BURA Collection:

Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights

Thu, 05 Mar 2026 00:00:00 GMT

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.

Environmental life cycle assessment of novel PV systems for desert conditions

Wed, 18 Mar 2026 00:00:00 GMT

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 .

Schinus terebinthifolia Raddi: Compounds Isolated by Countercurrent Chromatography and Biological Activities

Wed, 25 Mar 2026 00:00:00 GMT

Title: Schinus terebinthifolia Raddi: Compounds Isolated by Countercurrent Chromatography and Biological Activities Authors: Carneiro, MJ; Borghi, AA; Perez Pinheiro, G; Gois Ruiz, ALT; Mizobutti, D; Minatel, E; Santana Juliao, L; Ignatova, S; Hewitson, P; Frankland Sawaya, ACH Abstract: The chemical composition of natural products is complex and the investigation of bioactivities of compounds of interest demands their isolation. S. terebinthifolia Raddi is a tree belonging to the Anacardiaceae family and is used in Brazilian folk medicine; its fruit (pink peppers) are used in cooking and its bark in phytomedicine. Extracts of other parts of this plant contain a plethora of components and merit further studies. Countercurrent chromatography (CCC) is frequently employed with natural products due to the high sample recovery rate. The objective of this work was to determine the best solvent system (SS) to fraction the ethanol extracts of leaves, flowers and fruit of Schinus terebinthifolia by CCC and isolate compounds of interest and elucidate their structures through nuclear magnetic resonance (NMR) and mass spectrometry (MS). In addition, antiproliferative, potential cell regeneration and antioxidant activities of the fractions of interest were evaluated. In the present work, three compounds were isolated; two were identified as anacardic acids [(6-(8′, 11′-heptadecadienyl)-salicylic acid and 6-(8′-heptadecenyl)-salicylic acid], as well as (Z)-masticadienoic acid. These compounds showed antiproliferative and potential cell regeneration activities as well as varying degrees of antioxidant capacity. Although these compounds present potential therapeutic activity, more studies are necessary to confirm their safety. Description: Data Availability Statement: The original contributions presented in this study are included in the article/Supplementary Material (https://www.mdpi.com/2297-8739/13/4/103#app1-separations-13-00103). Further inquiries can be directed to the corresponding author.

Hybrid modelling of heat transfer systems: Combining physics-based and data-driven approaches for improved prediction and extrapolation

Thu, 29 Jan 2026 00:00:00 GMT

Title: Hybrid modelling of heat transfer systems: Combining physics-based and data-driven approaches for improved prediction and extrapolation Authors: Nazemzadeh, N; Loyola-Fuentes, J; Righetti, G; Diaz-Bejarano, E; Mancin, S; Coletti, F Abstract: Hybrid machine learning-assisted modelling techniques have gained increasing attention recently in many engineering fields. This is due to the challenges associated with pure first-principles and data-driven models, as the former requires deep phenomenological understanding and might become infeasible to describe a complex system with, and the latter needs extensive high-quality data and, more importantly, extrapolates poorly compared to its first principles counterparts. The integration of the two techniques in a framework will result in an integrated approach that benefits from the two realms by strengthening extrapolation capabilities, higher prediction accuracy, and less data demanding and more data-efficient. In this study, a systematic hybrid modelling framework is developed, allowing for the integration of mechanistic models and machine learning algorithms in parallel and series for modelling heat transfer systems to predict a desired target variable, as long as the system is not of a dynamic nature. The framework is developed according to a previous study that enabled the use of machine learning models for such systems. The application of the hybrid modelling framework in this study is demonstrated on the prediction of the condensation heat transfer coefficient in a microfin tube. A laboratory-scale dataset of 5708 datapoints is used for the validation of the developed framework. The validation of the model has been carried out in two different scenarios, both assessing the general prediction and extrapolation capabilities of the developed models in comparison with pure mechanistic and pure machine learning models. The hybrid models, series and parallel, outperform the mechanistic model by approximately 60% more accurate predictions and the machine learning model by 25%, while interpolating. More importantly, while extrapolating, the hybrid models showed approximately 50% more accurate predictions compared to pure machine learning and 27% more accurate compared to the mechanistic model. Description: Data availability: The data that has been used is confidential.