Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines data provenance and reproducibility in thermowettability in graphene channels, with links to intelligent manufacturing and evidence governance. The organizing question is what provenance is necessary to reproduce a result across data, material batches, models, and operating environments. 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 a reproducible computation being mistaken for a reproducible scientific conclusion. 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. (2026, May). Study on the correlation between manufacturing variability and electrochemical stability in large-scale lithium-ion battery production. In 2026 5th International Conference on Smart Energy and Clean Energy Power Generation Technology (SECP 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.
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
Wang, Z., Zhang, K., Wang, Y., Zhang, C., Liang, Q., Yu, P., Feng, Y., Liu, W., Wang, Y., Bao, Y., & Yang, Y. (2022). SongDriver: Real-time music accompaniment generation without logical latency nor exposure bias. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 1057-1067). ACM. https://doi.org/10.1145/3503161.3548368
World Health Organization. (2021). WHO global air quality guidelines: Particulate matter, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.