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Enregistrement W2187071797

Development of Microfluidic Chips for High Performance Electrophoresis Separations in Biochemical Applications

2013· dissertation· en· W2187071797 sur OpenAlexfundno aff
Seyed Mostafa Shameli

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2013
Typedissertation
Langueen
DomaineEngineering
ThématiqueMicrofluidic and Capillary Electrophoresis Applications
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Waterloo
Mots-clésMicrofluidicsElectrophoresisNanotechnologyChromatographyChemistryMaterials scienceComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Electrophoresis separation corresponds to the motion and separation of dispersed particles under the influence of a constant electric field. In molecular biology, electrophoresis separation plays a major role in identifying, quantifying and studying different biological samples such as proteins, peptides, RNA acids, and DNA. In electrophoresis separation, different characteristics of particles, such as charge to mass ratio, size, and pI, can be used to separate and isolate those particles. For very complex samples, two or more characteristics can be combined to form a multi-dimensional electrophoresis separation system, significantly improving separation efficiency. Much effort has been devoted in recent years to performing electrophoresis separations in microfluidic format. Employing microfluidic technology for this purpose provides several benefits, such as improved transport control, reduced sample volumes, simplicity of operation, portability, greater accessibility, and reduced cost. The aim of this study is to develop microfluidic systems for high-performance separation of biochemical samples using electrophoresis methods. \nThe first part of the thesis concerns the development of a fully integrated microfluidic chip for isoelectric focusing separation of proteins with whole-channel imaging detection. All the challenges posed in fabricating and integrating the chip were addressed. The chip was tested by performing protein and pI marker separations, and the separation results obtained from the chip were compared with those obtained from commercial cartridges. Side-by-side comparison of the results validated the developed chip and fabrication techniques. \nThe research also focuses on improving the peak capacity and separation resolution of two counter-flow gradient electrofocusing methods: Temperature Gradient Focusing (TGF) and Micellar Affinity Gradient Focusing (MAGF). In these techniques, a temperature gradient across a microchannel or capillary is used to separate analytes. With an appropriate buffer, the temperature gradient creates a gradient in the electrophoretic velocity (TGF) or affinity (MAGF) of analytes and, if combined with a bulk counter-flow, ionic species concentrate at unique points where their total velocity is zero, and separate from each other. A bilinear temperature gradient is used along the separation channel to improve both peak capacity and separation resolution simultaneously. The temperature profile along the channel consists of a very sharp gradient used to pre-concentrate the sample, followed by a shallow gradient that increases separation resolution. A simple numerical model was applied to predict the improvement in resolution when using a bilinear gradient. A hybrid PDMS/glass chip integrated with planar micro-heaters for generating bilinear temperature gradients was fabricated using conventional sputtering and soft lithography techniques. A specialized design was developed for the heaters to achieve the desired bilinear profiles using both analytical and numerical modeling. To confirm the temperature profile along the channel, a two-color thermometry technique was also developed for measuring the temperature inside the chip. Separation performance was characterized by separating several different dyes, amino acids and peptides. Experiments showed a dramatic improvement in peak capacity and resolution of both techniques over the standard linear temperature gradients. \nNext, an analytical model was developed to investigate the effect of bilinear gradients in counter-flow gradient electrofocusing methods. The model provides a general equation for calculating the resolution for different gradients, diffusion coefficients and bulk flow scan rates. The results indicate that a bilinear gradient provides up to 100% improvement in separation resolution over the linear case. Additionally, for some scanning rates, an optimum bilinear gradient can be found that maximizes separation resolution. Numerical modeling was also developed to validate some of the results. \nThe final part of the thesis describes the development of a two-dimensional separation system for protein separation, combining temperature gradient focusing (TGF) and sodium dodecyl sulfate (SDS) polyacrylamide gel electrophoresis (PAGE) in a PDMS/glass microfluidic chip. An experimental study was performed on separating a mixture of proteins using two characteristics: charge to mass ratio, and size. Experimental results showed a dramatic improvement in peak capacity over each of the one-dimensional separation techniques.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,181
Écart entre enseignants0,176 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission1
Résumé présentoui

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