Keywords
materials
Abstract
This review examines masked semantic inference and chirality-aware materials characterization through a mechanism-to-decision framework. It asks how spatial context can support rather than replace physicochemical explanation. Evidence is organized around representation, validation, uncertainty, and operational control, with no invented experiments or unreported quantitative results. Particular attention is given to segmentation consistency being treated as evidence of molecular mechanism. The synthesis shows that credible translation depends on explicit system boundaries, source-level traceability, failure-aware evaluation, and revalidation triggers. These principles provide a disciplined basis for chiral and nanostructured materials imaging while keeping component promise distinct from system readiness.
References
Cai, Y., Li, H., Zang, S., Liu, K., Pu, Z., Li, H., Song, H., Gao, S., Xiao, Y., Huang, J., & Yan, Y. (2026). Theoretical chemistry facilitated understanding of supramolecular chirality regulation by metal ions. ACS Materials Letters, 8(2), 621-627. https://doi.org/10.1021/acsmaterialslett.5c01526
Debe, M. K. (2012). Electrocatalyst approaches and challenges for automotive fuel cells. Nature, 486, 43-51. https://doi.org/10.1038/nature11115
Jaouen, F., Proietti, E., Lefevre, M., Chenitz, R., Dodelet, J.-P., Wu, G., Chung, H. T., Johnston, C. M., & Zelenay, P. (2011). Recent advances in non-precious metal catalysis for oxygen-reduction reaction in polymer electrolyte fuel cells. Energy & Environmental Science, 4, 114-130. https://doi.org/10.1039/C0EE00011F
Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Ma, Q., Zhang, Z., Qiao, P., Wang, Y., Ji, R., Liu, C., & Chen, J. (2025). Dual-level masked semantic inference for semi-supervised semantic segmentation. IEEE Transactions on Multimedia, 27, 4029-4042. https://doi.org/10.1109/TMM.2025.3535294
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
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.
Wu, Z., Wang, T., Wang, S., Liu, N., & Zhang, Y. (2026). See further, think deeper: Advancing VLM's reasoning ability with low-level visual cues and reflection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 18870-18880).
Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., Xu, S., Saha, S., Wu, Q., Gao, S., Abdul Razak, I. B., Brown, D. E., Ramani, V., Wang, R., Marks, T. J., Shao, Y., & Cheng, Y. (2024). Cooperative atomically dispersed Fe-N4 and Sn-Nx moieties for durable and more active oxygen electroreduction in fuel cells. Journal of the American Chemical Society, 146(49), 33569-33578. https://doi.org/10.1021/jacs.4c11121