Cu-Au/C nanocatalyst with a low content of Cu (8.2 at %) and Au (4.4 at %), and an average crystallite size of about 2 nm was synthesized by employing a one-pot two-steps borohydride reduction method in the presence of sodium citrate, characterized by energy-dispersive X-ray spectroscopy, X-ray diffraction, transmission electron microscopy and X-ray photoelectron spectroscopy and examined for the oxygen reduction reaction (ORR) in an alkaline solution. Pd/C and Pd-Cu/C catalyst with 17 at % Cu, which were synthesized by the same method, and a commercial Pt/C were used for comparison. In addition, Pd-Cu-Au/C and Pd-Cu/C nanocatalysts were treated in acid with a purpose of their dealloying. The electrochemically active surface area of Pd was determined from COads desorption. It was found that the ORR on all catalysts follows first order kinetics with respect to O2 with four electrons transferred per O2 molecule. The catalysts were subjected to a short-term cycling stability test. Pd-Cu/C and Pd-Cu-Au/C were more active for ORR than Pd/C and Pt/C. The acid-treated catalysts exhibited higher activity compared to their untreated counterparts; both initially and after the stability test (up to 1.7 times in specific activity and 2 times in mass activity). The catalyst with the highest specific and mass activity after the stability test was the acid-treated Pd-Cu-Au/C which surpassed Pd/C by a factor of up to 2.5 in specific activity and 5.7 in mass activity. It was concluded that the acid treatment leads to a major dissolution of Cu from the nanoparticles, thus leaving a highly disordered Pd structure exhibiting increased activity for the ORR, while Au stabilizes Pd structure contributing to better performance after the stability test.

Dual-responsive electrochemical immunosensor for CYFRA21-1 detection based on Au/Co Co-loaded 3D ordered macroporous carbon interconnected framework

Cytokeratin 19 fragment antigen 21–1 (CYFRA21–1) is a protein fragment released into the bloodstream during the death of lung epithelial cells, serving as a predictive biomarker in diagnosing non-small cell lung cancer (NSCLC) and need to be accurately detected. Herein, a dual-responsive label-free electrochemical immunosensor was developed based on a three-dimensional ordered interconnecting macroporous carbon skeleton material modified with gold-cobalt nanoparticles (Au/Co NPs-3D MCF) to detect cytokeratin-19 fragment (CYFRA21–1). The three-dimensional ordered interconnect macroporous structure, by providing a high specific surface area and an electrochemically active area, not only enhances the electron transport channel and reduces mass transfer resistance, but also offers a confined region that elevates the collision frequency with the active site. In addition to exhibiting excellent biocompatibility for antibody binding, gold-cobalt nanoparticles contribute significantly to the overall robustness of the immunosensor. By capitalizing on the 3D network structure and collective effect of Au and Co NPs, the Au/Co NPs-3D MCF immunosensors exhibit exceptional response signals in both chronocurrent testing and square-wave voltammetry, allowing for a wide linear response range of 0.0001–100 ng/mL and a low detection limit. Moreover, the constructed immunosensor is capable of detecting CYFRA21–1 in human serum and has the potential for further extension to detect multiple biomarkers. This work opens up new avenues for the construction of other highly selective 3D network immunosensors.

Modeling the dependence of electrolyte design on lithium-sulfur battery performance

Herein, we offer two electrochemical models, a zero-dimensional (0-D) and a one-dimensional (1-D) model, to mechanistically explore the impact of the electrolyte-to-sulfur (E/S) ratio and electrolyte properties on the discharge behavior of a lithium-sulfur (Li-S) cell. The electrochemically active area in both models is defined based on the carbon weight fraction and reference porosity, so the influence of the carbon loading and E/S ratio on the reaction kinetics is included. Mass transfer of the species must be considered to model the reliance of the discharge capacity on the current density. Instead, the 0-D model is more responsive to the influence of the E/S ratio on the discharge capacity. The effect of electrolyte properties can be modeled implicitly through model parameters. The 0-D model is less sensitive to the variance in model parameters than the 1-D model in terms of the predicted capacity.

Effects of electrochemical active surface area of Cu on electrochemical CO2 reduction in acidic electrolyte using Cu nanoparticles on surfactant-treated carbon

Electrochemical carbon dioxide reduction reaction (CO2RR) presents a viable approach for transforming CO2 into valuable products. Traditional employment of neutral or alkaline electrolytes in CO2RR aims to avoid the hydrogen evolution reaction (HER). However, the usage of such electrolytes has intrinsic limitations on the availability of CO2 due to homogeneous equilibrium reactions, resulting in high energy costs for electrolyte regeneration. Lowering the pH of electrolytes can mitigate this concern and facilitate industrially relevant efficiencies. While recent studies show promising results for CO2RR in acidic environments using flow cells, the translation of conventional electrocatalysts from neutral or alkaline conditions to acidic conditions remains unclear. Hence, our investigation focuses on assessing the impact of surfactant-modified synthesis methods on CO2RR performance in acidic electrolytes.We first synthesized Cu nanoparticles on surfactant-treated carbon black by employing various surfactants: hexadecyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate, and Triton X-100, representing cationic, anionic, and nonionic surfactants, respectively. The objective of this synthesis was to investigate how surfactants influence the size and distribution of Cu nanoparticles (Cu NPs), the electrochemical active surface area (ECSA) of Cu, and, consequently, the performance of CO2RR in acidic electrolytes. The findings reveal that Cu NPs on carbon black show a different profile of size and distribution depending on the surfactant used for synthesis, resulting in varied ECSA. In addition, ECSA plays a crucial role in the production of C2H4 in an acidic medium, determining the onset potential and production rate of C2H4 while maintaining original Cu activity. In summary, surfactant treatment not only controls the size but also enables the uniform distribution of Cu NPs on carbon black. Consequently, Cu NPs exhibiting larger ECSA demonstrate enhanced efficiency in the production of C2H4 compared to counterparts with smaller ECSA under acidic environment.

Pd nanoparticles supported on silver, zinc, and reduced graphene oxide as a highly active electrocatalyst for ethanol oxidation

In this manuscript, we present a facile approach to synthesize a highly active catalyst for the electro-oxidation of ethanol using Pd nanoparticles supported on a Ag–Zn alloy on reduced graphene oxide. This nanocomposite material was fabricated via a two-step process of incineration followed by sol-gel synthesis. The physical properties of the catalyst were evaluated using X-ray diffraction, X-ray photoelectron spectroscopy, and scanning and transmission electron microscopies. To understand the catalyst's activity for ethanol oxidation, we performed a series of cyclic voltammetry and electrochemical impedance spectroscopy experiments in alkaline medium. The catalyst exhibited good catalytic activity with a current density of 13.01 mA cm−2, a value seven times higher than the commercial standard Pd/C catalyst and higher than any other previously reported Pd-based catalyst. The results were attributed synergistic effects between the Pd and alloy support along with the large electrochemically active surface area of the material. Taken together, these results suggest that ethanol electrocatalysts based on nanocomposites of Pd, Ag, and Zn is promising candidates for future ethanol fuel cells.