Virus‐Assembled Biofunctional Microarrays with Hierarchical 3D Nano‐Reticular Network
Bibliographic record
Abstract
Abstract Three‐dimensional (3D) hierarchical wrinkled materials built with biological entities have so far remained exclusive to nature. Herein, multiscale functional ultraporous 3D bio‐networks of bioprinted phage‐built wrinkled microarrays are created through establishing a universal heat‐ and solvent‐independent substrate‐shrinkage method induced by high‐pressure carbon dioxide (HPCD). This method results in diverse wrinkled patterns on soft materials and is particularly powerful for solvent‐ and heat‐sensitive biomaterials, for which other methods have failed. The phage nanofilaments (7 nm width) self‐assemble into orderly‐aligned submicron bundles (100 nm width), which crimp into tunable microscale wrinkles (0.7–5.0 µm width) on size‐controllable micro‐arrays (200–600 µm width) exhibiting a four‐level hierarchical nano‐reticular structure. The HPCD method also protects the bioactivity of biorecognition molecules loaded into the microarrays, leading to the design of bacteria‐sensing chips, made with in‐house deoxyribozyme‐loaded 3D phage microarrays. The developed bacteria‐sensing chips achieve a limit of detection that is 100 × more sensitive with greater reproducibility compared to two‐dimensional (2D) microdot arrays and correctly identify Legionella pneumophila in contaminated water samples collected from industrial cooling towers, highlighting phage‐built wrinkled networks as a platform for bottom‐up assembly of biological building blocks into biofunctional material.
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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) | 0.001 | 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".