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Record W4396670358 · doi:10.1103/physrevb.109.195416

Adsorption model for atoms and molecules on doped semiconducting oxides

2024· article· en· W4396670358 on OpenAlexfundno aff
Abhinav S. Raman, Colin Lehman-Chong, Aleksandra Vojvodić

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectronic and Structural Properties of Oxides
Canadian institutionsnot available
FundersNational Energy Research Scientific Computing CenterCanadian Institute for Advanced ResearchU.S. Department of EnergyNational Science Foundation
KeywordsDopantAdsorptionMoleculeDopingMaterials scienceOxideChemical physicsMetalCrystallographyPhysical chemistryChemistry

Abstract

fetched live from OpenAlex

Fundamental understanding of the interaction between atoms and molecules with the surfaces of oxides including semiconducting oxides is crucial for the development of several thermo-, photo-, and electrocatalytic reactions as well as any application where surfaces are exposed to an environment beyond vacuum. While previous studies have postulated material features (descriptors) that to some extent suggest the adsorption energy trends on semiconducting oxides, a physics based model to describe the interaction of atoms and molecules with the surfaces of these materials is still lacking. In this study, we perform a series of controlled in silico experiments involving doping of quintessential semiconducting oxides (${\mathrm{SrTiO}}_{3}, {\mathrm{SrZrO}}_{3}$, and ${\mathrm{TiO}}_{2}$) to identify the perturbation by the dopant to the electronic structure of the host oxide and its resultant effect on the adsorption energies of simple atoms and molecules. We identify that a combination of three surface features: unique surface resonance states of the host-metal and lattice oxygen atoms of the terminating surface oxide layer as well as the gap states dominated by the introduced dopants contribute to the adsorption energy in a concerted fashion. We find that this intricate interplay between on the one hand host-metal and on the other hand oxygen surface resonance states with the adsorbate, respectively, results in a deviation from the well-established adsorbate scaling relations seen for ${\mathrm{NH}}_{x}$ ($x=0$--2) and ${\mathrm{CH}}_{x}$ ($x=0$--3) but not ${\mathrm{OH}}_{x}$ and ${\mathrm{SH}}_{x}$. Through this lens, we develop a physics based adsorption model hitherto referred as the generalized concerted coupling model (GCC model). The introduced model provides a physical understanding with an associated electronic structure descriptor rooted in the unique surfaces resonances that accurately captures the adsorption energy trends on doped semiconducting oxides. This paves the way for the atomistic design of doped semiconducting oxides for different catalytic applications, including sustainable energy applications such as electrochemical water splitting.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.034
GPT teacher head0.367
Teacher spread0.333 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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Same venuePhysical review. B./Physical review. BSame topicElectronic and Structural Properties of OxidesFrench-language works237,207