Pinecone‐Inspired Nanoarchitectured Smart Microcages Enable Nano/Microparticle Drug Delivery
Bibliographic record
Abstract
Drug delivery plays a vital role in medicine and health, but the on-demand delivery of large-sized drugs using stimuli-triggered carriers is extremely challenging. Most present capsules consist of polymeric dense shells with nanosized pores (<10 nm), thus typically lack permeability for nano/microparticle drugs. Here, a pinecone-inspired smart microcage with open network shells, assembled from cellulose nanofibrils (CNFs), is reported for nano/microparticle drug delivery. The approach allows the nanoarchitectured, functionalized CNFs to assemble into mechanically robust, haystack-like network shells with tunable large-through pores and polypeptide-anchored points on a large scale. Such open network shells can intelligently open/close triggered by lesion stimuli, making the therapy "always on-demand." The resulting pinecone-inspired microcages exhibit integrated properties of superior structural stability, superhydrophilicity, and pH-triggered, smart across-shell transport of emerging antimicrobial silver nanoparticles and bioactive silicate nanoplatelets (sizes of >100 nm), which enable both extraordinary anti-infection and bone regeneration. This work provides new insights into the design and development of multifunctional encapsulation and delivery carriers for medical and environmental applications.
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 | 0.000 | 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".