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Record W6979997867

Applications of Aminated cellulose

2024· dissertation· en· W6979997867 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2024
Typedissertation
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersFPInnovationsCentre québécois sur les matériaux fonctionnelsNatural Sciences and Engineering Research Council of CanadaMcGill University
Keywordsnot available
DOInot available

Abstract

fetched live from OpenAlex

In this thesis, a new chemical modification of cellulose on the macro, nano and molecular scale was performed and various applications of the modified celluloses were explored.On the macroscale, we synthesized a new cationic cellulose derivative fiber called diaminocellulose fiber (DAmF).DAmF was prepared by a two-step process; first by oxidizing the polymeric chains of pulp by a periodate reaction, followed by a reductive amination reaction to convert aldehyde groups to primary amine.The resulting fiber had a dark brown color, and its amine content was double that of chitosan (a well-known bio-renewable cationic polymer).Like chitosan, DAmF can also be solubilized at low pH which gives the molecular scale form, diaminocellulose (DAmC) On the nano scale, a new member of the hairy nanocellulose (HNC) family was developed.We refer to this new HNC as aminated nanocrystalline cellulose (ANCC).ANCC consists of a crystalline rod-like body and amorphous cellulose chains ("hairs") at both ends that contain primary amine groups.To synthesize ANCC, dialdehyde modified cellulose (DAMC) was prepared by partial periodate oxidation of cellulose and subsequently the aldehyde groups of DAMC were converted into primary amines by reductive amination reaction, which yielded diamine modified cellulose fiber (DAmMF).DAmMF were subjected to an acidic hot-water treatment to isolate amine-functionalized hairy nanocellulose.Finally, DAmF and DAmC were used in the purification of dyed wastewater and in the removal of lead from water and ANCC was used for its anti-microbial property.iii Résumé (French abstract) Dans cette thèse, une nouvelle modification chimique de la cellulose à l'échelle macro, nano et moléculaire a été réalisée et diverses applications des celluloses modifiées ont été explorées.À l'échelle macro, nous avons synthétisé une nouvelle fibre dérivée de cellulose cationique appelée fibre de diaminocellulose (DAmF).Le DAmF a été préparé selon un processus en deux étapes ; d'abord en oxydant les chaînes polymères de la pâte par une réaction au périodate, suivie d'une réaction d'amination réductrice pour convertir les groupes aldéhyde en amine primaire.La fibre résultante avait une couleur brun foncé et sa teneur en amine était le double de celle du chitosane (un polymère cationique bio-renouvelable bien connu).Comme le chitosane, le DAmF peut également être solubilisé à faible pH, ce qui donne la forme à l'échelle moléculaire, la diaminocellulose (DAmC).À l'échelle nanométrique, un nouveau membre de la famille des nanocelluloses poilues (HNC) a été développé.Nous appelons ce nouveau HNC cellulose nanocristalline aminée (ANCC).L'ANCC se compose d'un corps cristallin en forme de bâtonnet et de chaînes de cellulose amorphes (« poils ») aux deux extrémités qui contiennent des groupes amine primaire.Pour synthétiser l'ANCC, de la cellulose modifiée par du dialdéhyde (DAMC) a été préparée par oxydation périodate partielle de la cellulose, puis les groupes aldéhyde de la DAMC ont été convertis en amines primaires par réaction d'amination réductrice, ce qui a donné une fibre de cellulose modifiée par une diamine (DAmMF).Les DAmMF ont été soumis à un traitement acide à l'eau chaude pour isoler la nanocellulose poilue fonctionnalisée par une amine.Enfin, le DAmF et le DAmC ont été utilisés dans la purification des eaux usées teintes et dans l'élimination du plomb de l'eau et l'ANCC a été utilisé pour ses propriétés antimicrobiennes.Professor Theo van de Ven, my supervisor, has been tremendously helpful to me over the years.Because of your positive attitude and can-do attitude toward my work, I was able to reach my full potential and gain scientific self-assurance.Your direction kept me on course, and your confidence in me pushed me to assured greatness.I also want to thank my committee members, Professors Ashok Kakkar and Matthew J. Harrington who provided me with invaluable research advice and insight that greatly aided my approach to problem solving.Your passion for discovery and innovation is contagious.Over the years, many brilliant researchers worked in the van de Ven lab, and their contributions informed my own.Chouchou Nguyen, who helped me get started and contributed many great ideas, deserves special recognition.Despite your busy schedule, you put in a lot of time and attention into my project.The van de Ven lab would not be the same without the contributions of Md Shahidul Islam, Md Nur Alam, Mohammadhadi Moradian, Kayrel Edwards, Sierra Crammer-Smith, Yiwei Jiang, and Mandana Tavakolian.Your help has been much appreciated.I am also incredibly appreciative of Roya Koshani and Martin Sichinga

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: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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
Published2024
Admission routes1
Has abstractyes

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