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Record W2769909358 · doi:10.1039/c7dt03518g

All-gas-phase synthesis of amino-functionalized UiO-66 thin films

2017· article· en· W2769909358 on OpenAlexfundno aff
Kristian Blindheim Lausund, Veljko Petrovic, Ola Nilsen

Bibliographic record

VenueDalton Transactions · 2017
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsnot available
FundersUniversitetet i OsloRyerson University
KeywordsGas phaseMaterials scienceThin filmPhase (matter)Chemical engineeringNanotechnologyCombinatorial chemistryChemistryPhysical chemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Thin films of metal-organic frameworks (MOFs) prepared using all-gas-phase techniques such as atomic/molecular layer deposition (ALD/MLD) are emerging due to their potential for enabling suitable applications. Their high and specific porosity enables their use as membranes for separations and as a basis for sensors in microelectronics, provided that films can be made. The properties of such MOF materials can be tuned by choosing linker molecules that are functionalized with a variety of chemical groups. However, thin films of these functionalised MOFs have so far been prepared through wet based chemistries, which are difficult to combine with microelectronics and high aspect ratio structures. We here report on the thin film deposition of amino-functionalised UiO-66 through an all-gas-phase ALD/MLD process. By using amino-functionalised linkers, modulation by acetic acid to control the stoichiometry of the deposited film was no longer required, as opposed to the case in which unmodified terephthalic acid was used as a linker. The growth and properties of the films were characterised using an in situ quartz crystal microbalance (QCM), spectroscopic ellipsometry (SE), grazing incidence X-ray diffraction (GIXRD), Fourier transform infrared spectroscopy (FTIR) and other techniques to obtain information on their growth dynamics and physical properties.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.699
Threshold uncertainty score0.973

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0410.000

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 head0.027
GPT teacher head0.283
Teacher spread0.256 · 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

Citations54
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDalton TransactionsSame topicMetal-Organic Frameworks: Synthesis and ApplicationsFrench-language works237,207