Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines cross-scale modelling and validation in tapered graphene desalination, with links to intelligent manufacturing and evidence governance. The organizing question is which cross-scale relationships are mechanistically defensible and which are only empirical correlations. 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 cross-scale agreement being inferred from shared trends rather than mechanism. 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
Debe, M. K. (2012). Electrocatalyst approaches and challenges for automotive fuel cells. Nature, 486, 43-51. https://doi.org/10.1038/nature11115
Guan, G. F., Liu, X., & Wang, L. (2026, May). A data-driven framework for contamination risk assessment in high-volume lithium-ion battery manufacturing. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 492-496). IEEE.
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., Chen, B., Shao, X., Zheng, H., Hu, X., & Jiang, H. (2022). Simulations of tapered channel in multilayer graphene as reverse osmosis membrane for desalination. Journal of Wuhan University of Technology-Materials Science Edition, 37(3), 314-323. https://doi.org/10.1007/s11595-022-2533-z
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.