Electrochemical science advances最新文献

Urea is formed from the metabolism of proteins and used as a biomarker for diagnosing and monitoring various medical conditions. In this work, a urea biosensor based on an electrolyte-insulator-semiconductor capacitor (EISCAP) modified with a stacked polyelectrolyte polyallylamine hydrochloride (PAH)/urease bilayer prepared by the layer-by-layer (LbL) technique is presented for the first time. The LbL formation of the PAH/urease bilayer was monitored with an underlying charge-sensitive Al/p-Si/SiO₂/Ta₂O₅ EISCAP using convenient capacitive-voltage and constant-capacitance mode measurements. Urea-sensitive EISCAP biosensors were electrochemically characterised in buffer solutions and artificial urine (AU) samples spiked with various concentrations of urea between 0.1 mM and 50 mM. The biosensors exhibited urea sensitivities of ca. 35.4 mV/dec and 32.1 mV/dec in buffer and AU solutions, respectively. Finally, local surface pH changes as a function of urea concentration have been evaluated. The obtained findings demonstrate the potential of PAH/urease-modified EISCAPs for non-invasive urea biomarker detection in urine samples at homecare or in-field settings.

The energy storage performance of supercapacitors—defined by specific capacitance, energy density, and power density—is strongly influenced by the structural and electrochemical properties of electrode materials. While cathode development has advanced significantly, research on efficient and sustainable anode materials remains limited, hindering further improvements in energy density. This study presents a low-cost, sustainable anode material derived from Abelmoschus esculentus (AE) seed biomass. Activated carbon (AE-AC) was prepared via chemical activation and subsequently coated with magnetic Fe₃O₄ nanoparticles synthesised through co-precipitation to form an AE-AC-doped Fe₃O₄ nanocomposite. The materials were characterised using XRD, SEM–EDX, BET surface area analysis, and other techniques. Electrochemical performance was evaluated using cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), and electrochemical impedance spectroscopy (EIS). At a scan rate of 2.5 mV/s, both electrodes exhibited peak capacitance. GCD analysis showed specific capacitances of 119.97 F/g for AE-AC and 205.86 F/g for AE-AC-doped Fe₃O₄ at 0.05 A/g. EIS results confirmed enhanced performance of the nanocomposite in acidic medium. These findings highlight the potential of AE-based activated carbon composites as environmentally friendly and efficient anode materials for next-generation supercapacitors.

Anion exchange membrane (AEM) electrolysis is one of the most promising water electrolysis technologies, combining the advantages of proton exchange membrane (PEM) electrolysis, such as high gas purity, high current densities and dynamic operation, while using cheap transition metal electrocatalysts known from alkaline water electrolysis (AWE). AEM water electrolysis (AEMWE), when operated liquid (electrolyte or water) free (dry) at the cathode side, offers simplified water management, reducing the balance-of-plant. Numerous factors, such as cell design, membrane properties, flow rate of electrolyte and operation parameters, directly or indirectly, impact the performance of AEMWE, which becomes even more vital when the cathode compartment is operated liquid free. Herein, this work presents a comprehensive overview of several factors involved in the performance of a dry cathode AEMWE. Advancements and challenges in membrane materials, asymmetric electrolyte feeds and operating parameters were analysed. Finally, to have a durable and efficient AEMWE, this article discusses current development on the dry cathode AEMWE technology and outlines prospective avenues for further improving the system.

“Green” Hydrogen will be a cornerstone of a carbon neutral energy future. In order to produce it on a large scale, a thorough de-risking of electrolysis technology is a fundamental step. This special issue discusses concepts to improve PEM- & AEM-Electrolyser technology from the fundamental catalyst level up to scalable factory concepts for series production. This comprehensive approach will be essential for the pathway towards the Gigawatt scale.

Cancer is a result of uncontrolled cell growth with the potential to damage or spread to another part of the body. It is the deadliest disease in the world; therefore, rapid and sensitive detection is essential to fight it. In the past few decades, many diagnosis tools have been developed to detect cancer and monitor therapy progress. Among them, electrochemical biosensor showed the promising significance due to its capability of early detection, selectivity, sensitivity, flexibility, portability and cost-effectiveness. The performance of the electrochemical sensor depends on the sensor surface engineering as well as development techniques based on the types of biomarkers. This review covers the importance of cancer diagnosis, the basic concept of the electrochemical biosensor, design strategy of biosensors including surface engineering and the state-of-the-art for different types of biomarker detection. Additionally, the limitations and advantages of different types of biosensors were parallelly explained. Finally, the future direction for the advancement of electrochemical biosensor is comprehensively discussed. The author trusts that the insights thus explained will lead to further research in the scholarly community aimed at expanding theoretical knowledge and pragmatic innovation in electrochemical sensing devices for cancer detection. Such research findings are anticipated to facilitate high-end developments both in the theoretical area and the application.

Several quinones, diphenoquinones and respective reduced forms, hydrobenzoquinones and hydrodiphenoquinones, were synthesized, and their electrochemical properties were studied by cyclic voltammetry (CV) in non-aqueous medium to assess their upcoming applicability as organic redox mediators. Benzoquinones and diphenoquinones exhibited two reversible electron transfers (ETs) as exemplified by tetra-tert-butyldiphenoquinone, which displayed ETs at standard potential (E⁰) at E⁰ = −0.53 V and E⁰ = −0.92 V versus SCE (saturated calomel electrode). However, hydrobenzoquinones displayed chemically irreversible ET, whereas hydrodiphenoquinones exhibited either chemically irreversible or quasi-reversible ETs. For instance, di-tert-butylhydrobenzoquinone demonstrated two irreversible ETs at E_pc = 0.31 V and E_pa = 1.00 V versus SCE.

