Precision Self‐Assembly of Supramolecules with Heterogeneous Derivatives
Bibliographic record
Abstract
Abstract Supramolecular self‐assembly with well‐defined building blocks like lipids, deoxyribonucleic acid, or ligands relies on accessible molecular structures and predictable interactions. However, assembling heterogeneous, undefined blocks, such as disordered proteins, amorphous solids, and catecholic derivatives, remains challenging due to their unpredictable assembly, leading to irreversible aggregation, severe precipitation, and unreliable performance. Here, the first programmable, sustainable, and durable self‐assembly strategy of supramolecules with heterogenous is presented, derived blocks via harmonizing multiple molecular interactions. This approach achieves reversible assembly/disassembly, ≈73.7% reduced precipitation, and salt‐ and alkaline‐durability under freeze‐thaw cycles in model catecholic derivatives, functioning effectively as robust adhesive primers and hydrogel interfacial strengtheners. Moreover, through molecular force measurements and computational simulations, the first general criterion and benchmark for high precision supramolecular self‐assembly is proposed, applicable to complex derivatives and interactions: with blocks bearing multiple binding sites existing, the co‐assembling blocks should bear at least two binding sites with minimum binding strength (≈17 to ≈37 kJ mol −1 ) to prevent disassembly. This study paves the way and provides benchmarks for precision self‐assembly of diverse supramolecules using heterogeneous derivatives for adhesion technology, nanomaterial synthesis and bio‐inspired applications.
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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 | 0.001 |
| 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 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".