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Record W4391637901 · doi:10.1149/ma2023-02552684mtgabs

In Situ Transmission Electron Microscopy and Soft X-Ray Spectro-Microscopy to Understand Electrochemical Processes

2023· article· en· W4391637901 on OpenAlexaff
Drew Higgins Higgins, Ahmed Abdellah, Chunyang Zhang, Kholoud Abousalem, Robert W. Black, Haytham Eraky, Adam P. Hitchcock

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsNational Research Council CanadaMcMaster University
Fundersnot available
KeywordsTransmission electron microscopyIn situMicroscopyElectron microscopeMaterials scienceElectrochemistryX-rayScanning confocal electron microscopyEnergy filtered transmission electron microscopyNanotechnologyOpticsElectrodeScanning transmission electron microscopyChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO 2 conversion offers a route to use renewable sources of electricity to convert CO2 into valuable carbon-based fuels and chemicals, including carbon monoxide, ethanol and ethylene. For electrochemical CO 2 conversion technologies to become a viable component of future sustainable energy infrastructures, improved performance materials (catalysts, electrodes, membrane electrode assemblies) are needed to achieve high conversion rates, selectivity and single-pass utilization of CO 2 . This talk will focus on the development of techniques to characterize the properties of electrochemical CO 2 conversion materials under reaction conditions. These in-situ methods are producing results which will guide the design of next generation materials and reactors. The talk will focus primarily on in situ transmission electron microscopy (TEM) and related spectroscopic techniques (energy dispersive X-ray analysis and selected area electron diffraction), along with synchrotron-based methods including in-situ soft X-ray scanning transmission X-ray microscopy (STXM).

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 score0.010
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
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.296
Teacher spread0.284 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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