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Record W7115811618

Facile and Flexible Glucose Sensors Based on Copper Thin Films

2023· dissertation· en· W7115811618 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
FundersUniversity of Texas at DallasMcMaster University
KeywordsElectrodeBiosensorFabricationElectrochemistryGlucose oxidaseAmperometryCopper
DOInot available

Abstract

fetched live from OpenAlex

The electrochemical enzymatic electrodes dominate the world market for blood glucose monitoring devices for controlling, as well as reducing the detrimental effects of diabetes. However, the enzymatic electrodes exhibit constraints restricting their reliance on the enzyme’s activity which can be influenced by the external, and the environmental factors such as temperature, pH, and humidity etc. However, the greater thickness of the enzyme layer hinders the performance of the glucose biosensors resulting in signal dampening or loss. In addition, the selectivity of the electrodes is affected by the interferents present in blood. Moreover, the invasive nature of the electrodes is a major problem considering the patient’s perspective. In contrast, recent research activities demonstrated that the electrochemical non-enzymatic electrodes possess huge potential for inexpensive and highly sensitive glucose monitoring devices, yet these electrodes are invasive in nature. Therefore, the purpose of this research was to fabricate electrochemical non-enzymatic non-invasive electrodes for sweat glucose monitoring devices. A very simple low-cost fabrication technique has been shown to make the facile, flexible, and inexpensive electrodes to detect sugar in sweat bio-analyte for a non-invasive glucose monitoring system using the native stable Cu oxides (CuNOx), Cu2O, layers grown on 35 µm thin Cu foils keeping under ambient conditions (25℃- and 760-mm Hg) for more than 2 years so that the oxide layers are full-grown, and fully stable. Moreover, the foils also annealed at various temperatures such as 160, 230, and 280℃ with new temperature profile for reducing the required time of growing stable oxides and producing oxides with larger crystallized structures with higher surface – to – volume ratio. The X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM) results supported that at 280℃ annealing temperature the surface, mostly, transformed into highly electrocatalytic CuO with larger grain sizes, crystallized structures, and the uniform layer of ~ 140 nm. The electrochemical characterization, and sensing performance of the electrodes have been done by cyclic voltammetry (CV), one of the excellent and well accepted electrochemical methods, with the 3 – electrode configuration of the potentiostat. The CuNOx sensors of having ~10 nm layer of stable Cu2O exhibited a sensitivity of 603.42 μA mM−1 cm−2, a linear range beyond the desired limit of 7.00 mM with excellent linearity (R2 = 0.9983) and a low limit of detection of 94.21 μM. In contrast, the new annealing profile has. the CuNOx sensors annealed at 280 ºC using new temperature profile provided twin calibration curves of linear ranges of 0.05 – 1.00 mM and 1.00 – 7.00 mM, that applicable for sweat and blood glucose sensing, respectively, and exhibited a sensitivity of 1795 μA mM−1 cm−2, a linear range up to the desired limit of 1.00 mM for sweat glucose sensing with excellent linearity (R2 = 0.9844), and a lower limit of detection of 135.39 μM. In addition, it has been shown that the peak electro-oxidation current of glucose sensing is linearly related with the squire root of the annealing temperature, √T. This can help to figure out the required applied annealing temperature for getting desired peak electro-oxidation current of glucose in a human health monitoring system.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.186
Teacher spread0.178 · 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
Published2023
Admission routes1
Has abstractyes

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