Johnson Matthey Technology Review - Fast Track

High-entropy alloy (HEA) electrocatalysts have emerged as a transformative platform in the pursuit of efficient, robust water electrolysis. By harnessing compositional complexity and synergistic interactions between multiple metallic elements, HEAs offer a route to simultaneously enhance activity, durability, and tunability for both the hydrogen and oxygen evolution reactions. This review surveys recent advances in the synthesis, structural engineering, and electrochemical performance of HEA-based nanomaterials ranging from nanoparticles and aerogels to nanofibers and thin films. Particular focus is given to the interplay of atomic distribution, surface properties, and electronic structure in dictating catalytic behavior, as well as to the growing body of bifunctional and pH-universal catalysts. Outstanding challenges, including compositional optimization, stability in harsh environments, and scalable fabrication, are critically evaluated. Finally, future prospects for data-driven design and operando insights are discussed, highlighting the potential of HEA electrocatalysts to drive next-generation, sustainable hydrogen production.

Industrial research and development (R&D) of advanced materials, particularly catalysts for sustainable technologies, is fundamentally hindered by a data analysis bottleneck. State-of-the-art characterisation instruments generate vast and complex datasets, the processing of which has become a significant impediment to the pace of innovation. This paper introduces the Johnson Matthey Data Science App Store, a strategic digital platform developed to overcome this challenge. The platform is a centralised, web-based suite of data science applications designed to democratise advanced analytics through intuitive, user-friendly graphical interfaces. The usefulness of the approach in generating value is illustrated with examples in three-dimensional device and micron level characterisation, particle size analysis and high-precision strain analysis.

The Southern Sichuan Basin in China is frequently plagued by particulate matter (PM2.5) pollution events in winter, owing to its unique topographical and meteorological conditions. This study combined the machine learning method with the receptor model to reveal the significance of driving factors and their impacts on PM2.5 concentrations. The results indicated that three primary wintertime severe PM2.5 pollution episodes in the Southern Sichuan Basin were driven by the combination of high temperatures (> 283 K), high atmospheric pressure (> 980 hPa), high relative humidity (> 80%), weak wind speeds (< 1 m·s-1), and low boundary layer height (< 500 m). Emissions were identified as the dominant factor (76.5%), followed by meteorological conditions (12.8%) and atmospheric chemical reactions (10.7%). Secondary sources (25.5%) and transport-related sources (24.7%) were identified as the main contributors to PM2.5 concentrations. The sensitivity analysis of secondary inorganic aerosols revealed that the most influential factor was ammonium (NH4+), followed by sulphate (SO42-) and nitrate (NO3-). This study advances our understanding of PM2.5 drivers and informs targeted pollution control strategies.

Nanoparticles underpin a significant portion of the chemicals industry, with nanoparticle catalysts serving as some of the most extensively deployed technologies at scale. Most notably their performance can be directly linked to structural and compositional properties of the catalyst. Scanning transmission electron microscopy provides a comprehensive insight into a catalyst’s microstructure, however the technique is prone to manual and laborious data acquisition. As a result, it is difficult to perform this process over a statistically significant number of particles. This challenge grows when data must be acquired under industrially realistic operating conditions, such as at elevated temperatures and/or in oxidizing or reducing conditions. With recent developments in automation in the field, we present a framework for autonomous particle sizing and spectrum imaging acquisition which incorporates machine learning, programmable mask-based scanning, in-situ stimulus control, particle size distribution, and compositional analysis. This pipeline paves the way toward studying nanoparticle catalyst structure-property relationships both at scale and under operating conditions.

