MétaCan
Menu
Back to cohort
Record W4410364483 · doi:10.5772/intechopen.1010442

Porphyrin Optical Sensing: Three Applications

2025· book-chapter· en· W4410364483 on OpenAlexfundno aff
Gamal Khalil

Bibliographic record

VenueIntechOpen eBooks · 2025
Typebook-chapter
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
FundersYork UniversityUniversity of WashingtonUniversity of ConnecticutAbbott Laboratories
KeywordsPorphyrinMaterials scienceComputer scienceRemote sensingOptoelectronicsGeologyChemistryPhotochemistry

Abstract

fetched live from OpenAlex

Porphyrin derivatives play an important role in various applications, including optical chemical sensors. The electronic structure of the inner 16-membered ring in porphyrins, which consists of 18 electrons, governs their key optical features. Different classes of porphyrin molecules provide a diverse range of ring structures and substitutions, which can be effectively utilized to tune or modify specific spectral properties. Additionally, these properties are influenced by chemical modifications or changes in the pyrrole rings. Porphyrin is a highly symmetrical molecule with two pairs of low-energy excited states: quasi-forbidden Q states in the visible region and strongly allowed B states in the near-UV region. Over the past 30 years, our research group, in collaboration with other organizations, has played a key role in developing a wide array of optical porphyrin-based sensors by exploring the complex spectral properties of porphyrin derivatives. Here, we present three applications of optical porphyrin-based sensors: continuous blood-oxygen measurements for intensive care unit (ICU) use, pressure-sensitive paint for aeronautic wind tunnel measurement, and pH imaging in cementitious materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.006

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueIntechOpen eBooksSame topicAnalytical Chemistry and SensorsFrench-language works237,207