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Record W2799547137 · doi:10.1039/c8ob00485d

Molecular engineering of logic gate types by module rearrangement in ‘Pourbaix Sensors’: the effect of excited-state electric fields

2018· article· en· W2799547137 on OpenAlexaff
Jake C. Spiteri, Sergey A. Denisov, Gediminas Jonušauskas, Sylwia Klejna, Konrad Szaciłowski, Nathan D. McClenaghan, David C. Magri

Bibliographic record

VenueOrganic & Biomolecular Chemistry · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsCanadian Nautical Research Society
FundersHorizon 2020 Framework ProgrammeCentre National de la Recherche ScientifiqueMalta Council for Science and Technology
KeywordsPourbaix diagramLogic gateExcited stateChemistryState (computer science)AND gatePass transistor logicElectronic engineeringOptoelectronicsElectrical engineeringComputer scienceQuantum mechanicsAlgorithmElectronic circuitEngineeringPhysicsElectrodePhysical chemistryElectrochemistryDigital electronics

Abstract

fetched live from OpenAlex

Two types of fluorescent logic gates are accessed from two different arrangements of the same modular components, one as an AND logic gate (1) and the other as a PASS 0 logic gate (2). The logic gates were designed with an 'electron-donor-spacer1-fluorophore-spacer2-receptor' format and demonstrated in 1 : 1 (v/v) methanol/water. The molecules consist of ferrocene as the electron donor, 4-aminonaphthalimide as the fluorophore and a tertiary alkylamine as the receptor. In the presence of high H+ and Fe3+ levels, regioisomers 1a and 1b switch 'on' as AND logic gates with fluorescence enhancement ratios of 16-fold and 10-fold, respectively, while regioisomers 2a and 2b are functionally dormant, exhibiting no fluorescence switching. The PASS 0 logic of 2a and 2b results from the transfer of an electron from the excited state fluorophore to the ferrocenium unit under oxidising conditions as predicted by DFT calculations. Time-resolved fluorescence spectroscopy provided lifetimes of 8.3 ns and 8.1 ns for 1a and 1b, respectively. The transient signal recovery rate of 1b is ∼10 ps while that of 2b is considerably longer on the nanosecond timescale. The divergent logic attributes of 1 and 2 highlight the importance of field effects and opens up a new approach for regulating logic-based molecules.

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 score0.031
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.194
Teacher spread0.191 · 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

Citations30
Published2018
Admission routes1
Has abstractyes

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