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Record W4285398974 · doi:10.1149/ma2022-01451876mtgabs

(Invited) Improved Thermal Stability of Low-Probe Density DNA SAMs Prepared with Electrodeposition

2022· article· en· W4285398974 on OpenAlexaff
Tianxiao Ma

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThermal stabilityMonolayerBiosensorElectrodeSubstrate (aquarium)Materials scienceNanotechnologyDeposition (geology)ElectrochemistryChemical engineeringAnalytical Chemistry (journal)ChemistryChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

DNA self-assembled monolayer (SAM) on gold electrodes has been widely used for biosensing.1, 2 The sensor stability and performance determine the reliability of the test results in early disease detection and point-of-care use.3, 4 We have carefully examined the thermal stability of a mixed SAMs composed of a short alkylthiol and alkylthiol modified DNA prepared with electrodeposition vs. those prepared at OCP (Open Circuit Potential) on a single crystal gold bead electrode. The nature of these two types of SAMs were carefully studied in buffer after exposure to different temperatures using in-situ fluorescence microscopy and electrochemical measurements. The analysis showed that surfaces with hexagonal symmetry were thermally less stable than those with rectangular or square symmetry, suggesting that the crystallography of the gold substrate plays an important role in sensor stability and performance in addition to the DNA density.5 We also found that OCP deposition creates a surface with high DNA coverage (>1012 DNA/cm2) but with higher level of irreproducibility. Even though this kind of surface can be more thermally stable, it is not optimal for effective biosensing due to its high coverage.6 It also seems to be challenging to make DNA SAMs with both low coverage and high thermal stability by using current SAM preparation protocols (e.g., OCP). In this study, we show that electrodeposition can prepare DNA SAMs with both higher thermal stability than those made at OCP and with good control of low surface coverage. This study provides valuable insights on making high quality DNA SAMs with better stability and functionality and improved reproducibility. References: Brittain, W. J.; Brandsetter, T.; Prucker, O.; Rühe, J. ACS Appl. Mater. Interfaces 2019, 11, 39397–39409. Civit, L.; Fragoso, A.; O’Sullivan, C. K. commun. 2010, 12, 1045–1048. Sheridan, C. Biotechnol. 2020. Panjan, P.; Virtanen, V.; Sesay, A. M. Talanta 2017, 170, 331–336. Ma, Tianxiao; Dan Bizzotto. Analytical Chemistry. 2021. 93 (48), 15973–15981. Peterson, A. W.; Heaton, R. J.; Georgiadis, R. M. Nucleic Acids Res. 2001, 29, 5163–5168. Figure 1

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.224
Teacher spread0.217 · 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
Published2022
Admission routes1
Has abstractyes

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