Data-guided bioelectrodes pave way for greener remediation

Chlorinated organic pollutants (COPs), widely used in industry and agriculture, persist in soil and groundwater for decades and threaten ecosystems and human health. Traditional methods to accelerate their breakdown through microbial electrorespiration often rely on slow, costly trial-and-error testing. This study introduces a machine learning–based inverse design framework that rapidly identifies optimal operating conditions for bioelectrodechlorination, including cathode potential, temperature, and microbial community composition. The system predicted pollutant degradation rates with less than 6% error, outperforming conventional approaches. By integrating biological and experimental parameters, this strategy makes bioremediation more efficient, scalable, and environmentally friendly, offering new pathways to tackle persistent pollution.

Persistent COPs such as tetrachloroethene and trichloroethene resist natural degradation, contaminating aquifers and soils for decades. Microbial electrorespiration, which harnesses electrode-respiring bacteria to drive reductive dechlorination, is a promising solution. Yet, its application is hampered by aquifer heterogeneity, unpredictable microbial dynamics, and the difficulty of pinpointing the right operating conditions. Current laboratory optimization often demands labor-intensive, unsustainable experiments. Meanwhile, machine learning has shown strong predictive power in fields such as water treatment and membrane design. Due to these challenges, there is an urgent need to develop data-driven strategies that can accelerate and scale up microbial dechlorination for contaminated environments.

Researchers from Harbin Institute of Technology and Northwestern Polytechnical University report a new machine learning framework that integrates experimental features with microbial biofilm data to optimize bioelectrodechlorination. The study, published (DOI: 10.1016/j.ese.2025.100625) on September 27, 2025 in Environmental Science and Ecotechnology, demonstrates that inverse design can accurately predict dechlorination rates and identify the most effective conditions for breaking down chlorinated pollutants. By combining algorithms such as random forest and extreme gradient boosting with particle swarm optimization, the method reduces reliance on exhaustive laboratory testing while enhancing remediation efficiency.

The team compiled a dataset of 357 entries from 68 peer-reviewed studies, covering experimental designs, cathodic biofilm profiles, and reaction rates. They tested multiple machine learning models, including random forest, multilayer perceptron, and XGBoost, to capture the complex interplay among environmental factors, electrochemical conditions, and microbial taxa. Results showed that incorporating genus-level microbial data significantly improved predictions, with R² values above 0.87. Key drivers included temperature, cathode potential, electrode area, and the relative abundance of bacteria such as Clostridium, Desulfovibrio, Geobacter, and Dehalococcoides. Using particle swarm optimization, the framework identified optimal potentials (−260 to −510 mV) and a temperature of ~23 °C for dechlorinating tetrachloroethene, trichloroethene, and 1,2-dichloroethane. Experimental tests confirmed that predictions matched observed reaction rates within 6% error. Life cycle assessment further showed that adopting ML-guided optimization reduced global warming potential by nearly 15 kg CO₂-equivalent and lowered energy demand. This approach marks the first time microbial community data have been integrated into an inverse design model for bioelectrodechlorination, highlighting its potential to guide scalable, eco-friendly pollutant remediation.

"Our study demonstrates that combining microbial ecology with machine learning can fundamentally change how we design bioremediation systems," said corresponding author Prof. Aijie Wang. "Instead of relying on lengthy trial-and-error experiments, we can now pinpoint effective operating conditions with high accuracy. This not only saves time and resources but also deepens our understanding of how microbial communities contribute to pollutant degradation. Such a framework provides a practical bridge between laboratory research and real-world environmental engineering."

This data-driven framework paves the way for practical, scalable remediation of contaminated groundwater and soils. By cutting down experimental costs and improving predictive accuracy, the method accelerates the cleanup of sites polluted by industrial solvents, pesticides, and persistent chlorinated compounds. Beyond COPs, the strategy can be adapted to other bioelectrochemical processes, including wastewater treatment, bioenergy production, and removal of emerging pollutants. As more datasets and genomic information become available, integrating functional genes with inverse design may further refine predictive models. Ultimately, this approach brings bioelectrochemical remediation closer to widespread, sustainable application in global environmental management.

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References

DOI

10.1016/j.ese.2025.100625

Original Source URL

https://doi.org/10.1016/j.ese.2025.100625

Funding information

This research was supported by the National Natural Science Foundation of China (No. 52370163), National Key Research and Development Program of China (No. 2022YFA0912501), and State Key Laboratory of Urban-rural Water Resource & Environment (Harbin Institute of Technology) (No. 2025DX12).

About Environmental Science and Ecotechnology

Environmental Science and Ecotechnology (ISSN 2666-4984) is an international, peer-reviewed, and open-access journal published by Elsevier. The journal publishes significant views and research across the full spectrum of ecology and environmental sciences, such as climate change, sustainability, biodiversity conservation, environment & health, green catalysis/processing for pollution control, and AI-driven environmental engineering. The latest impact factor of ESE is 14.3, according to the Journal Citation Reports^TM 2024.