Early-stage forecasting of bronze disease development with chlorine mapping: Integrating computer vision and multimodal characterization methodologies
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Geochemistry and Geologic Mapping · Mineral Processing and Grinding · Advanced X-ray and CT Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (69)
Show source-provided references
1. Study of initial corrosion and protection behavior of bronze from the Western zhou dynasty assisted by BTA [J]10.1016/j.corsci.2024.112419 ↗
2. Comparison and study of the corrosion differences of bronze alloys in oxygen-free and oxygen-containing conditions [J]10.1016/j.corsci.2024.112553 ↗
3. Corrosion behaviour of lead bronze from the Western zhou dynasty in an archaeological-soil medium [J]10.1016/j.corsci.2021.109721 ↗
4. Characterization of corroded bronze ding from the yin ruins of China [J]10.1016/j.corsci.2006.11.005 ↗
5. Photo-induced passivation: a new corrosion mitigation strategy for bronze artefacts [J]10.1016/j.corsci.2024.112401 ↗
6. Chinese bronzes: casting, finishing, patination, and corrosion [J]
7. Micro-chemical and micro-structural investigation of archaeological bronze weapons from the ayanis fortress (lake Van, Eastern Anatolia, Turkey) [J]10.1007/s00339-013-7772-6 ↗
8. Production and circulation of bronzes among the regional states in the Western zhou dynasty [J]10.1016/j.jas.2020.105191 ↗
9. Scott · 2002
10. On the degradation factors of an archaeological bronze bowl belonging to a private collection [J]10.1016/j.apsusc.2014.06.067 ↗
11. Bronze Disease-An electrochemical explanation. [J]10.1179/iccm.1981.7.1.002 ↗
12. Berthelot M.P.E. Etude sur les métaux qui composent les objets de cuivre, de bronze, d′étain, d′or et d′argent, découverts dans les fouilles de Dahchour et provenant du Musée de Gizeh. [M]//MORGAN E J D. In Fouilles à Dahchour Vienna: A. Holzhausen. 1895: 131-146.
13. Aspects of bronze patina and its treatment [J]10.1179/sic.1963.002 ↗
14. Dept. Of fine arts
15. Tracking the progression of bronze disease – a synchrotron X-ray diffraction study of nantokite hydrolysis [J]10.1016/j.corsci.2014.11.021 ↗
16. Mechanistic studies of corrosion product flaking on copper and copper-based alloys in marine environments [J]10.1016/j.corsci.2014.03.028 ↗
17. Novel insight into bronze disease gained by synchrotron-based photoelectron spectro-microscopy, in support of electrochemical treatment strategies [J]10.1080/00393630.2016.1235339 ↗
18. Morphology and mechanisms of formation of natural patinas on archaeological Cu–Sn alloys [J]10.1016/S0010-938X(98)00096-1 ↗
19. Bronze disease: a review of some chemical problems and the role of relative humidity [J]10.1179/019713690806046064 ↗
20. New insight into the nature and properties of pale Green surfaces of outdoor bronze monuments [J]10.1007/s00339-008-4468-4 ↗
21. Electrochemical basis of corrosion of cultural objects [M]
22. A mechanistic study of stratified patina evolution on Sn-bronze in chloride-rich atmospheres [J]10.1016/j.corsci.2020.108477 ↗
23. Electrochemical and spectroscopic characterizations of patinas formed on an archaeological bronze coin [J]10.1016/j.electacta.2005.01.050 ↗
24. Investigation of a Copper-based hoard from the megalithic site of al-Midamman, Yemen: an interdisciplinary approach [J]10.1006/jasc.2001.0686 ↗
25. New insights on the corrosion of ancient bronzes using X-ray powder diffraction: the importance of paratacamite, sampleite, and connellite [J]10.1080/00393630.2016.1219466 ↗
26. Raman approach to the forensic study of bronze patinas [J]
27. The characterization of Sn-based corrosion products in ancient bronzes: a Raman approach [J]10.1002/jrs.4037 ↗
28. IR and Raman spectroelectrochemical studies of corrosion films on tin [J]10.1016/S0013-4686(00)00660-5 ↗
29. A new application of fiber optics reflection spectroscopy (FORS): identification of “bronze disease” induced corrosion products on ancient bronzes [J]10.1016/j.culher.2021.03.007 ↗
30. Detecting copper trihydroxychlorides with reflectance spectroscopy and machine learning methods [J]10.1016/j.culher.2022.11.004 ↗
31. Artificial intelligence image recognition method based on convolutional neural network algorithm [J]10.1109/ACCESS.2020.3006097 ↗
32. A review of face recognition technology [J]10.1109/ACCESS.2020.3011028 ↗
33. 3-D gabor convolutional neural network for hyperspectral image classification [J]
34. Deep machine learning-A new frontier in artificial intelligence research10.1109/MCI.2010.938364 ↗
35. Autonomous structural visual inspection using Region-Based deep learning for detecting multiple damage types [J]10.1111/mice.12334 ↗
36. Misal A., Sayyad S. Corrosion Image Classification using Deep Learning and Transfer Learning Approaches; proceedings of the 2024 4th Asian Conference on Innovation in Technology (ASIANCON), F 23-25 Aug. 2024, 2024 [C].10.1109/ASIANCON62057.2024.10837953 ↗
37. Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods [J]10.1038/s41529-024-00532-z ↗
38. Chen L.-C., Papandreou G., Kokkinos I., et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs [J]. CoRR arXiv, 2014.
