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Record W2755711468

Design, Fabrication, and Characterization of Multi-Scale Materials for Integration on Lab-on-a-Chip Devices

2016· dissertation· en· W2755711468 on OpenAlexfundno aff
Christine M. Gabardo

Bibliographic record

VenueMacSphere (McMaster University) · 2016
Typedissertation
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
FundersScience and Engineering Research BoardNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsFabricationCharacterization (materials science)ChipNanotechnologyScale (ratio)Lab-on-a-chipMaterials scienceSystems engineeringEngineeringComputer scienceElectrical engineeringMicrofluidicsPhysicsMedicine
DOInot available

Abstract

fetched live from OpenAlex

Currently, molecular diagnosis is conducted by professionals in centralized laboratories, which can be time consuming, expensive, and not readily available in remote locations. This motivates the efforts to develop highly accurate, highly sensitive, and high speed lab-on-a-chip (LOC) platforms for inexpensive point-of-care (POC) diagnostics. Multi-scale materials – with tunable features from the nanometer to millimeter length-scales – have shown to enhance performance of electrodes in LOC biosensing and bioprocessing applications. The controlled fabrication of three-dimensional metallic hierarchical electrodes using standard lithographic, machining, printing, etc. techniques is complex and not suitable for rapid, dynamic, and inexpensive prototyping. The work presented herein addresses the need to develop a benchtop rapid prototyping approach to create tunable multi-scale electrodes for integration in LOC devices. In this work, rapid benchtop fabrication is carried out through a combination of xurography, to create electrodes with specific configurations, and controlled thin film wrinkling using pre-stressed polymer substrates, to structure sputtered electrodes on the nanoscale and microscale. This method creates wrinkled structures with sufficient adhesion (no peeling) and conductance (<1 Ω/󠆾󠆾 sheet resistance) for electrical applications. Further development of this method allows fabrication to be carried out completely on the benchtop using all-solution processing methods to deposit high quality thin films, with similar adhesion (no peeling) and conductance (<1 Ω/󠆾󠆾sq sheet resistance) as sputtered films, on the polymer substrates. Moreover, this fabrication technique is extended to create wrinkled nanoparticle films presenting sub-100 nm wavelengths and a dual degree of tunability, as the nanoparticles tune both the thickness and mechanical properties of the films. Structural tuning of the nanoscale and microscale architectures of wrinkled electrodes is studied with scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), and white light interferometry, and then related to functional tuning via four-point probe measurements and electrochemical measurements. As the structure is shown to determine key functional parameters, such as resistance and surface area, various electrodes structures are designed and applied to important LOC bioprocessing and sensing applications. For example, structurally optimized electrodes are applied to low voltage cell lysis and are able to lyse bacteria at only 4 V. Furthermore, the problem of integrating three-dimensional, multi-scale wrinkled electrodes into microfluidic devices is investigated. New approaches combining surface treatment and partial curing of microchannels are developed to solve these issues and create fully contained, functional electro-fluidic devices that can withstand flow rates greater than 25 mL/min and pressures of greater than 125 kPa. Ultimately, we are able to create and integrate high quality, tunable, functional, multi-scale electrodes on the laboratory benchtop at low cost and high speed. Thus, this research enables the expedited development of LOC electrode devices that are functionally optimized through structural tuning.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.217
Teacher spread0.200 · 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
Published2016
Admission routes1
Has abstractyes

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