Engineered Organometallic Polymer and Hybrid Systems Containing Nanoparticles and/or Poly(ferrocenylsilanes)
Bibliographic record
Abstract
ROSKOV, KRISTEN EKIERT.Engineered Organometallic Polymer and Hybrid Systems Containing Nanoparticles and/or Poly(ferrocenylsilanes). (Under the direction of Professor Richard Spontak.) Formation of polymer nanocomposites is becoming an increasingly attractive and facile means by which to combine the desirable properties of metals and metal oxides (e.g., electrical, magnetic, optical, and thermal) with those of polymers (e.g., flexible, lightweight and tough).Incorporation of nanoscale objects such as spheroidal nanoparticles or elongated nanorods into electrospun polymer nano/microfibers measuring from 50 nm to 1 μm in diameter yields functional nanomaterials that can be used in various applications ranging from data storage and conductive nanowires to nonwoven sensors, magnetic filters and drug delivery patches.By aligning nanoscale objects in one-dimensional constructs, we expect that desirable attributes arising from highly anisotropic electronic, optical, thermal, magnetic, and catalytic properties can be realized.The objective of this study is to gain a better DEDICATIONTo my parents, Andrea and Bernie, for their steadfast love and support.You have taught me life's greatest lessons by example and that has shaped the woman I have become.I love you so much.
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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) | 0.001 | 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".