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Record W4405315437 · doi:10.3390/cmd5040029

Ascorbic Acid, Ascorbate, and Dehydroascorbic Acid as Green Corrosion Inhibitors: A Computational Investigation

2024· article· en· W4405315437 on OpenAlexaff
Bruno De Souza, Mateus R. Lage, Adenílson O. dos Santos, Francisco F. de Sousa, Rodrigo Gester, Stanislav R. Stoyanov, Tarciso Andrade‐Filho

Bibliographic record

VenueCorrosion and Materials Degradation · 2024
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsNatural Resources Canada
FundersFundação Amazônia Paraense de Amparo à PesquisaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDehydroascorbic acidAscorbic acidChemistryCorrosionBiochemistryNuclear chemistryFood scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Ascorbic acid is widely used as an immunity-enhancing and antioxidant supplement for treating influenza and other virus-based illnesses. The lactone ring and the oxygenated groups make this system and derived structures attractive as possible environmentally friendly green corrosion inhibitors. Thus, we investigate the corrosion inhibition influence of ascorbic acid, ascorbate, and dehydroascorbic acid on the α-Fe(110) surface using density functional theory calculations. The adsorption, density of states, and charge transfer results indicate that dehydroascorbic acid is this series’s most potent corrosion inhibitor. The projected density of states near the Fermi energy reveals notable hybridization between the iron surface and dehydroascorbic acid adsorbed on it. The calculated structural, electronic, and energetic properties obtained in this work pave the way for understanding the corrosion inhibitory performance of the investigated systems.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.255
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

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