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Record W2625919274

Self-Assembled N-Heterocyclic Carbene Monolayers on Gold as a Tunable Platform for Designing Biosensor Surfaces

2017· dissertation· en· W2625919274 on OpenAlexfundno aff
Zhijun Li

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsnot available
FundersQueen's University
KeywordsBiosensorMonolayerCarbeneNanotechnologySelf-assembled monolayerMaterials scienceCombinatorial chemistryChemistryOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Surface plasmon resonance (SPR)-based biosensing is an excellent tool to probe the recognition process between biomolecules in real-time, without need for labels. Thiol-based self-assembled monolayers (SAMs) on gold have been widely used as linker layers for biosensor chips used in SPR. However, their use is hampered by the gold-thiol linkage’s high propensity for oxidation in ambient atmosphere. To this end, a new family of materials based on N-heterocyclic carbenes (NHCs) has emerged as an alternative anchor for surface modification. Most importantly, the NHC SAMs outperform thiol analogues under a wide range of harsh conditions. Here, we compare the performance of an alkylated NHC SAM on gold with a commercial thiol-based analogue, a hydrophobic association (HPA) chip. Compared to the thiol-based sensor surface, the NHC sensor surface features a desired list of performance merits, including lower non-specific binding capacity, better chemical stability, higher reproducibility, shorter equilibration time, and longer life span. We also demonstrate that the NHC sensor surface can be used for rapid and efficient formation of a hybrid lipid bilayer for use in membrane interaction studies. A sensor surface consisting of an NHC SAM coupled to carboxymethylated dextran was also developed. Standard SPR instrument tests demonstrated the NHC-dextran surfaces to be homogeneous and sufficiently responsive to meet performance standards. In terms of the real life biosensing validation, we found that the NHC-supported dextran surfaces yielded comparable performance to commercial thiol supported biosensor surfaces in kinetic analysis of drug/plasma protein and antibody/antigen interactions. Three types of NHC-based dextran surfaces derivatized with affinity capture functional molecules to enable specifically oriented binding of ligands for biosensing applications were developed: NHC-Streptavidin (SA), NHC-Nitrilotriacetic acid (NTA), and NHC-Protein A. Our results show that, when properly designed and applied, the NHC-based platform has the potential that allows facile tuning of surface properties in a highly divergent fashion, enabling various ligand to be efficiently immobilized for reliable biomolecular interactions. These results highlight the potential viability of chemical modifications to gold surfaces using NHC ligands as a robust and versatile platform to enable efficient evaluation of a wide range of biomolecular interactions.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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
Published2017
Admission routes1
Has abstractyes

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