Controlling the Surface Properties of Electrodeposited Ni Films
Bibliographic record
Abstract
Polymer/metal composites, including polymer films deposited on metallic layers, are very important because of the increasing demand of these advanced materials in multiple applications, such as in microelectronics, automotive, aerospace, and medical devices [1]. The surface topography of the metal substrate is a critical factor in producing a good adhesion surface, particularly for polymer films. In this work, electrodeposition was used to create a micro/nano-structured Ni surface with specific engineered properties, with Ni selected as it has excellent abrasion, erosion, and corrosion resistance and can therefore be used in many aggressive environments. While previous work [e.g., 2,3] has examined the effect of electrodeposition conditions on the resulting Ni surface morphology and crystallite size, particularly to achieve a high surface areas [4,5], our goal was to determine the optimum surface properties to enhance the adhesion of various elastomeric coatings, while also ensuring that a high strength Ni layer was produced. In the present work, a Watt’s bath was employed for Ni electrodeposition [2], with the main variables being the applied current density, temperature, stirring rate, and chemical additives, resulting in microstructures of a range of shapes and roughness [6, 7], where the surfactant was used to minimize hydrogen bubble adhesion under negative polarization [8]. A range of Ni substrates was examined, including Ni plates, rods and electroformed Ni, having a thickness of ca. 0.1 mm and a complex shape, intended for use particularly in aerospace applications. For our purposes, it was found that low current densities of ~ 5 mA/cm2 and a temperature of 40 oC produced surface morphologies having the desired characteristics. An example of SEM and 3D optical profilometry images of an electrodeposited Ni deposited on a Ni plate is shown in Fig. 1, with the applied current density clearly affecting the surface roughness and crystallite shape. At lower current densities, the electrodeposited Ni surface exhibited pyramidal-like structures, interconnected with a finer matrix and having a high roughness factor, while at higher current densities, the surface is fully covered by a very fine structure with a small grain size and a lower degree of roughness. The results also showed that the adhesion of the electrodeposited Ni films on the Ni substrate is better for samples prepared at lower current densities. This presentation will also discuss the results of pulsed electrodeposition and electrodeposition-dissolution methods to produce the desired Ni surface morphology and strength characteristics, as well as the results of elastomer adhesion testing. References [1] Yacobia BG, Martin S, Davis K, Hudson A, Hubertb M. J Appl Phys 91(2002)6227. [2] S. Shriram, S. Mohan, N.G. Renganathan, R. Venkatachalam, Transactions of the IMF, 78:5(2000)194. [3] C. Ma, S. C. Wang & F. C. Walsh, Transactions of the IMF, 93:1(2015)8. [4] I. Herraiz-Cardona, E. Ortega, L. Vázquez-Gómez, V. Pérez-Herranz, International Journal of Hydrogen Energy, 37:3(2012)2147. [5] H. Shin, J. Dong, M. Liu, Advanced Materials 15(2003)1610. [6] Y. Deng, H. Ling, X. Feng, T. Hang, M. Li, CrystEngComm 17(2015)868. [7] T. Hang, M. Li, Q. Fei, D. Mao, Nanotechnology 19(2008)035201. [8] Chen L, Wang L, Zeng Z, Zhang J. Materials Science and Engineering: A. 434 (2006)319. Figure 1. SEM and 3D profilometry images of Ni films electrodeposited on a Ni plate at (a,b) lower (5 mA/cm2) and (c,d) higher current densities (60 mA/cm2). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".