Abstract
This critical review examines evidence boundaries and scale-up in thermowettability in graphene channels, with links to intelligent manufacturing and evidence governance. The organizing question is which measurements and assumptions remain valid when a laboratory component is translated into a production decision. 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 component-level success being reported as end-to-end readiness. 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.
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.
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., Pei, J., & Jiang, H. (2024). Desalination driven by temperature gradient coupled with surface wettability in a graphene channel. Industrial & Engineering Chemistry Research, 63(49), 21565-21571. https://doi.org/10.1021/acs.iecr.4c03251
World Health Organization. (2021). WHO global air quality guidelines: Particulate matter, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.
Zhang, Z., Liu, W., Tao, J., Zhu, H., Li, S., & Xiao, Y. (2025, December). Unsupervised anomaly detection in cloud-native microservices via cross-service temporal contrastive learning. In 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) (pp. 221-226). IEEE.
