Electrochemical science advances最新文献

Lactate is a useful analytical indicator in various fields. The lactate monitoring benefits from evaluating the body's condition, as excessive muscle use or fatigue can result in injury. Further, it is useful for alerting to emergencies like haemorrhage, hypoxia, respiratory distress, and sepsis. Additionally, the determination of the food's lactate level is very important in examining freshness, storage stability, and fermentation degree. Given such benefits, the determination of lactate in various samples has been widely explored, especially using electrochemical sensor technology. Despite enzymatic sensors being the focus of numerous studies, enzyme-free platforms have gained focus over the last few years to address the matter of enzyme stability. This review article respectfully offers an overview of the concepts, applications, and recent advances of electrochemical lactate detection platforms. A comparison of hot research for enzymatic and enzyme-free lactate sensors in terms of electrode surface engineering, enzymes and their immobilisation matrices, and several analytical parameters, including linear dynamic range, the limit of detection, sensitivity, and stability, have been discussed. In addition, future perspectives have been highlighted in this review.

In the first part of this study, double layer (DL) capacitances of plane and porous electrodes were related to electrochemical active surface areas based on electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) measurements. Here, these measured data are described with equivalent circuit models (ECMs), aiming to critically assess the ambiguity, reliability, and pitfalls of the parametrization of physicochemical mechanisms. For microstructures and porous electrodes, the resistive–capacitive contributions of DL in combination with resistively damped currents in pores are discussed to require the complexity of convoluted transmission line ECMs. With these ECMs, the frequency-dependencies of the capacitances of porous electrodes are elucidated. Detailed EIS or CV data-based reconstructions of complex microstructures are discussed as impossible due to the blending of individual structural features and the related loss of information. Microstructures in combination with charge transfer reactions and weakly conducting parts require parameter-rich ECMs for an accurate physicochemical description of all physicochemical mechanisms contributing to the response. Nevertheless, the data of such a complex electrode in the form of an oxidized titanium electrode are fitted by an oversimplistic ECM, showing how easily unphysical parameterizations can be obtained with ECM-based impedance analysis. In summary, trends in how microstructures, charge transfer resistances and oxide layers can influence EIS and CV data are shown, while awareness for the overinterpretation of ECM-analysis is raised.

The increasing impact of industrialization on climate change, primarily due to the emission of greenhouse gases such as carbon dioxide (CO₂), underscores the urgent need for effective strategies for CO₂ fixation and utilization. Electrochemical CO₂ reduction holds promise in this regard, owing to its scalability, energy efficiency, selectivity, and operability under ambient conditions. However, the activation of CO₂ requires suitable electrocatalysts to lower energy barriers. Various electrocatalysts, including metal-based systems and conducting polymers like polyaniline (PANi), have been identified to effectively lower this barrier and enhance CO₂ reduction efficiency via synergistic mechanisms. PANi is particularly notable for its versatile interaction with CO₂, cost-effectiveness, stability, and tunable properties, making it an excellent catalyst option for CO₂ reduction reactions (CO₂RR). Recent advancements in research focus on enhancing PANi conductivity and facilitating electron transfer through metal and metal oxide doping. Leveraging PANi's π–π electron stabilization ensures high conductivity and stability, rendering it suitable for real-time applications. Strategic dopant selection and optimization of Lewis acid-base interactions are crucial for selective CO₂-to-hydrocarbon conversion. Tailored electrode modifications, especially metal/metal oxide-loaded PANi electrodes, outperform conventional approaches, underscoring the importance of catalyst design in advancing CO₂ electroreduction technologies. This review provides a comprehensive analysis of the systematic methodology involved in preparing PANi-modified electrodes and explores the enhancements achieved through the incorporation of metals and metal oxides onto PANi-modified electrodes. It highlights the superior efficiency and selectivity of CO₂RR facilitated by these modified electrodes through profound synergistic approach compared to conventional metal electrodes such as platinum.

Nano-sized bubbles (NBs: nanobubbles) have attracted attention in various fields such as physics, engineering, medicine and agriculture for fundamental and practical reasons. Atomic force microscopy (AFM) has revealed the occurrence of NBs and discovered their flattened shape. However, their dynamic behaviours have not yet been discussed much owing to the slow scanning speed. The existence of these energetically unfavourable structures is still controversial owing to the lack of studies on bubble-like behaviour of NB such as aggregation, growth and dissolution. Recently developed high-speed AFM (HS-AFM) can observe nano-interface phenomena at a speed of 0.5 frame s⁻¹. In this study, HS-AFM was applied to electrolytic H₂ NBs. We successfully observed NB nucleation, growth and dissolution during a potential scan. Image analysis revealed flattened nuclei with heights of less than 10 nm. The NBs remained stable for a short period after the hydrogen evolution stopped, and they rapidly dissolved at the anodic potential. As the potential sweep was repeated, the number of NB nuclei increased. This is the first study showing the dynamic motion of NBs during the potential sweep by AFM. Videos captured by HS-AFM make NB existence more certain. This research contributes not only to the NB study but also to the clarification of the gas evolution mechanism on electrodes.

