Bibliographic record
Abstract
The focus of the research presented in this thesis is the design, synthesis, and anion recognition properties of a structurally novel class of poly(amides) that incorporates the diaminocyclobutenedione (squaramide) group into the polymer backbone. \nIn Chapter 1, a brief overview of different anion-responsive synthetic macromolecules is presented. Emphasis is placed on the wide structural diversity of the polymers, the mechanisms of their anion-induced responses, and features such as signal amplification, multivalency, and cooperative behavior that can be exploited productively in the context of anion recognition and sensing. \nChapter 2 describes a new method for the regioselective preparation of squaramides, using Lewis acid-catalyzed condensations of diethyl squarate and different anilines. Zinc trifluoromethanesulfonate promotes efficient condensations of anilines with squarate esters, providing access to symmetrical and unsymmetrical squaramides in high yields from readily available starting materials. Colorimetric anion-sensing behavior and computational studies illustrating the enhanced hydrogen bond donor ability and acidity of squaramides in comparison to ureas are presented. \nIn Chapter 3, the application of the synthetic method described above to the selective preparation of polysquaramides composed of 1,2-isomeric repeat units is described. The optical, thermal and aggregation properties of these materials are also discussed. \nFinally, Chapter 4 describes self-assembly properties as well as applications of these materials in the area of anion recognition and sensing. Incorporating an anion-binding squaramide group into a polymeric architecture results in drastic alterations in the selectivity and magnitude of its anion-induced response, resulting in a sensitive and discriminating turn-on fluorescence sensor for dihydrogenphosphate ions. This unusual behavior is the result of a cooperative, anion-triggered aggregation process that was further probed by dynamic light scattering (DLS), transmission electron microscopy (TEM) and laser confocal microscopy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".