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Record W4310598234 · doi:10.1002/cplu.202200362

A Tool, an App and a Field: Fluorescent PET Sensors, Blood Electrolyte Analysis and Molecular Logic as Products of Supramolecular Photoscience from Northern Ireland and Sri Lanka

2022· review· en· W4310598234 on OpenAlexfundno aff
Chaoyi Yao, Prasanna N. de Silva

Bibliographic record

VenueChemPlusChem · 2022
Typereview
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersEuropean CommissionQueen's UniversityJapan Society for the Promotion of ScienceQueen's University BelfastEngineering and Physical Sciences Research CouncilUniversity of ColomboChina Scholarship CouncilLeverhulme TrustProcter and Gamble
KeywordsSupramolecular chemistryPhotoinduced electron transferRotaxaneFluorescenceChemistryMoleculeField (mathematics)Molecular machineMolecular orbitalNanotechnologyComputer scienceCombinatorial chemistryMaterials scienceElectron transferOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract The general tool of fluorescent PET (photoinduced electron transfer) sensors/switches – a molecular design principle with engineering features – is outlined, with the aid of frontier orbital energy diagrams. Fluorophores such as anthracene, 1,3‐diaryl‐Δ 2 ‐pyrazolines and 4‐amino‐1,8‐naphthalimides are employed within this system, alongside receptors such as amines, carboxylates, crown ethers and amino acids. This tool appealed to a multinational corporation for building a medical analyzer for electrolytes such as Na + , K + , Ca 2+ and gases like CO 2 , which became a commercially successful application. Finally, the tool was a springboard for chemistry to cross into computer science. The field of molecular logic can elucidate how molecules inside us handle information. Molecular examples of the simplest logic gates such as YES, NOT, OR, AND are described. A case of a human‐level computation – visual edge detection – is also included.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.251
Teacher spread0.238 · 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
GenreReview

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

Citations2
Published2022
Admission routes1
Has abstractyes

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