Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines knowledge extraction and traceability in forward-osmosis hydrogel systems, with links to intelligent manufacturing and evidence governance. The organizing question is how relational evidence should be extracted, linked to sources, and kept distinct from model-generated interpretation. 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 extracted relations being treated as causal facts without source-level verification. 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
Tang, B., Gao, S., Gui, C., Luo, Q., Wang, T., Huang, K., Huang, L., & Jiang, H. (2024). Osmotic pressure regulated sodium alginate-graphene oxide hydrogel as a draw agent in forward osmosis desalination. Desalination, 586, 117863. https://doi.org/10.1016/j.desal.2024.117863
Tao, J., Lyu, R., & Cao, X. (2026). A scalable data governance architecture for privacy-aware intelligent learning systems in lifelong. Future-Adaptive Intelligence and Lifelong Systems, 1(1).
World Health Organization. (2021). WHO global air quality guidelines: Particulate matter, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. World Health Organization.