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Low-dose (S)TEM elemental analysis of water and oxygen uptake in beam sensitive materials

2019· article· en· W2980149919 on OpenAlexaff
Zino J. W. A. Leijten, Maarten Wirix, Mike Strauss, Jürgen M. Plitzko, Gijsbertus de With, Heiner Friedrich

Bibliographic record

VenueUltramicroscopy · 2019
Typearticle
Languageen
FieldEngineering
TopicOrganic Electronics and Photovoltaics
Canadian institutionsMcGill University
FundersAgence de l'Environnement et de la Maîtrise de l'Energie
KeywordsOxygenAcceptorChemistryElectron acceptorAnalytical Chemistry (journal)InterphaseChemical physicsMaterials scienceNanotechnologyPhotochemistryEnvironmental chemistryPhysics

Abstract

fetched live from OpenAlex

The performance stability of organic photovoltaics (OPVs) is largely determined by their nanoscale morphology and composition and is highly dependent on the interaction with oxygen and water from air. Low-dose cryo-(S)TEM techniques, in combination with OPV donor-acceptor model systems, can be used to assess oxygen- and water-uptake in the donor, acceptor and their interface. By determining a materials dependent critical electron dose from the decay of the oxygen K-edge intensity in Electron Energy Loss Spectra, we reliably measured oxygen- and water-uptake minimizing and correcting electron beam effects. With measurements below the dose limit the capability of STEM-EDX, EFTEM and STEM-EELS techniques are compared to qualitatively and quantitatively measure oxygen and water uptake in these OPV model systems. Here we demonstrate that oxygen and water is mainly taken up in acceptor-rich regions, and that specific oxygen uptake at the donor-acceptor interphase does not occur. STEM-EELS is shown to be the best suitable technique, enabling quantification of the local oxygen concentration in OPV model 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.008
Threshold uncertainty score0.483

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 head0.003
GPT teacher head0.200
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2019
Admission routes1
Has abstractyes

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