Microplastics (MPs), a class of emerging pollutants, are widely present in the atmosphere. The present study was conducted with the objective of investigating the distribution of microplastics in the traffic environment. To this end, a year-long sampling programme was implemented on Lianhua Street in Zhengzhou, China. This street is a typical urban arterial road characterised by a high daily traffic volume and a mixture of commercial and residential surroundings. The samples were first analyzed for quantity and shape by stereomicroscopy, followed by polymer identification via micro-Raman spectroscopy (532 nm laser). The findings indicated that the average concentration of MPs in traffic areas was highest in winter (2340 ± 660 items/m3), followed by autumn and summer, and lowest in spring (960 ± 480 items/m3). Concentrations were greater on non-working days compared to working days, but there was no significant difference between peak and off-peak traffic hours. Most traffic-related MPs detected between 10 and 30 µm (69.2%). The average particle size was largest in summer (27.77 ± 16.53 µm), then autumn (26.75 ± 16.50 µm), spring (26.25 ± 13.74 µm), and smallest in winter (21.14 ± 11.18 µm). The main polymer types identified were tire wear particles (TWPs), ethylene-vinyl acetate (EVA), polyethylene (PE), and polyurethane (PU), accounting for 79.7%, 14.5%, 5.1%, and 0.7% of the annual average, respectively. TWPs and PE were most prevalent in spring, while TWPs and EVA dominated during the other seasons. Microplastics primarily appeared as granules (76.6%), fragments (20.2%), and fibers (3.2%). The findings reveal the temporal distribution of MPs in traffic environments and provide essential baseline data for their management and regulation.

Fuel cells for heavy-duty applications have garnered significant attention. Decades of advancements in stable oxygen reduction reaction (ORR) catalysts must now be leveraged to meet the increased cyclability demands of the heavy-duty vehicle market. The deposition of metal oxide coatings on Pt-based catalysts for ORR is hypothesized to balance high initial surface area with short-term electrochemical surface area (ECSA) retention in acid electrolytes. To explore this potential, a literature review covering 2001–2023 was conducted. Between-study heterogeneity was assessed through regression analyses at a 5% significance level. A total of 24 studies evaluated the electrochemical properties of metal oxide-coated Pt-based catalysts. Regression analyses indicated that 4-9 wt.% TiO2-coated catalysts on thin-film rotating disk electrodes degrade in a systematic manner, making their properties more predictable. In contrast, 11-55 wt.% SiO2-coated catalysts exhibited higher active surface area retention in comparison to TiO2-coated catalysts but with less consistency. Our findings suggest that thinner, atomically-grown MO2-type metal oxide coatings with more ionic M–O bonds, such as TiO2, provide the optimal balance between active site accessibility and catalyst stability under potential cycling. Furthermore, metal oxide coating shows potential for protecting and stabilizing platinum particles against dissolution and coalescence, presenting a promising avenue for further research to optimize ORR activity and long-term performance.

This study presents the design and scaling of a pilot-scale process for synthesizing antimicrobial oxygenated apatites, with emphasis on reproducibility and industrial scalability. The system achieves a daily production of 600 g of bioactive apatite using calcium nitrate or chloride, and 300 g/day with calcium carbonate under optimized conditions. Key units—including a double-jacketed reactor (40°C) with a marine agitator and an optimized mixer—were dimensioned using experimental data to maintain a Ca/P ratio of 1.575, ensuring structural integrity and controlled release of oxygenated species. Heat transfer, mixing dynamics, and agitation power were rigorously calculated to enable consistent scale-up. This work provides a validated framework for transitioning lab-scale apatite synthesis to controlled industrial production.

Nanoparticles based on platinum group metals (PGMs) are effective for catalyzing reductions of widely occurring water contaminants, including nitrate (NO3-) and per- and poly-fluoroalkyl substances (PFAS).  This Short Article presents a new way to use PGMs that enable efficient use of hydrogen gas (H2) as the reductant to detoxify nitrate (NO3-), per- and poly-fluoroalkyl substances (PFAS), and other oxidized water contaminants.  The platform is a H2-based membrane catalytic-film reactor (MCfR).  The MCfR’s foundation is a Pd catalyst film that is in situ deposited on the outer surface of gas-transfer membranes.  H2 is provided to the membrane’s lumen, permeates the membrane’s wall, and is delivered directly to the base of the nanometer-thick catalytic film.  H2-delivery pressure, Pd loading on the membrane surface, and the alloying of Pd with other PGMs control the kinetics and selectivity of the reduction reactions.  The MCfR concept takes PGMs another step closer practical clean-water applications, by transitioning from suspended nanoparticles to immobilized nanofilms.

This is Part II of the review discussing the role of molecular platinum complexes in homogeneous catalysis, with a focus on Pt-NHC pre-catalysts introduced in Part I (1). The following sections are devoted to the development of platinum-catalysed hydrosilylation of alkenes and alkynes, providing both historical context and an overview of the evolution in the mechanistic understanding of this reaction, as well as the most recent advancements and alternatives based on non-platinum catalysts.