EFFCTS OF pH AND Cl− CONCENTRATION ON THE CORROSION BEHAVIOR OF COPPER IN BORIC ACID BUFFER SOLUTION
Bibliographic record
Abstract
The strategy for disposal of high-level radioactive waste in china is to enclose the spent nuclear fuel in sealed metal canisters which are embedded in bentonite clay hundreds meters deep in the bed-rock.The choice of container material depends largely on the redox conditions and the aqueous environment of the repository.One of the choices for the fabrication of waste canisters is copper, for it is thermodynamically stable under the saline,anoxic conditions over the large majority of the container lifetime.For this advantage,some other countries(Canada,Sweden) have selected copper as the material of nuclear waste container.However,in the early aerobic phase of the geological disposal the corrosion of copper could take place,and the corrosion behavior of copper would be influenced by the complex chemical conditions of groundwater markedly.Regularly,in atmosphere environment the semiconductor passive film which is constructed by Cu_2O would generate on the surface of copper.On the one hand,the Cu_2O film could protect copper from corrosion.On the other hand,the formation of Cu_2O film is necessary to maintain the propagation of crack during the stress corrosion cracking. Therefore,the study of the effect of water chemistry conditions on the stability of Cu_2O passive film makes great sense for the general corrosion and stress corrosion cracking of copper.For copper and copper alloy,Cl~- is a highly aggressive ion.Lots of cases of failure and corrosion behavior of copper and copper alloy in Cl~- environment have been studied.The immersion of Cl~- would affect the semiconductor properties of Cu_2O,therefore,we focused on the effect of pH and Cl~- concentration on the corrosion behavior and semiconductor properties of Cu_2O in this paper.The polarization behavior of Cu electrodes and the stability of passive film Cu_2O in boric acid buffer solution have been investigated respectively by potentiodynamic polarization,electrochemical impedance spectroscopy (EIS) and semiconductor capacitance method(Mott-Schottky method).The results showed that the destruction and dissolution of the passive film Cu_2O happened in the conditions of low pH and high Cl~- concentration.The semiconductor character of passive film changed from p-type into n-type in the solution with high Cl~- concentration,so that Cl~- is more likely to enter the passive film and take complex reaction with Cu~+,which could destroy the passive film and accelerate corrosion.The passive film Cu_2O is more steady in the condition of high pH and low Cl~- concentration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".