Supramolecular metal-phenolic foams with monodisperse microstructure
Bibliographic record
Abstract
Monodisperse foams are desirable in applications that require structural control, for example, in biomedical scaffolds, catalysis, insulative and acoustic materials. This study presents a novel approach for the fabrication of self-supporting metal-phenolic network (MPN) foams produced with a microfluidic flow-focusing system. For this purpose, tannic acid (TA) and titanium ions (Ti 4+ ) were used to generate stable, monodisperse liquid foams, which were subsequently dried in ambient condition resulting in solid foams. We investigated the effects of gas pressure and Ti 4+ concentration on foam formation. Rheological analysis confirmed an increase in viscosity with Ti 4+ addition, with an upper threshold at n Ti/ n TA = 0.4, beyond which fast gelation hindered microfluidic processing. The bubble size of the liquid foam templates ranged from 200 to 310 μm in diameter, depending on the gas pressure (100–200 mbar) and Ti 4+ concentration. After ambient drying we obtained open-cell solid foams with pore sizes approximately twice the initial bubble diameter, attributed to gas uptake during drying. Uniaxial compression testing showed a negative correlation between pore size and mechanical strength, with ultimate compressive strength values ranging from 5 to 25 kPa. The strongest foams were obtained at n Ti/ n TA = 0.3, balancing crosslinking density and structural integrity. Our proposed green approach provides a platform for morphological control of functional MPN foams, opening their potential for sustainable, cell seeding scaffolds, drug delivery or catalysis, among other.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".