Accurate determination of the platinum crystallite size distribution in carbon-supported platinum (Pt/C) catalysts is critical for evaluating both the initial performance and long-term durability of proton exchange membrane fuel cells (PEMFCs). Conventional characterization approaches—including transmission electron microscopy (TEM), small-angle X-ray scattering (SAXS), and the Scherrer equation—are limited by restricted statistical sampling, sensitivity to support interference, or the inability to resolve the full breadth of the size distribution. In this work, we present a regularization-based inversion framework that extracts the volume-weighted crystallite size distribution directly from X-ray diffraction (XRD) line profiles, without requiring any a priori assumption regarding the functional form of the distribution. The observed diffraction intensities at three high-angle Pt reflections—(220), (311), and (222)—can be expressed as a linear combination of pre-computed Pearson VII single-crystallite profiles organized in a dictionary matrix, and the unknown size distribution is recovered by solving a non-negative Tikhonov-regularized least-squares problem with automatic L-curve parameter selection. Multi-peak joint inversion substantially improves numerical stability and provides an internal self-consistency check across all three reflections. Validation against TEM particle size statistics on a series of commercial Pt/C catalysts with Pt loadings of 40 wt% – 60 wt% demonstrates good quantitative agreement between the XRD-derived and TEM-measured distributions. Applied to intact aged catalyst-coated membranes (CCMs) after removal of both carbon papers, the method successfully resolves a bimodal degradation signature comprising a ~ 5 nm population attributable to electrochemical Ostwald ripening, and a ~25 nm population consistent with particle migration and coalescence, in agreement with post-mortem TEM analysis. The proposed framework offers a non-destructive, statistically representative, and computationally efficient alternative to existing size characterization techniques, with broad applicability to nanocrystalline materials beyond fuel cell catalysts. An open-source Python implementation is publicly available at https://github.com/dragonMaLong/xrd-analyzer.
Polyethylene terephthalate (PET) waste represents both an environmental burden and a loss of carbon resources. Alkaline depolymerization of PET yields terephthalic acid and ethylene glycol (EG). Electrocatalytic oxidation of EG to glycolic acid (GA), the monomer of biodegradable polyglycolic acid (PGA), offers a sustainable route driven by renewable electricity. However, the ethylene glycol oxidation reaction (EGOR) involves competing pathways toward C2 products (GA and oxalic acid) and C1 products (formic acid and CO2), making selective GA production a central challenge. This review provides a comprehensive overview of recent progress in selective EG-to-GA electrooxidation from catalyst design, mechanistic understanding, and electrolyte system evolution.
Early cancer screening can significantly improve therapeutic outcomes, volatile organic compounds (VOCs) and other related gases in exhaled breath, as cancer-specific biomarkers, offer a novel approach for non-invasive diagnosis. Metal oxide semiconductor (MOS)-based gas sensors are pivotal for exhaled breath VOCs detection, featuring compact size, cost-effectiveness, and rapid response. This review summarizes the most recent five-year research advances in cancer biomarker detection, and clarifies the core mechanisms of electrical property changes from sensing material-VOCs interactions and key performance metrics including sensitivity, selectivity, humidity tolerance, response/recovery time, stability, and operating temperature. This review also highlights the optimization strategies of sensing materials such as metal doping, surface modification, heterostructure fabrication, and metal oxide-carrier composite synthesis to enhance trace detection, selectivity, and stability via expanded specific surface area, the introduction of active sites and interfacial charge transfer modulation. Finally, this review outlines sensor arrays integrated with machine learning for precise cancer recognition. This review provides theoretical support for the performance optimization and clinical translation of MOS sensors, and facilitates their widespread application in the non-invasive early diagnosis of cancers.