39. Tan M., Le Q.V. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks [Z]. 36th International Conference on Machine Learning: ICML 2019, Long Beach, California, USA, 9-15 June 2019, Part 16 of 19. Long Beach. 2019: 10691-700.
40. Automatic detection of carbon steel corrosion degree based on image identification [J]10.1016/j.commatsci.2023.112717 ↗
41. Statistical analysis of metastable pitting behavior of 2024 aluminum alloy based on deep learning [J]10.1016/j.corsci.2024.112077 ↗
42. Prediction of quantitative in-situ local corrosion via deep learning [J]10.1016/j.corsci.2024.112431 ↗
43. Quantitative prediction of Mg-RE-Ni alloy corrosion behavior by machine learning [J]10.1016/j.corsci.2024.112324 ↗
44. Fusion of multi source data for atmospheric corrosion evaluation using sensors and image recognition [J]10.1038/s41529-025-00555-0 ↗
45. Advanced multi-image segmentation-based machine learning modeling strategy for corrosion prediction and rust layer performance evaluation of weathering steel [J]10.1016/j.corsci.2024.112334 ↗
46. Surface characterization of artificial corrosion layers on copper alloy reference materials [J]10.1016/S0169-4332(02)00005-3 ↗
47. Formation processes of CuCl and regenerated cu crystals on bronze surfaces in neutral and acidic media [J]10.1016/j.apsusc.2005.08.041 ↗
48. The effects of UV and visible light on the corrosion of bronze covered with an oxide film in aqueous solution [J]10.1016/j.corsci.2019.01.009 ↗
49. The inhibition effect and mechanism of l-cysteine on the corrosion of bronze covered with a CuCl patina [J]10.1016/j.corsci.2015.04.018 ↗
50. The application of a non-destructive XRF method to identify different alloys in the bronze statue of the capitoline horse [J]10.1179/sic.1997.42.4.241 ↗
51. LAMQS and XRF analyses of ancient Egyptian bronze coins [J]10.1080/10420151003729508 ↗
52. Detection of Corrosion-Indicating oxidation product colors in steel bridges under varying illuminations, shadows, and wetting conditions [J]10.3390/met10111439 ↗
53. Detection of corrosion on steel structures using automated image processing [J]
54. Image Processing-Based pitting corrosion detection using metaheuristic optimized multilevel image thresholding and Machine-Learning approaches [J]
55. Characterizing steel corrosional features in atomic force microscopy: a sobel edge detection approach [J]
56. Identification of corrosion factors in blast furnace gas pipe network with corrosion big data online monitoring technology [J]10.1016/j.corsci.2024.111906 ↗
57. Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model [J]10.1016/j.corsci.2020.109084 ↗
58. Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning [J]10.1016/j.corsci.2020.108697 ↗
59. An improved deep forest model for forecast the outdoor atmospheric corrosion rate of low-alloy steels [J]10.1016/j.jmst.2020.01.044 ↗
60. Topological analyses of cuprite, Cu2O, using high-energy synchrotron-radiation data [J]10.1107/S0108767300010254 ↗
61. Refractive index and dispersion of fluorides and oxides [J]10.1063/1.1497384 ↗
62. The crystal structure of clinoatacamite, a new polymorph of cu 2 (OH) 3 cl [J]
63. Structures of the pseudo-trigonal polymorphs of Cu2(OH)(3)Cl [J]10.1107/S0108768109013901 ↗
64. The charge density distribution and antiferromagnetic properties of azurite Cu3[CO3]2(OH)2 [J]10.1007/s002690100176 ↗
65. Unusual mineral diversity in a hydrothermal Vein-type deposit: the clara mine, SW Germany, as a type example [J]10.3749/canmin.1900003 ↗
66. Enhanced electrochemical cu release from commercial Cu-Sn alloys: fate of the alloying elements in artificial perspiration [J]10.1016/j.electacta.2017.04.092 ↗
67. The golden alloy Cu5Zn5Al1Sn: patina evolution in chloride-containing atmospheres [J]10.1016/j.corsci.2018.01.027 ↗
68. Determination of the long term corrosion behavior of coated steel with A.C. Impedance measurements [J]10.1016/0010-938X(83)90064-1 ↗
69. The spectroscopic analysis of corrosion products on Gold-Painted Copper-Based bodhisattva (Guanyin) in half lotus position from national museum of China. [J]