A reliable and continuous assessment of the degradation state of industrial water electrolyzers is crucial for maintenance planning and dispatch optimization, thus facilitating risk management for both suppliers and operators. Although voltage is a widely used and easily measurable degradation indicator, its effectiveness is compromised in industrial settings due to the impact of arbitrary operating conditions. Existing methods to correct the impact of operating conditions often rely on measuring characteristic curves, which typically only provide a single-dimensional correction and do not allow varying corrections over time. We propose a data-driven method for degradation state quantification that adjusts the measured voltage under arbitrary operating conditions to a reference condition, using an empirical voltage model and degradation history from a fleet of electrolyzers. This method involves fitting the empirical voltage model for each time series segment and calculating the voltage under the reference condition. To assist model fitting under limited data coverage, the method utilizes a Bayesian approach to incorporate fleet knowledge–an aggregation of the degradation trajectories of the electrolyzer fleet. This method was validated using both synthetic data and operation data from 12 industrial electrolyzers with 1–3 years of operation history, including in-depth sensitivity analyses on the data coverage, fleet–target discrepancy, and fleet size. Results proved the superiority of the proposed fleet-based method over the benchmark method without using fleet knowledge.

Electrochemical nitrogen reduction reaction (eNRR) is an emerging field in sustainable chemistry. This review explores the advancements and challenges in both low-temperature (LT) and high-temperature (HT) eNRR methodologies, including aqueous systems, solid-state synthesis, and molten salt techniques. The primary challenges in eNRR are sufficient selectivity and energy efficiency with reliable data acquisition, especially in terms of false positive results. This review explores the current state of eNRR research, emphasizing the ongoing difficulties in improving both the efficiency and reliability of low-temperature aqueous systems. We highlight the potential of lithium-mediated systems in molten salts and organic solvents, which currently demonstrate considerable promise for industrial application, although energy efficiency remains a significant challenge. The review underscores the need for rigorous testing and more consistent research methodologies. This comprehensive analysis places recent advances in eNRR in the broader context of sustainable ammonia synthesis. It emphasizes the importance of continued innovation, standardized research practices, and collaborative validation efforts across different laboratories. In conclusion, while the eNRR field is evolving, a unified approach in research and validation is essential to overcome existing challenges. The successful development and implementation of eNRR technologies, especially lithium-mediated methods, could revolutionize global ammonia production, offering a sustainable alternative to the conventional Haber–Bosch process.

The p-type semiconducting copper oxides CuO and Cu₂O are of interest for the conversion of solar energy due to their medium-wide bandgap and the position of their conduction band, allowing for reductive processes in junctions with electrolytes under irradiation. In this work, on Cu₂O, the efficiency of several such processes in competition with self-reduction is critically reviewed and experimentally studied. Up to 2000 nm thick films were obtained via potentiostatic electrodeposition on fluorine-doped tin oxide on glass from alkaline solutions of CuSO₄ using lactic acid as a complexant. The films consisted of a dense arrangement of crystallites as seen by scanning electron microscopy and were of phase pure Cu₂O as shown by X-ray diffraction (XRD). The films were specular, with an absorption coefficient of 50,000 cm⁻¹ at 480 nm and a direct bandgap of 2.5 eV. In junctions with aqueous electrolytes, the material was found to be p-type. Under electrical bias, cathodic and photocathodic currents passed and increased dramatically when reducible redox compounds were added. The influence of various redox couples (O₂, H₂O₂, and methylviologen [MV, 1,1'-dimethyl-4,4'-bipyridinium]) and their concentration in the electrolyte on the stability of the electrodes was studied. Long-time experiments showed that to avoid degradation of the electrodes, the use of oxygen-saturated solutions was mandatory when no other redox couple was added. H₂O₂-containing electrolytes gave rise to constant photocurrents and no alteration of the electrodes was found by XRD. MV yielded cathodic photocurrents. Reoxidation of its reduced form by dissolved oxygen was necessary in order to hinder dimerization or further reduction to MV⁰ and association of the latter to MV⁰_n, producing a whitish layer on top of the electrodes which led to their inactivation.

Proton-exchange membrane (PEM) water electrolysis is pivotal for green hydrogen production, necessitating accurate predictive models to manage their non-linearities and expedite commercial deployment. Understanding degradation mechanisms through macro-scale modeling and uncertainty quantification (UQ) is crucial for advancing this technology via efficiency enhancement and lifetime extension. This study primarily utilizes a one-dimensional physics-based model to elucidate the presence of electron transport within the PEM, another degradation phenomenon, besides gas crossover. This work also applies a machine learning (ML) algorithm, such as eXtreme Gradient Boosting (XGBoost), to model PEM electrolytic cell (PEMEC) operation based on a dataset generated from the previously mentioned physics-based model. The ML model excels in predicting the polarization behavior. Based on this surrogate model, UQ and sensitivity analysis are finally employed to enlighten the dependence of PEMECs performance and Faradaic efficiency on the effective electronic conductivity of PEM, especially when electronic pathways exist within the membrane and operating at low current densities.