Protein-Based Three-Dimensional Memories
Bibliographic record
Abstract
Significant progress has been made in the remaining tasks, specifically materials development, protein optimization, computer interface development, and prototype development. The primary goal of the materials effort was to develop a polymer matrix for encapsulation of the protein, characterized by optical clarity, long-term stability, protein compatibility, and resistance to gel dehydration and contraction. Handled via subcontract to Wayne Wang of Carleton University in Ottawa, Canada, a poly(acrylamide) based hydrogel has been developed that fits the majority of these characteristics. Light scattering was achieved primarily by the addition of refractive index-matching agents, with sucrose achieving the best reduction. A newly developed high-density acrylamide matrix demonstrates the largest reduction of light scattering, by roughly one order of magnitude over previous gels. Optimization of the protein response was approached primarily through site-directed mutagenesis (SDM), with the goal of increasing the efficiency with which the branched photocycle can be assessed; two avenues were explored to this end, including enhancement of the 0-state yield and the quantum yield of the 0-> P transition. The former avenue increases the amount of P-state formed through simple mass transfer-a higher yield of 0 will result in more P formation, despite the 0-> P quantum efficiency. The latter approach seeks to directly increase the 0-> P quantum efficiency.
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 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".