A comparative study on corrosion resistance of <scp>316 L</scp> stainless‐steel welds in nutrient fertilizer solutions such as Ca( <scp> NO <sub>3</sub> </scp> ) <sub>2</sub> , <scp> KNO <sub>3</sub> </scp> , <scp> KH <sub>2</sub> PO <sub>4</sub> </scp>
Bibliographic record
Abstract
Abstract Electrochemical polarization and electrochemical impedance spectroscopy (EIS) tests were performed to evaluate corrosion resistance of 316 L weld metal in different nutrient solutions at the temperature of 20°C. Both hot‐rolled 316 L plates with dimensions of 200 mm × 100 mm × 8 mm were welded together by manual metal arc welding with an E316L‐16(A022) electrode. Welding was performed using direct current electrode negative (DCEN). A welding current of 88 ~ 115A, arc voltage of 22 ~ 23 V, and welding speed of 120 ~ 160 mm/min were used to manufacture specimens, while inter‐pass temperatures were maintained below 60°C. Potentiodynamic polarization measurements were taken at a scan rate of 10 mV/s at a potential initiated at −600 to +1600 mV. EIS measurements were performed at corrosion potential value by employing a signal with amplitude of 10 mV in a frequency range of 100 mHz–100 kHz. Observation of the optical microstructure of parent material revealed the equiaxed austenite grain, delta ferrite stringers, and micro‐carbides distributed along cold rolling in the inner region. Weld metal zones consisted of an austenite/skeletal and spherical delta ferrite structure. The polarization test showed that corrosion potentials of the weld metal (−0.377 V, −0.268 V, −0.350 V, respectively, in 45 wt.% KH 2 PO 4 , 45 wt.% KNO 3 , and 45 wt.% Ca(NO 3 ) 2 ) were clearly lower than those of the parent material (−0.233, −0.199, and −0.015 V) due to the heterogeneity of the microstructure in the welds. EIS data indicate that the corrosion mechanism is under diffusion control in the 45 wt.% KNO 3 solution, and under charge transfer control in the 45 wt.% KH 2 PO 4 and 45 wt.% Ca(NO 3 ) 2 solutions. The results showed that there was need for corrosion protection by inhibitors in the 45 wt.% KH 2 PO 4 and 45 wt.% Ca(NO 3 ) 2 solutions.
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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 | 0.000 | 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 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".