Keywords
materials
systems
evidence synthesis
reliability
provenance
Abstract
This critical review examines lifecycle reliability and translation in lithium-sulfur separator engineering, with links to intelligent manufacturing and evidence governance. The organizing question is how lifecycle evidence should connect design, manufacturing, operation, degradation, and end-of-life choices. 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 short-term efficiency shifting environmental or reliability costs downstream. 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
Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
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.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wang, Z., Li, W., Wang, T., Pang, M., Kong, Z., An, J., Li, Z., Ye, J., & Xia, G. (2025). Nanoporous ZnGa2O4-modified separator as a multifunctional polysulphide barrier for advanced lithium-sulfur batteries. Electrochemistry Communications, 178, 107990. https://doi.org/10.1016/j.elecom.2025.107990
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
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.