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Record W4381856139 · doi:10.1007/s00706-023-03091-2

Spatial characterization of peptide nucleic acid molecularly imprinted inverse opal

2023· article· en· W4381856139 on OpenAlexafffund
Sebastián Estrada, Sabrina Gaidies, Jana Febbraro, Ayse Turak, Hong‐Ru Lin, Yolanda Salinas, Oliver Brüggemann

Bibliographic record

VenueMonatshefte für Chemie - Chemical Monthly · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsConcordia UniversityMcMaster University
FundersOntario Ministry of Research and InnovationÖsterreichischen Akademie der Wissenschaften
KeywordsPeptide nucleic acidMaterials scienceBiological systemNucleic acidNanotechnologyCharacterization (materials science)PolymerInverseChemistryMathematicsBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract The combination of molecularly imprinted polymers (MIPs) and inverse opals (IO) have been a point of interest in the past few years due to their potential in sensing applications. At the same time, peptide nucleic acid (PNA) is a stable analogue to natural occurring genetic material. In this study, we describe the preparation and characterization of a PNA imprinted matrix, based on the controlled self-assembly of organized silica particles (SiPs) arrays. The degree of organization of the silica arrays are compared to the organization of the cavities after the removal of the SiPs, using spatial statistical analysis. This analysis of the Voronoi tessellations, pair correlation functions and bond order showed that the successfully formed arrays contain a high degree of quasi-hexagonal (hexatic) organization of the cavities, with both global and local order. The adsorption analysis of the materials show potential for developing future materials with tunable structural reflective properties, such as on-site, color- changing genetic material sensor. Graphical abstract

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
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 integrity0.0000.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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