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Record W3175227792 · doi:10.1039/d1ra02775a

Nitrogen and sulfur Co-doped carbon dots as ecofriendly and effective corrosion inhibitors for Q235 carbon steel in 1 M HCl solution

2021· article· en· W3175227792 on OpenAlexaff
Mingjun Cui, Xia Li

Bibliographic record

VenueRSC Advances · 2021
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsImpact
FundersNingbo UniversityNational Natural Science Foundation of China
KeywordsSulfurCarbon fibersNitrogenChemistryCorrosionInorganic chemistryCarbon steelEnvironmental chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

a hydrothermal procedure using citric acid (CA) and thiourea (TU) as precursors, and the corresponding corrosion protection performance was first investigated for Q235 carbon steel in 1 M HCl solution. Experimental results indicated that N,S-CDs as mixed-type corrosion inhibitors could effectively prevent Q235 carbon steel from corrosion in 1 M HCl solution, and the corrosion inhibition efficiency was improved with the increase in N,S-CD concentration. The maximum value was achieved at 400 ppm of N,S-CDs at 25 °C, which was approximately 96.6%, 94.6% and 90.55%, according to the potentiodynamic polarization curves, EIS results and weight loss measurement, respectively. Additionally, with the temperature ranging from 25 to 55 °C, the inhibition efficiency obtained from the weight loss measurement was enhanced from 90.55% to 94.04%. Such superior inhibition effect was assigned to the physisorption and chemisorption of N,S-CDs on the Q235 carbon steel surface, which was also be confirmed by XPS analysis. The adsorption of N,S-CDs onto a steel substrate conformed to the Langmuir adsorption isotherm.

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)0.000
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 score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.008
GPT teacher head0.274
Teacher spread0.267 · 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

Citations61
Published2021
Admission routes1
Has abstractyes

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