Surface Engineering of Carbon-Supported Platinum as a Route to Electrocatalysts with Superior Durability and Activity for PEMFC Cathodes

Metal-Support Interaction in Pt Nanodisk-Carbon Nitride Catalyst: Insight from Theory and Experiment

Metal-support interaction plays a critical role in determining the eventual catalytic activity of metals loaded on supporting substrates. This interaction can sometimes cause a significant drop in the metallic property of the loaded metal and, hence, a drop in catalytic activity in the reactions, especially in those for which low charge carrier transfer resistance is a necessary parameter. Therefore, there should be a case-by-case experimental or theoretical (or both) in-depth investigation to understand the role of support on each metal. Here, onto a layered porous carbon nitride (g-CN), we grew single crystalline Pt nanodisks (Pt@g-CN) with a lateral average size of 21 nm, followed by various characterisations such as electron microscopy techniques, and the measurement of electrocatalytic activity in the O2 reduction reaction (ORR). We found that intercalating Pt nanodisks in the g-CN interlayers causes an increase in electrocatalytic activity. We investigated the bonding mechanism between carbon support and platinum using density functional theory and applied the d-band theory to understand the catalytic performance. Analysis of Pt's density of states and electronic population across layers sheds light on the catalytic behaviour of Pt nanoparticles, particularly in relation to their thickness and proximity to the g-CN support interface. Our simulation reveals an optimum thickness of ~11 Å, under which the catalytic performance deteriorates.

Synergistic Effect of Electrocatalyst for Enhanced Oxygen Reduction Reaction: Low Pt-Loaded CuPt Alloy Nanoparticles Supported on N-Doped Hierarchical Porous Carbon

It is challenging to synthesize oxygen reduction reaction (ORR) electrocatalysts that are highly efficient, affordable, and stable for use in proton exchange membrane fuel cells. To address this challenge, we developed a low platinum-loading (only 6.68% wt) ORR catalyst (PtCu1-NC), comprising CuPt nanoparticles (average size: 1.51 nm) supported on the N-doped carbon substrates. PtCu1-NC possesses a high specific surface area of 662 m2 g-1 and a hierarchical porous structure, facilitating efficient mass transfer. The synergistic effect from introduced copper and the electron effect from nitrogen modify the electronic structure of platinum, effectively accelerating the ORR reaction and enhancing stability. Density functional theory calculations demonstrate the catalytic mechanism and further verify the synergistic effect. Electrochemical assessments indicate that PtCu1-NC exhibits specific activity and mass activity 5.3 and 5.6 times higher, respectively, than commercial Pt/C. The half-wave potential is 27 mV more positive than that of commercial Pt/C. The electrochemical active surface area value is 104.3 m2 g-1, surpassing that of Pt/C. Approximately 78% of current is retained after 10,000 s chronoamperometry measurement. These results highlight the effectiveness of alloying in improving the catalyst performance.

Interfacial engineering and chemical reconstruction of Mo/Mo2C@CoO@NC heterostructure for promoting oxygen evolution reaction

Chemical reorganization and interfacial engineering in hybrid nanomaterials are promising strategies for enhancing electrocatalytic performance. Herein, MoO₃@zeolitic imidazolate framework-67 (ZIF-67) heterogeneous nanoribbons are designed through coordination assembly. By following heat treatment, a Mo/Mo₂C@CoO@NC heterostructure with nitrogen-doped carbon-encapsulated CoO hexagons (CoO@NC) anchored on the Mo/Mo₂C jag matrix was fabricated. Notably, through controllable experimental optimization, the as-prepared Mo/Mo₂C@CoO@NC heterostructure exhibits numerous active centers (e.g. Mo, Mo₂C, CoO, and NC), fully exposed active sites (numerous pores and jagged structures), and abundant heterointerfaces (Mo/Mo₂C, Mo₂C/CoO@NC, Mo₂C/amorphous, and CoO@NC/amorphous), and exhibits good conductivity (localized single-crystal behavior, graphitized carbon). As a result, the as-developed Mo/Mo₂C@CoO@NC heterostructures inherit impressive oxygen evolution reaction (OER) performance with an overpotential of only 215 mV at 10 mA cm^-2. Furthermore, Mo/Mo₂C@CoO@NC heterostructures exhibit excellent stability with a current density retention of 98.4% after 20 h chronoamperometry. This work provides deep insights into chemical reconstructions and tuning heterointerfaces to efficiently enhance the OER activity of heterostructure-based electrocatalysts.

Intriguing H2S Tolerance of the PtRu Alloy for Hydrogen Oxidation Catalysis in PEMFCs: Weakened Pt-S Binding with Slower Adsorption Kinetics

