http://bura.brunel.ac.uk/handle/2438/25430 2026-05-23T06:59:39Z 2026-05-23T06:59:39Z Kapancik Ülker, E Mohammadzadeh, K Hirani, P He, Y Guan, S Lahiri, A http://bura.brunel.ac.uk/handle/2438/33314 2026-05-19T02:00:31Z 2026-04-16T00:00:00Z

Title: Mechanism of Zn storage in polyaniline in deep eutectic solvent–water mixtures: effect of anion and interfacial phenomena Authors: Kapancik Ülker, E; Mohammadzadeh, K; Hirani, P; He, Y; Guan, S; Lahiri, A Abstract: Zn-ion batteries (ZIBs) are promising energy storage devices, wherein both the electrode and electrolytes play a pivotal role. Aqueous electrolytes have a limited electrochemical window that reduces the energy density of the battery. In comparison, deep eutectic solvents (DES) have a wider electrochemical window that can offer a higher energy density. In this paper, the performance of Zn-ion batteries in formamide-based DES containing Zn salts of different anions (Cl⁻, SO₄²⁻, Ac⁻ and TfO⁻) was investigated. The study revealed that anions significantly influence Zn solvation, hydrogen evolution reaction (HER), charge storage mechanism and stability of the battery. X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and charge–discharge analyses showed that both chlorine and triflate anions store charge by anion exchange followed by Zn storage in polyaniline. For acetate and sulfate anions, the storage mechanism is by direct interaction of Zn with Zn-polyaniline (PANI). Among the four anions studied, the dual-storage mechanism in ZnCl₂- and ZnTfO-based DES electrolytes resulted in a more stable Zn–PANI battery performance. However, ZnCl₂-based DES electrolytes led to corrosive issues that affected the long-term stability. The study provides useful insights on novel electrolyte development through manipulating the solvation chemistry of the molecules and the electrode/electrolyte interface. Description: Data accessibility: Data have been made available in Brunel University London’s repository via the Brunel Figshare database. Supplementary material is available online [43]. Ülker E, Mohammadzadeh K, Hirani P, He Y, Guan S, Lahiri A. 2026 Supplementary material from: Mechanism of Zn storage in polyaniline in deep eutectic solvent-water mixtures: effect of anion and interfacial phenomena. Figshare. (doi:10.6084/m9.figshare.c.8369279).

2026-04-16T00:00:00Z Zick, A Schmidt Rivera, X Reynolds, C Farinha, C Case-Humphries, E Cross, P http://bura.brunel.ac.uk/handle/2438/33272 2026-05-14T02:00:36Z 2026-05-05T00:00:00Z

Title: Beyond the kitchen: co-creating sustainable menu strategies through participatory action learning Authors: Zick, A; Schmidt Rivera, X; Reynolds, C; Farinha, C; Case-Humphries, E; Cross, P Abstract: Introduction: This study explores the systems-level dynamics of menu transformation in the hospitality and food service (HaFS) sector through participatory action learning (PAL) workshops with professional chefs. Framing the menu not merely as a list of dishes but as an “operating principle” within a business, the research investigates how chefs navigate competing priorities and stakeholder influences in the context of sustainability goals, particularly reducing food waste and greenhouse gas emissions (GHGE). Methods: Two PAL workshops were conducted with professional chefs (n = 8 and n = 12). Drawing on Bronfenbrenner's ecological systems theory, stakeholder maps and menu priority artefacts generated during the workshops were analysed to identify the relative influence of micro, meso, exo, macro, and chrono-level influencing agents on menu decision-making. Results: Findings indicate that meso-level agents, such as procurement teams, business owners, and restaurant managers, are perceived as the most influential on menu decisions, while micro-level agents, including chef colleagues and friends, are seen as the least impactful. Menu priorities were similarly ranked, with product/dish and customer-related factors dominating over sustainability and acceptability considerations. The workshops also revealed a shift in participant thinking from identifying “who” influences menus to understanding “how” decisions are shaped by systemic constraints such as shelf life, infrastructure, and profitability. Discussion: The participatory format enabled chefs to surface tacit knowledge, reflect on their agency, and engage in systems thinking. While the findings are context-dependent, they highlight the need for multi-level stakeholder engagement in menu transformation and suggest that sustainability goals must be embedded within the operational logic of the menu to be actionable. This research contributes to the growing literature on participatory methods in food systems change and offers a replicable model for chef-led sustainability interventions. Highlights: • Chefs ranked product/dish and customer factors above sustainability in menu design. • Meso-level actors were seen as most influential in shaping menu decisions. • Workshops enabled chefs to reflect on food waste and GHGE in their practices. • GHGE calculations triggered critical learning and inspired recipe reformulation. • Participatory methods surfaced tacit knowledge and fostered systems thinking. Description: Data availability statement: The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.; Supplementary material: The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2026.1698446/full#supplementary-material