Electrocatalytic nitrate reduction to ammonia (NitRR) represents a sustainable pathway that integrates environmental remediation with the production of high-value ammonia. However, the activity and selectivity of this process are limited by the generation, consumption, and dynamic equilibrium of the key transient intermediateu2014active hydrogen (*H). Precise modulation of *H necessitates a comprehensive understanding of its behavior, which relies fundamentally on advanced in situ electrochemical characterization techniques. However, a systematic review dedicated to the rational selection and application of in situ electrochemical techniques for the specific characterization of *H in NitRR, with clear definitions of each techniqueu2019s u201Cdetectable targets, quantitative accuracy, and application boundariesu201D remains absent. This review aims to fill this methodological gap by systematically summarizing in situ electrochemical techniques for probing *H behavior in NitRR, including in situ spectroscopic techniques, in situ electrochemical methods, and scanning electrochemical microscopy. We construct a problem-oriented hierarchical methodological framework tailored for *H characterization in NitRR. By detailing the principles, representative applications, and limitations of these techniques, this review provides a reliable guide for selecting appropriate characterization methods in future *H research, thereby deepening the mechanistic understanding of NitRR. Additionally, future research directions and development trends are discussed, offering insights to advance efficient and selective green ammonia synthesis technologies.
Developing cost-efficient and durable multifunctional electrocatalysts of hydrogen evolution reaction, oxygen reduction reaction and oxygen evolution reaction is crucial for improving energy conversion efficiency in electrolytic water splitting electrolyzer and advancing rechargeable zinc-air batteries. Polyhedral shaped nanocrystals with well-defined crystal facets represent a type of model catalysts that enables the exploration of structure-activity relationships. However, it is still very challenging to prepare alloy nanocrystals with multiple metal components due to their complicated redox potentials and mixing enthalpy. Herein, we report a rapid microwave-assisted polyol reduction method for syntheses of core-shell Pd@PdPtCu, Pd@PdPtCuNi, Pd@PdPtCuCo and Pd@PdPtCuZn octahedral nanocrystals, and recessed truncated-octahedral Pd@PdPtCuFe nanocrystals. Ascribing to the fast reduction and nucleation rate of Pd precursors, Pd core was firstly formed. Further concurrent reduction and efficient diffusion of the rest metal precursors resulted in the PdPtCu or PdPtCuM (M = Ni, Co, Fe and Zn) alloy shell. It was observed that Fe plays a decisive role in driving the morphological evolution from octahedral to recessed truncated-octahedral nanocrystals. The Fe3+-Bru2013 coordination modulates crystal growth kinetics by altering surface adsorption dynamics, resulting in a mixed exposure of {111} and {100} facets rich in edge and vertex sites. Structural analyses confirmed a well-defined Pd core and homogeneous PdPtCuFe alloy shell with a face-centered-cubic phase. Owing to the synergistic electronic coupling between Fe and noble-metal components and the strain-regulated surface configuration, the obtained Pd@PdPtCuFe/C exhibited superior multifunctional activity with a HER overpotential of 18 mV at 10 mAu00B7cmu20132 and Tafel slope of 27 mVu00B7decu20131, the most active HER catalyst among the five prepared core@shell catalysts and superior to commercial Pt/C. In addition, it delivered an OER overpotential of 360 mV at 10 mAu00B7cmu20132 and a Tafel slope of 75.9 mVu00B7decu20131 comparable to benchmark IrO2 catalysts. All the prepared catalysts exhibited certain ORR activity in alkaline electrolytes, and the champion catalyst Pd@PdPtCuFe/C is manifested with an ORR half-wave potential of 0.792 V in alkaline electrolytes. When applied as a cathode catalyst in ZABs, the Pd@PdPtCuFe/C showed a peak power density of 81.2 mWu00B7cmu20132 and maintained stable upon cycling for over 50 h. This work provides a generalizable route for ultrafast morphology-controlled synthesis of multimetallic nanocrystals, paving the way for the rational design of high-performance, low-PGM bifunctional catalysts for next-generation energy conversion and storage systems.
