Photoswitching metal-organic frameworks: \ntowards light controlled adsorptivity in porous \nmaterials
Bibliographic record
Abstract
Metal Organic Frameworks (MOFs) are porous material composed of metal nodes \nbridged by organic linkers. The resultant structures form porous 3-dimensional frameworks; \nthe chemistry and applications of these materials is incredibly diverse. Through \njudicious choice of the metal coordination chemistry coupled with the imaginationlimited \norganic linker design, MOFs have been tailored for numerous applications \nincluding gas storage, gas separations, and catalysis. While these properties are easily \ntuned, they are considered static (i.e., the properties do not change once the MOF \nis formed). For this reason, research into the design of stimuli-responsive MOFs has \ngained notoriety in the MOF literature. This is owed to changeable adsorptivity in \nresponse to introduced stimuli such as heat, pressure, and light. \nThis thesis discusses strategies to design PSZ-1, a new class of light-responsive \nMOFs that incorporates dithienylethene photoswitches into the pore lining. This new \nmaterial behaves as a light controlled chemical filter and undergoes photoisomerization \nfor a minimum of 5 times without degradation to the materials structure. Further work \nstudied photophysical properties of a small family of structurally analogous DTEs, \nwhich are studied in order to understand the influence of 2-imidazoyl substituents on \nthe thermal stability of these molecules. Finally, we report the synthesis of PSU-68, \nwhich has a controlled degrees of photoswitch incorporation and investigate the effect \nof linker loading on separation properties in the MOF.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".