Johnson Matthey Technology Review - Volume 66, Issue 2, 2022

It is human nature to prefer additive problem solving even if removal may be the more efficient solution. This heuristic has wide ranging implications when dealing with science, innovation and complex problem solving. This is compounded when dealing with these issues at an institutional level. Additive solutions to workflows with extra software tools and proprietary digital solutions can impede work without offering any advantages in terms of Findable, Accessible, Interoperable, Reusable (FAIR) data principles or productivity. This viewpoint highlights one possible workflow and the mentality underpinning it with an aim to incorporate FAIR data, improved productivity and longevity of written documents while improving workloads within industrial research and development (R&D).

The design of catalyst products to reduce harmful emissions is currently an intensive process of expert-driven discovery, taking several years to develop a product. Machine learning can accelerate this timescale, leveraging historic experimental data from related products to guide which new formulations and experiments will enable a project to most directly reach its targets. We used machine learning to accurately model 16 key performance targets for catalyst products, enabling detailed understanding of the factors governing catalyst performance and realistic suggestions of future experiments to rapidly develop more effective products. The proposed formulations are currently undergoing experimental validation.

The catalytic steam reforming process of natural gas consumes up to approximately 60% of overall energy used in ammonia production. The optimisation of the reforming catalyst performance can significantly improve the operation of the whole ammonia plant. An online model uses actual process parameters to optimise and reconcile the data of primary reforming products with possibility to predict the catalyst performance. The model uses a combination of commercial simulator and open-source code based on scripts and functions in the form of m-files to calculate various physical properties of reacting gases. The optimisation of steady-state flowsheet, based on real-time plant data from the distributed control system (DCS), is essential for the application of the model at the industrial level. The simplicity of the calculation method used by the model provides the fundamental basis for industrial application in the frame of digitalisation initiative. The principal aim of the optimisation procedure is to change the working curve for methane regarding its equilibrium curve as well as methane outlet molar concentration. This is the critical process parameter in reforming catalyst operation. An industrial top fired primary reformer unit based on Kellogg Inc technology design served for the validation of the model. Calculation procedure is used for continuous online evaluation of the most commercially available primary reformer catalysts. Based on the conducted evaluation, the model can indicate possible recommendations which can mitigate marginal performance and prolong reformer catalyst lifetime.

This study intends to identify the characteristics of heat regulation in heat storage microencapsulated fabrics and to examine the effect of the microcapsules application method. For this purpose, phase-changing material (PCM) microcapsules were applied by impregnation and coating methods on cotton fabrics. The presence and distribution of microcapsules on the fabric surface were investigated by scanning electron microscopy (SEM). The temperature regulation of the fabrics was examined using a temperature measurement sensor and data recorder system (thermal camera). According to the differential scanning calorimetry (DSC) analysis, melting in fabrics coated with microcapsules occurred between 25.83°C–31.04°C and the amount of heat energy stored by the cotton fabric during the melting period was measured as 2.70 J g⁻¹. Changes in fabric surface temperature due to the presence of microcapsules in the fabric structure were determined. When comparing the PCM capsules transfer methods, the contact angle of impregnated and coated fabric was obtained as 42° and 73°, respectively. Analysis of the microcapsules transferred to the fabric by impregnation and coating methods shows that the PCM transferred fabric prepared by the impregnation method performs more efficient temperature regulation. However, the analysis shows that PCM transferred fabrics prepared by coating also perform heat absorption, although not as much as the impregnation method. Performance evaluation according to the target properties of the textile will give the most accurate results for fabrics treated by coating and impregnation methods.

This paper overviews the fabrication, microstructural characteristics, mechanical properties and tribological behaviour of B4C reinforced aluminium metal matrix composites (AMMCs). The stir casting procedure and parameters used to fabricate the Al-B4C composites are discussed. The influence of physical parameters such as applied load, sliding speed and sliding distance on tribological behaviour is analysed. The role of the mechanically mixed layer (MML) and wear mechanisms on the wear behaviour and friction coefficient are emphasised. The overview of tribological behaviour revealed that the Al-B4C composites possess excellent abrasion resistance and the ability to operate over a wide range of physical parameters. The Al-B4C composites exhibited better tribological behaviour when compared with the composites reinforced with conventional reinforcement particles (SiC).

The value of using statistical tools in the scientific world is not new, although the application of statistics to disciplines such as chemistry creates multiple challenges that are identified and addressed in this article. The benefits, explained here with real examples, far outweigh any short-term barriers in the initial application, overall saving resources and obtaining better products and solutions for customers and the world. The accessibility of data in current times combined with user-friendly statistical packages, such as JMP^®, makes statistics available for everyone. The aim of this article is to motivate and enable both scientists and engineers (referred to subsequently in this article as scientists) to apply these techniques within their projects.

This study aims to investigate the interactions between collagen and tanning processes performed by ecol-tan^®, phosphonium, EasyWhite Tan^®, glutaraldehyde, formaldehyde-free replacement synthetic tannin (syntan), condensed (mimosa) and hydrolysed (tara) vegetable tanning agents as alternatives to conventional basic chromium sulfate, widely used in the leather industry. Collagen stabilisation with tanning agents was determined by comparative thermal analysis methods: differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and conventional shrinkage temperature (Ts) measurement. Analysis techniques and tanning agents were compared and bonding characteristics were ranked by the thermal stabilisation they provided. Chromium tanning agent was also compared with the alternative tanning systems. The results provide a different perspective than the conventional view to provide a better understanding of the relationship between tanning and thermal stability of leather materials.