MétaCan
Menu
Back to cohort
Record W2280536708 · doi:10.1021/acs.analchem.5b04661

Cellulose-Based Biosensors for Esterase Detection

2016· letter· en· W2280536708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2016
Typeletter
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersMinistry of Technology, Innovation and Citizens' ServicesNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for Innovation
KeywordsChemistryBiosensorCelluloseEsteraseChromatographyBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Cellulose has emerged as an attractive substrate for the production of economical, disposable, point-of-care (POC) analytical devices. Development of novel methods of (bio)activation is central to broadening the application space of cellulosic materials. Ironically, such efforts are stymied by the inherent biocompatibility and recalcitrance of cellulose fibers. Here, we have elaborated a versatile, chemo-enzymatic approach to activate cellulosic materials for CuAAC "click chemistry", to develop new fluorogenic esterase sensors. Gentle, aqueous modification conditions facilitate broad applicability to cellulose papers, gauzes, and hydrogels. Tethering of the released fluorophore to the cellulose surface prevents signal degradation due to diffusion and enables straightforward, sensitive visualization with a simple light source in resource-limited situations.

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.

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score1.000

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 integrity0.0020.001
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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