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Adsorption of HCl on Single-Crystal α-Al2O3 (0001) Surface: A DFT Study

2003· article· en· W6903168576 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAdsorptionPseudopotentialDensity functional theorySupercellBinding energyMoleculeDissociation (chemistry)

Abstract

fetched live from OpenAlex

First principles calculations based on density functional theory (DFT) and the pseudopotential method have been used to study the adsorption of HCl on the basal plane of an -Al2O3 crystal. The calculations accurately reproduce the energetic and structural properties of bulk alumina and of the -Al2O3 (0001) surface. A 2 ? 2 supercell slab model was used to study both the molecular and dissociative adsorption of HCl on the -Al2O3 (0001) surface. Our calculations indicate that the dissociative configurations have adsorption energies that are at least 28 kcal/mol greater than the molecular configurations on the surface. Several ionic adsorption configurations have been investigated in which the proton is adsorbed on a nearest neighbor surface O-ion site (1-2 adsorption), or a next nearest neighbor surface O-site (1-4 adsorption). We have found that the highest binding energy corresponds to 1-2 adsorption. Analysis of the surface coverage effects shows that by increasing the coverage of 1-2 adsorbed HCl molecules to a full monolayer, the adsorption energy of each HCl decreases by about 10 kcal/mol as a result of repulsions between neighboring molecules. Implications of HCl binding to particles of -Al2O3 released in the exhaust of the space shuttle booster rockets on the active chlorine-producing reaction in the stratosphere are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2003
Admission routes1
Has abstractyes

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