High quality of hydrogen is the key to the long lifetime of proton-exchange membrane fuel cell (PEMFC) vehicles, while trace H₂S impurities in hydrogen significantly affect their durability and fuel expense. Herein, we demonstrate a robust PtRu alloy catalyst with an intriguing H₂S tolerance as the PEMFC anode, showing a stronger antipoisoning capability toward hydrogen oxidation reaction compared with the Pt/C anode. The PtRu/C-based single PEMFC shows approximately 14.3% loss of cell voltage after 3 h operation with 1 ppm of H₂S in hydrogen, significantly lower than that of Pt/C-based PEMFCs (65%). By adopting PtRu/C as the anode, the H₂S limit in hydrogen can be increased to 1.7 times that of the Pt/C anode, assuming that the PEMFC runs for 5000 h, which is conductive for the cost reduction of hydrogen purification. The three-electrode electrochemical test indicates that PtRu/C exhibits a slower adsorption kinetics toward S^2- species with poisoning rates of 0.02782, 0.02982, and 0.03682 min^-1 at temperatures of 25, 35, and 45 °C, respectively, all lower than those of Pt/C. X-ray absorption fine structure spectra indicate the weakened Pt-S binding for PtRu/C in comparison to Pt/C with a longer Pt-S bond length. Density functional theory calculation analyses reveal that adsorption energy of sulfur on the Pt surface was reduced for PtRu/C, showing 1-10% decrease at different Pt sites for (111), (110), and (100) planes, which is ascribed to the downshifted Pt d-band center caused by the ligand and strain effects due to the introduction of second metallic Ru. This work provides a valuable guide for the development of the H₂S-tolerant catalysts for long-term application of PEMFCs.

A novel electrochemical immunosensor based on PdAgPt/MoS2 for the ultrasensitive detection of CA 242

Dynamic monitoring of tumor markers is an important way to the diagnosis of malignant tumor, evaluate the therapeutic effect of tumor and analyze the prognosis of cancer patients. As a tumor marker of digestive tract, CA242 is often used to Assess the therapeutic effect of colorectal cancer and pancreatic cancer. In this study, immunosensor technology was used to detect CA242. PdAgPt nanocomposites, which have great advantages in biocompatibility, electrical conductivity and catalytic properties, were prepared by hydrothermal synthesis method. The prepared PdAgPt nanocomposites were loaded onto the surface of molybdenum disulfide (MoS₂) with large surface area, and the new nanocomposites were synthesized. Using PdAgPt/MoS₂ as signal amplification platform, the label-free CA242 electrochemical immunosensor has a wide detection range that extends from 1*10⁻⁴ U/ml to 1*10² U/ml and a low detection limit (LOD, 3.43*10⁻⁵ U/ml) after optimization of experimental conditions. In addition, the CA242 immunosensor designed in this study also performed well in the evaluation of repeatability, selectivity and stability, and was successfully used for the detection of CA242 in human serum sample. Therefore, the label-free electrochemical immunosensor constructed in this study has a broad application prospect in the detection of clinical biomarkers.

Perovskite-Carbon Joint Substrate for Practical Application in Proton Exchange Membrane Fuel Cells under Low-Humidity/High-Temperature Conditions

Highly active catalysts with promising water retention are favorable for proton exchange membrane fuel cells (PEMFCs) operating under low-humidity/high-temperature conditions. When PEMFCs operate under low-humidity/high-temperature conditions, performance attenuation rapidly occurs owing to reduced proton conductivity of dehydrated membrane electrode assemblies. Herein, we load platinum onto a perovskite-carbon joint substrate (BaZr_0.1Ce_0.7Y_0.1Yb_0.1O_3-σ-XC-72R) to construct a highly active and durable catalyst with good water retention capacity. We propose that the Pt/(BZCYYb-C) catalyst layer at cathode can promote the water back diffusion of produced water from the cathode to the membrane, thus preventing the decay of fuel-cell performance under low-humidity/high-temperature conditions. The catalyst exhibited outstanding mass activity of 0.542 A mg_pt^-1 at 0.9 V vs RHE. PEMFCs with such a catalyst delivered very high peak power densities (1.70/1.14 W cm^-2 under H₂-O₂/air conditions at 70 °C) and kept 85.3%/92.1% of initial performance values under low-humidity/high-temperature conditions (relative humidity 60%@70 °C/100 °C).

A Critical Review on Artificial Intelligence for Fuel Cell Diagnosis

In recent years, fuel cell (FC) technology has seen a promising increase in its proportion in stationary power production. Several pilot projects are in operation across the world, with the number of running hours steadily rising, either as stand-alone units or as part of integrated gas turbine–electric energy plants. FCs are a potential energy source with great efficiency and zero emissions. To ensure the best performance, they normally function within a confined temperature and humidity range; nevertheless, this makes the system difficult to regulate, resulting in defects and hastened deterioration. For diagnosis, there are two primary approaches: restricted input information, which gives an unobtrusive, rapid yet restricted examination, and advanced characterization, which provides a more accurate diagnosis but frequently necessitates invasive or delayed tests. Artificial Intelligence (AI) algorithms have shown considerable promise in providing accurate diagnoses with quick data collecting. This work focuses on software models that allow the user to evaluate many different possibilities in the shortest amount of time and is a vital method for proper and dynamic analysis of such entities. The artificial neural network, genetic algorithm, particle swarm optimization, random forest, support vector machine, and extreme learning machine are common AI approaches discussed in this review. This article examines the modern practice and provides recommendations for future machine learning methodologies in fuel cell diagnostic applications. In this study, these six AI tools are specifically explained with results for a better understanding of the fuel cell diagnosis. The conclusion suggests that these approaches are not only a popular and beneficial tool for simulating the nature of an FC system, but they are also appropriate for optimizing the operational parameters necessary for an ideal FC device. Finally, observations and ideas for future research, enhancements, and investigations are offered.

Investigation of multiple commercial electrocatalysts and electrocatalyst degradation for fuel cells in real vehicles

The coalescence of Pt nanoparticles during operation in a real vehicle is considered to be the main reason to weaken the ORR. The trajectories of oriented attachment were disclosed by observing the coalescence events of Pt NPs using in situ TEM.