Atomic Layer Deposition and Anodic Oxidation: A Good Tool Combination to Build Nanostructured Electrodes for Energy Applications
Bibliographic record
Abstract
Anodic oxidation has been used for a longtime as a way to passivate metallic electrodes to prevent corrosion. It is the discovery of self-ordered nanoporous alumina membranes reported in 1995 [1] that has revealed its potential use for surface nanostructuring. Later, the anodic growth of TiO2 nanotubes (TiO2-nt) has considerably widened the field of applications of such approach since TiO2 exhibits many valuable properties [2]. It is now possible to use other metals or alloys to grow porous or tubular oxidized nanostructures. However to further improve the properties of such electrodes, it is necessary to functionalize their surface with other materials of interest. Among the various thin film deposition methods, Electrochemical Deposition (ED) and Atomic Layer Deposition (ALD) have shown a great ability to conformally coat porous structures exhibiting a high aspect ratio. The main advantages of ALD over ED is that it can be carried out onto non-conductive materials, it allows an accurate control of the thickness and it is usually more direct to grow oxides or nitrides. We report, here, three examples of nanostructured electrodes fabricated using anodic oxidation of Al and Ti in combination with ALD of active materials. The targeted applications are in the fields of electrocatalysis, Li-ion microbatteries and photoelectrochemical water splitting. Direct Ethanol Fuel Cells offer significant advantages due to ethanol non-toxicity and renewability and its high power density. TiO2 have been successfully used as replacement of C as catalyst support because it exhibits a good chemical stability and it can enhance the activity of the catalyst. After a brief reminding on the TiO2-nt fabrication and properties, the ALD of Pd nanoparticles into TiO2-nt array will be presented. The electrochemical activity toward ethanol oxidation has been tested in alkaline medium. Although a high and stable electroactivity has been measured further improvements such as annealing of the TiO2-nt and ALD of SnO2 onto the TiO2-nt have been proposed. As seen on Fig. 1a, the electrochemical response is at its highest when the tubes have been annealed and covered by SnO2. A 3D nano-architectured composite negative electrode has been fabricated in order to be used in microbatterie. It consists of TiO2-nt coated by a thin SnO2 film grown by ALD. TiO2-nt are known to exhibits very good insertion properties and the SnO2 coating that displays a high lithium insertion capability (max. theoretical capacity of 720 µAh/cm2·µm) is used to enlarge the capacity of the electrode. Such composite 3D structure increases therefore the active area, improves the kinetics of the system, facilitates the ion exchange at the electrode/electrolyte interface and, better accommodates the volume expansion induced by the Li insertion within the LixSn alloy. The ALD of SnO2 will be described in details. The influence of various parameters such as precursor nature, exposure sequence as well as reaction temperature on the deposit morphology, chemistry and crystalline structure will be presented. The electrochemical performances of such systems have been tested as function of the SnO2 film thickness and after thermal treatments that change the crystalline structure of the electrodes. The results show that TiO2/SnO2 delivers a promising capacity (~150 µAh/cm2) which is better than bare TiO2-nt (Fig. 1b). It demonstrated then that such 3D nano-architectured composite electrode opens valuable perspectives for microbatteries design. The last example consists of producing a nanostructured photocathode for water splitting in which the photogeneration and transport of the charge carriers do not occur in the same material. In order to create tailored 3D nanostructures, p-NiO and i-Sb2S3 thin films have been successively grown into nanoporous alumina by ALD (Fig. 1c). The photocathode composition, structure and geometry are well controlled. Then the optical and photoelectrochemical properties have been investigated and optimized using cyclic voltammetry in the dark and under illumination as well as UV-visible absorption spectroscopy. It has been sought to uncover trends in the photoelectrochemical activity of the system (photocurrent density and photovoltage) and rationalize them in terms of optical and electrical functions. [1] H. Masuda, K. Fukuda, Science, 268, 1466 (1995) [2] V. Zwilling, E. Darque-Ceretti, A. Boutry-Forveille, D. David, M. Y. Perrin, M. Aucouturier, Surf. Interface Anal., 27, 629 (1999) 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".