Casting inorganic structures with DNA molds
Notice bibliographique
Résumé
Introduction The ability to manufacture inorganic nanoparticles (NPs) with arbitrarily prescribed three-dimensional (3D) shapes and positional surface modifications is essential to enabling diverse applications (e.g., in nano-optics and biosensing). However, it is challenging to achieve 3D arbitrary user-specified shapes with sub–5-nm resolution. Top-down lithography has limited resolution, particularly for 3D shapes; capping ligands can be used to tune the energy difference of selected crystallographic facets, but typically only for highly symmetric shapes with identical surface facets. Rationale We developed a framework to program arbitrary 3D inorganic NPs using DNA, which serves both as an informational “genome” to encode the 3D shape of a NP and as a physical “fabricator” to retrieve the information and execute the instruction to manufacture the NP. Specifically, our method uses a computationally designed, mechanically stiff synthetic DNA nanostructure with a user-specified cavity as a “mold” to cast the target inorganic NP. The mold encloses a small gold (Au) “seed.” Under mild conditions, the Au seed grows into a larger metal NP that fills the entire cavity, thereby replicating its prescribed 3D shape. The remaining DNA mold additionally acts as a spatially programmable functionalization surface. Results Using this DNA nanocasting method, we constructed three distinct sub–25-nm 3D cuboid silver (Ag) NPs with three independently tunable dimensions. The shape versatility of DNA-based nanocasting was further demonstrated via the synthesis of Ag NPs with equilateral triangular, right triangular, and circular cross sections. The material versatility was demonstrated via synthesis of a Au cuboid in addition to the Ag NPs. The DNA mold served as an addressable coating for the casted NP and thus enabled the construction of higher-order composite structures, including a Y-shaped Ag NP composite and a quantum dot (QD)–Ag-QD sandwiched structure through one-step casting growth. We investigated the key design parameters for stiff DNA molds through mechanical simulations. Multilayered DNA molds provided higher mechanical stiffness for confining NP growth within the mold than single-layer DNA molds, as confirmed by experimental observation. We additionally characterized plasmonic properties of the designer equilateral Ag triangle and Ag sphere through electron energy loss spectroscopy. Tuning of particle symmetry produced a shape-specific spectrum, which is consistent with the predictions of electromagnetism-based simulations. Conclusion DNA nanocasting represents a new framework for the programmable digital fabrication of 3D inorganic nanostructures with prescribed shapes, dimensions, and surface modifications at sub– 5-nm resolution. The key design strategy is to encode linear sequences of DNA with the sophisticated user-specified 3D spatial and surface information of an inorganic NP, as well as to retrieve and execute the information to physically produce this structure via geometric confinement. Such a method may lead to computationally designed functional materials for the digital manufacture of optical nanocircuits, electronic nanocomputers, and perhaps even sophisticated inorganic nanorobots, each with their blueprints (or “genomes’’) encoded in the DNA molecules that constitute their “nanofabricators.”
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».