This study aims to investigate the electrochemical properties of usnic acid (UA), a secondary metabolite commonly biosynthesized by a variety of lichen species, and its biosynthetic precursor methylphloroacetophenone (MPA). During cyclic and differential pulse voltammetry, well-defined anodic peaks were observed for UA and MPA in 0.04 M Britton–Robinson buffer solution (pH 5) containing 20% (v/v) acetonitrile. The absence of cathodic peaks during the reverse voltammetric scans revealed that both oxidation reactions are chemically irreversible. Scan rate studies demonstrate that UA oxidation is an adsorption-controlled process, whereas the oxidation of MPA molecules occurs as a diffusion-controlled process. For both molecules, the number of electrons transferred during the oxidation was calculated to be 3. Differential pulse voltammetry results demonstrate that the anodic peak for the two molecules is markedly influenced by the solution pH and the same numbers of protons and electrons are involved in the oxidation process of the molecules. Based on the evidence generated by the electrochemical studies, oxidation mechanisms are proposed for UA and MPA, which involves a two-step electron loss with a hydration reaction taking place in between. This study provides an understanding of the bioactivity mechanisms of these two natural products.

The dynamics of the structural changes in the electrochemical double layer at the interface between a Ag(111) electrode and 0.1 M KOH electrolyte have been probed using surface X-ray diffraction measurements. The X-ray measurements utilised a lock-in amplifier technique to obtain a time resolution down to the millisecond scale. Two potential step regions were explored in an attempt to separate the dynamics of the reversible adsorption/desorption of hydroxide species (OH_ad) and the subsequent cation (K⁺) ordering in the double layer. By probing different positions in reciprocal space, sensitive to different structural changes, the time-dependent response of the electrode surface was probed and time constants for the different associated processes were obtained.

The replacement of conventional lithium-ion batteries with solid-state batteries is currently under investigation by many players both from academia and industry. Sulfide-based electrolytes are among the materials that are regarded as most promising, especially for application in the transport sector. The performance of anode, cathode, and solid electrolyte materials of this type of solid electrolyte is typically evaluated using manually assembled cells such as Swagelok cells, EL-CELLs, and in-house built pressure devices. Coin cells, however, are often disregarded. Though coin cells cannot accurately predict how a material will perform in an end-use application battery cell format, they are easy to assemble and can provide reproducible data compared to the other cell types, which make them an interesting option for testing the materials under conditions more relevant for their envisioned application. The coin cell preparation method presented in this work has been evaluated interlaboratory for reproducibility and, in addition, can be modified depending on the optimization parameters of the solid electrolyte, cathode material, bilayer comprised on cathode and solid electrolyte, lithium metal anode, and cell in general. Besides, an interlab round-robin test (RRT) is carried out between four laboratories, measuring defined electrochemical tests of sulfide solid-state batteries in coin cell configuration. This RRT for the preparation of coin cell solid-state batteries with sulfide solid electrolyte, lithium nickel manganese cobalt oxides cathode, and lithium metal anode is intended for academic researchers and provides guidelines of research in this field.

As a subfield of artificial intelligence (AI), machine learning (ML) has emerged as a versatile tool in accelerating catalytic materials discovery because of its ability to find complex patterns in high-dimensional data. While the intricacy of cutting-edge ML models, such as deep learning, makes them powerful, it also renders decision-making processes challenging to explain. Recent advances in explainable AI technologies, which aim to make the inner workings of ML models understandable to humans, have considerably increased our capacity to gain insights from data. In this study, taking the oxygen reduction reaction (ORR) on {111}-oriented Pt monolayer core–shell catalysts as an example, we show how the recently developed theory-infused neural network (TinNet) algorithm enables a rapid search for optimal site motifs with the chemisorption energy of hydroxyl (OH) as a single descriptor, revealing the underlying physical factors that govern the variations in site reactivity. By exploring a broad design space of Pt monolayer core–shell alloys ($��\sim �17�,�000�$ candidates) that were generated from $��\sim �1500�$ thermodynamically stable bulk structures in existing material databases, we identified novel alloy systems along with previously known catalysts in the goldilocks zone of reactivity properties. SHAP (SHapley Additive exPlanations) analysis reveals the important role of adsorbate resonance energies that originate from $��s�p�$-band interactions in chemical bonding at metal surfaces. Extracting physical insights into surface reactivity with explainable AI opens up new design pathways for optimizing catalytic performance beyond active sites.

Chemical additive and physical template-free electrochemical methods to prepare carbon-supported nanostructures of catalyst metals represent an emerging technology. Formation of the metal nano/microstructures depends not only on the electrochemical method/parameters but also on the nature of the underlying carbon material. Here, we present a comparative evolution of unevenly distributed coral-like aggregates of nanocuboid-shaped gold nanostructures (AuNCBs) on the oxidised form of boron, nitrogen-doped carbon nanoonions (oxi-B,N-CNO) compared to evenly distributed bud-like aggregates of cubic shaped gold nanostructures on bare glassy carbon electrode under a similar electrochemical approach. The synthesis method provided the best availability of the surface active sites, whereas the shape of the structures showed a direct influence of both outer-sphere and inner-sphere electron transfer reactions. The higher sensitivity of AuNCBs@oxi-B,N-CNO compared to individual components and bare carbon/gold electrodes toward the inner-sphere oxidative reaction of N-acetyl-L-cysteine (NAC) was exploited in order to develop an electrochemical assay method with sensitivity and linear dynamic range of (4.70 ± 0.25) × 10⁻⁴ C∙cm⁻²∙mM⁻¹ and 0.2–2.5 mM, respectively in acetate buffer (pH 4.45). Furthermore, the sensor design was deployed in the quantitation of NAC in pharmaceutical preparations, resulting in 89%–106% recovery.