2026-05-05T00:00:00Z Okafor, UC Alghamdi, SM Anguilano, L Yang, Y http://bura.brunel.ac.uk/handle/2438/33106 2026-04-04T02:00:27Z 2026-03-05T00:00:00Z

Title: Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights Authors: Okafor, UC; Alghamdi, SM; Anguilano, L; Yang, Y Abstract: Hydrocarbon contamination, particularly with polycyclic aromatic hydrocarbons (PAHs), poses a significant environmental challenge due to its persistence and carcinogenic effects on ecosystems and human health globally. This review explores how ML algorithms can enhance the efficiency of bio-augmentation and phytoremediation through predictive modeling, real-time optimization of microbial consortia, and plant species selection. Traditional bioremediation methods, such as bioaugmentation and phytoremediation, are characterized by slow degradation rates and sub-optimal performance in complex, multi-contaminant environmental milieus. The use of machine learning (ML) models with multi-omics data presents an advanced predictive approach to optimizing bioremediation processes by providing a systematic understanding of microbial and plant-mediated hydrocarbon degradation strategies and processes. ML models can predict which microbial strains or plant species will effectively degrade hydrocarbons under specific environmental conditions by utilizing supervised learning methods such as support vector machines and neural networks. Additionally, the combination of multi-omics data with ML facilitates the identification of critical genes, enzymes, and metabolic pathways involved in the degradation of hydrocarbons, and offers insights into the molecular mechanisms which drive the bioremediation process. The translation of laboratory-based ML models into large-scale, real-world bioremediation strategy is hindered by the complex, dynamic nature of our contaminated environments. This review paper showcases these hinderances and provides a direction for future research, including the development of field-deployable technologies, adaptive ML models, and real-time environmental monitoring strategies. The integration of ML with multi-omics holds substantial promise for enhanced efficiency, adaptability, and scalability of bioremediation strategies which ultimately mitigates carcinogenic risks often associated with hydrocarbon-polluted lithosphere.

2026-03-05T00:00:00Z Cruz, JM Schmidt Rivera, X Jalil-Vega, F O'Ryan, R Valencia, F Rabanal-Arabach, J Ayllón Opazo, E Morris Carmona, PA Larrain Yañez, P http://bura.brunel.ac.uk/handle/2438/33067 2026-03-30T02:00:31Z 2026-03-18T00:00:00Z

Title: Environmental life cycle assessment of novel PV systems for desert conditions Authors: Cruz, JM; Schmidt Rivera, X; Jalil-Vega, F; O'Ryan, R; Valencia, F; Rabanal-Arabach, J; Ayllón Opazo, E; Morris Carmona, PA; Larrain Yañez, P Abstract: Solar photovoltaic (PV) systems are currently seen as an affordable and mainstream renewable energy option to support energy decarbonisation, aligning with commitments of the UN Sustainable Development Goals (SDG 7). This technology prevails in high irradiance places such as deserts, where some of the largest PV systems are installed globally. However, harsh desert conditions reduce PV systems' efficiency and lifespan, among other negative effects. While research on designing PV systems that endure desert conditions is ongoing, little is known about the environmental impacts of these novel PV solutions. This study uses the life cycle assessment (LCA) methodology to assess the environmental impacts of four novel PV system designs (HJT 1–4) for desert conditions and compares them with three systems available in the current market (PERC, PERC+ and TOPCon). The functional unit of the study is ‘the production of 1 kWh of electricity AC, considering a PV system connected to a 570kWp grid in the Atacama Desert with a lifespan of 25 years’. The inventories were built using data from tested designs in the desert. 18 environmental impact indicators were included following ReCiPe method, and complemented with energy payback time (EPBT). Results show that the novel design (HJT 3) achieves up to 30% reduction in GWP100 per kWh of electricity generated compared to conventional monofacial PERC modules, and a 15% reduction compared to TOPCon modules, primarily due to higher efficiency and reduced materials consumption. The Balance of System (BOS) and installation stage shows the greatest impact on PV systems, contributing 46% on average across all environmental burden, followed by the wafer manufacturing (25% on average) and module manufacturing stages (18% on average). Across all impact categories, including EPBT, PERC is the worst performer, and HJT 3 and HJT 4 are the best performers, followed by TOPCon. This study validates the effort of performing environmental impact assessments on new designs, to ensure both technical performance and the environmental and economic sustainability of renewable energy systems. Description: Supplementary data are available online at: https://www.sciencedirect.com/science/article/pii/S2352550926000333#s0170 .

2026-03-18T00:00:00Z