Ixora coccinea extract as an efficient eco-friendly corrosion inhibitor in acidic media: Experimental and theoretical approach
Bibliographic record
Abstract
The corrosion inhibition power of an eco-friendly green inhibitor Ixora coccinea extract (ICE) for mild steel in 1 M HCl and 0.5 M H2SO4 was analysed using physicochemical and electrochemical techniques. 1–5 v/v% ICE was prepared and added into the corrosive acidic media for different analyses. Weight loss measurements conducted for a period of 24hrs for mild steel immersion revealed 89.38% and 77.96% inhibition capacity of ICE in 1 M HCl and 0.5 M H2SO4 medium respectively. Electrochemical impedance parameters show that as concentration is increased, efficiency increases and double layer capacitance decreases. Potentiodynamic polarization techniques are also in good agreement with impedance studies and exhibit the mixed type inhibition character of ICE. Electrochemical noise spectrum also strongly supports the anti-corrosive property of ICE and the magnitude of noise signal decreases concerning the increase in ICE concentration. Ixorene, which is one of the major constituents of Ixora coccinea leaves was also studied for its corrosion inhibition nature by quantum mechanical calculations and was found to match with all other results. Adsorption studies of ICE are in accordance with Langmuir isotherm. Surface morphological studies confirm the formation of a protective barrier on mild steel surface in both media, i.e., 1 M HCl and 0.5 M H2SO4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".