Responsible Automation and Governance for Carbon-Nanotube Desalination Membranes: Measurement and Comparator Design
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Keywords

environment
materials
systems
evidence synthesis
reliability
provenance

Abstract

This critical review examines responsible automation and governance in carbon-nanotube desalination membranes, with links to intelligent manufacturing and evidence governance. The organizing question is how authority, documentation, and appeal should be assigned when automated evidence shapes consequential action. Ten or more scholarly and user-supplied records are synthesized through a mechanism-to-decision framework spanning system boundaries, measurement, representation, evaluation, translation, and governance. No experiment, participant dataset, effect estimate, or production result is invented. Particular attention is given to automated recommendations becoming de facto decisions without accountable review. The review argues that credible translation requires source-level traceability, explicit validity domains, failure-aware evaluation, and revalidation triggers. Google Scholar-supplied records are retained in structured APA form, and no missing DOI or pagination field is completed by conjecture.

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References

Chen, C.-Y., Guan, G. F., & Liu, X. (2026, May). Statistical monitoring and variability control for reliability enhancement in high-throughput lithium-ion battery manufacturing. In 2026 11th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2026). IEEE.

Debe, M. K. (2012). Electrocatalyst approaches and challenges for automotive fuel cells. Nature, 486, 43-51. https://doi.org/10.1038/nature11115

Hernan, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.

Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate.

Norskov, J. K., Rossmeisl, J., Logadottir, A., Lindqvist, L., Kitchin, J. R., Bligaard, T., & Jonsson, H. (2004). Origin of the overpotential for oxygen reduction at a fuel-cell cathode. The Journal of Physical Chemistry B, 108(46), 17886-17892. https://doi.org/10.1021/jp047349j

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.

Ren, L., Tang, Y., Chen, B., Tao, J., & Chen, F. (2026, May). Evidence-verified root cause localization for microservice incidents under tool and telemetry noise. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 664-669). IEEE.

Salthammer, T., Mentese, S., & Marutzky, R. (2010). Formaldehyde in the indoor environment. Chemical Reviews, 110(4), 2536-2572. https://doi.org/10.1021/cr800399g

Wang, T., Jiang, H., Shao, X., Pei, J., Zheng, H., & Hu, X. (2021). Carbon nanotube arrays as monolayer nanoscale membrane for enhanced desalination. Desalination and Water Treatment, 234, 333-347. https://doi.org/10.5004/dwt.2021.27638

World Health Organization. (2021). WHO global air quality guidelines: Particulate matter, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.