A revisit of the interaction of gaseous ozone with aqueous iodide. \nEstimating the contributions of the surface and bulk reactions
Bibliographic record
Abstract
Hydrogels are everyday materials characterized by their remarkable properties, of bridging the gap between liquid and solid states. While most hydrogels are traditionally formed from polymers, biomolecules can also undergo gelation, as seen with proteins (e.g., collagen), enabling numerous applications. Peptide-based low-molecular-weight hydrogels (LMWHs), composed of amino acids, have emerged as innovative materials with a broad range of biomedical and biotechnological applications, gaining commercial interest in the 2010s. However, natural peptides composed solely of proteinogenic amino acids present several drawbacks, requiring structural or chemical modifications to enhance their performance. Additionally, multicomponent approaches, which involve combining multiple compounds to form hydrogels, have recently gained prominence as a promising strategy for developing more versatile and efficient systems. In this context, we explore emerging hybrid molecules, i.e., peptides functionalized with DNA bases (i.e., adenine, thymine, guanine, and cytosine), known as nucleopeptides. These compounds have shown encouraging results, yet much remains to be explored to unlock their full potential. In this study, we present a novel series of six (nucleo)-peptides derived from two distinct peptide sequences, Phe-Glu-Phe-Glu and Phe-Lys-Phe-Lys, negatively and positively charged at physiological pH, respectively, making them complementary in terms of electrostatic interactions. These peptides are functionalized with one of the four DNA nucleobases, introduced via a peptide nucleic acid (PNA) moiety. Thus, through a comprehensive multiscale systematic study, we report herein on the impact of charge complementarity and/or nucleobase-pair complementarity on the mechanical and physicochemical properties of the resulting multicomponent hydrogels (including gelation time, sol-gel transition temperature, stiffness, resistance to external stress, fibrillar network morphology, etc.). Then, the results highlight the undeniable potential of this approach, demonstrating that careful selection of components allows the fine-tuning of hydrogel properties. Interestingly, our findings reveal unexpected behaviors, underscoring the complexity of these bioinspired hybrid multicomponent systems while reinforcing their potential for the development of high-performance and innovative supramolecular hydrogels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".