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Record W2117501673 · doi:10.1126/science.1258361

Casting inorganic structures with DNA molds

2014· article· en· W2117501673 on OpenAlexfundno aff
Wei Sun, Étienne Boulais, Yera Hakobyan, Wei Li Wang, Amy Guan, Mark Bathe, Peng Yin

Bibliographic record

VenueScience · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersArmy Research OfficeBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaOffice of ScienceDefense Advanced Research Projects AgencyHansjörg Wyss Institute for Biologically Inspired Engineering, Harvard UniversityU.S. Department of EnergyDivision of Civil, Mechanical and Manufacturing InnovationNational Science FoundationNational Institutes of HealthOffice of Naval ResearchHarvard University
KeywordsCuboidNanotechnologyDNA origamiNanostructureMaterials scienceBiosensorTemplateEquilateral triangleDNAGeometryChemistryMathematics

Abstract

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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.”

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.005
GPT teacher head0.248
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations303
Published2014
Admission routes1
Has abstractyes

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