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Prototyping and modelling a photovoltaic–thermal electrochemical stripping system for distributed urine nitrogen recovery

Nature Water volume 3, pages 913–926 (2025)Cite this article

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Abstract

Distributed solar-enabled nitrogen capture from urine helps to manage the nitrogen cycle and increases fertilizer, sanitation and electricity access. Here we provide proof of concept for a photovoltaic–thermal electrochemical stripping (ECS) system, known as solar-ECS, that recovers ammonium sulfate fertilizer from real urine independently of the electricity grid. Constant control of photovoltaic currents and extracting waste heat to cool the solar panel while heating ECS enabled 59.3 ± 3.6% more power production and improved ammonia recovery efficiency by 22.4 ± 7.4% relative to prototypes with no heat transfer and uncontrolled currents. The added heat accelerated ammonia volatilization (the rate-limiting step of ECS), while preventing excessive current via charge controllers reduced energy use by 2.24 ± 0.25 kJ g−1 N per excess milliampere per square centimetre. A new process model for ECS operation at different currents and temperatures was proposed and applied to estimate possible net fertilizer revenues of up to US$2.18 kg−1 N in US markets and US$4.13 kg−1 N in African markets. By advancing the recovery of high-purity commodity chemicals from underused wastewaters, this work supports United Nations Sustainable Development Goals for zero hunger, clean water and sanitation, clean energy and responsible production.

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Fig. 1: ECS set-up.
Fig. 2: Effect of applied current on ECS operation.
Fig. 3: Effects of temperature on ECS operation.
Fig. 4: Solar-ECS prototype configurations.
Fig. 5: Temperature variation during prototype operation.
Fig. 6: Comparison of solar-ECS performance under different operation modes.

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Data availability

The data that support the findings of this study are available via the Stanford Digital Repository at https://doi.org/10.25740/yx617jq4815 (ref. 63).

Code availability

The code used for modelling is available via GitHub at https://github.com/orisac/SolarECS.

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Acknowledgements

We acknowledge the Knight-Hennessy Fellowship (to O.Z.C.) and the National Science Graduate Research Fellowship (DGE-2146755 to O.Z.C.) for their support. This work was also funded by the Stanford Center for Innovations in Global Health, the Camille Dreyfus Teacher-Scholar Award (TC-22-093 to W.A.T.), the Stanford Sustainability Accelerator and the Fundação de Amparo à Pesquisa do Estado de São Paulo and Capes (2019/11866-5 and 2023/01032-5 to A.B.B.J.). We also thank A. Kogler, K. Williams and M. Liu for their support throughout the research process.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Stanford University, Stanford, CA, USA

    Orisa Z. Coombs

  2. Department of Chemical Engineering, Stanford University, Stanford, CA, USA

    Taigyu Joo, Amilton Barbosa Botelho Junior & William A. Tarpeh

  3. Department of Chemical Engineering, Polytechnic School, University of São Paulo, São Paulo, Brazil

    Amilton Barbosa Botelho Junior

  4. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    Divya Chalise

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  1. Orisa Z. Coombs
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Contributions

O.Z.C. conceived the idea. O.Z.C. and W.A.T. designed the research. O.Z.C. and A.B.B.J. carried out experiments and performed data analysis. O.Z.C., T.J. and D.C. formulated the model. O.Z.C wrote the code. O.Z.C., T.J., A.B.B.J. and W.A.T. participated in the discussion and writing of the paper. All of the authors approved the final version of the paper.

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Correspondence to William A. Tarpeh.

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Nature Water thanks Jonathan Bessette, Wei He and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model Fitting.

Comparison of the model output to the fitting dataset and a portion of the validation dataset (Figure S7). The y-axis of each plot is non-dimensionalized concentration; the x-axis is time. The experimental data are plotted as mean values ± s.d. (n = 3 independent experiments), with symbols representing the means and error bars representing the standard deviation (s.d.). The model output is plotted as median values ± CI (n = 10,000 model realizations), with solid lines representing the median and error bands representing the 95% confidence interval (CI).

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Supplementary Discussion, Figs. 1–17 and Tables 1–14.

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Coombs, O.Z., Joo, T., Botelho Junior, A.B. et al. Prototyping and modelling a photovoltaic–thermal electrochemical stripping system for distributed urine nitrogen recovery. Nat Water 3, 913–926 (2025). https://doi.org/10.1038/s44221-025-00477-w

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