Machine Learning-Guided Systematic Search of DNA Sequences for Sorting Carbon Nanotubes

Controlled Quantum Well Formation on DNA-Wrapped Carbon Nanotubes via Peroxide-Mediated Aryl Diazonium Reduction

Quantum well defect-modified single-walled carbon nanotubes are nanomaterials with wide-ranging applications in biosensing, imaging, quantum computing, and catalysis. The most common method for covalent functionalization of nanotubes for biosensing applications involves reactions with aryl diazonium salts to generate sp3 aryl defect sites, commonly followed by wrapping with single-stranded DNA for aqueous dispersion. We describe herein a rapid aryl diazonium functionalization reaction directly compatible with DNA-wrapped nanotubes, mediated by hydrogen peroxide. The reaction uses mild aqueous conditions at physiological pH and can be easily monitored in real-time via fluorescence analysis to control the degree of functionalization. Overall, this reaction greatly simplifies the production of covalently functionalized DNA-wrapped carbon nanotubes, expanding their potential for industrial and biomedical applications.

Single-wall carbon nanotube separations via aqueous two-phase extraction: new prospects enabled by high-throughput methods

Aqueous two-phase extraction (ATPE) is an effective and scalable liquid-phase processing method for purifying single species of single-wall carbon nanotubes (SWCNTs) from multiple species mixtures. Recent metrological developments have led to advances in the speed of identifying solution parameters leading to more efficient ATPE separations with greater fidelities. In this feature article, we review these developments and discuss their vast potential to further advance SWCNT separations science towards the optimization of production scale processes and the full realization of SWCNT-enabled technologies.

Accurate DNA Sequence Prediction for Sorting Target-Chirality Carbon Nanotubes and Manipulating Their Functionalities

Synthetic single-wall carbon nanotubes (SWCNTs) contain various chiralities, which can be sorted by DNA. However, finding DNA sequences for this purpose mainly relies on trial-and-error methods. Predicting the right DNA sequences to sort SWCNTs remains a substantial challenge. Moreover, it is even more daunting to predict sequences for sorting SWCNTs with target chirality. Here, we present a deep-learning (DL) enhanced strategy for the accurate prediction of DNA sequences capable of sorting target-chirality nanotubes. We first experimentally screened 216 DNA sequences using aqueous two-phase (ATP) separation, resulting in 116 resolving sequences that can purify 17 distinct single-chirality SWCNTs. These experimental results created a comprehensive training data set. We utilized the recently released 3D molecular representation learning framework, Uni-Mol, to construct a DL workflow that maps atomistic-level structural information on DNA sequences into the feature space. This information captures the structural features of DNA molecules that are crucial for their interactions with SWCNTs. This may account for the superior performance of our DL models. The models successfully predicted resolving sequences for (6,5), (6,6), and (7,4) SWCNTs with accuracy rates of 87.5, 90, and 70%, respectively. Importantly, the discovery of numerous resolving sequences for (6,5) SWCNTs allows us to systematically manipulate the sequence-dependent absorption spectral shift, photoluminescence intensity, and surfactant sensitivity of DNA-(6,5) hybrids and elucidate the underlying mechanisms.

Molecular Determinants of Optical Modulation in ssDNA-Carbon Nanotube Biosensors

Most traditional optical biosensors operate through molecular recognition, where ligand binding causes conformational changes that lead to optical perturbations in the emitting motif. Optical sensors developed from single-stranded DNA-functionalized single-walled carbon nanotubes (ssDNA-SWCNTs) have started to make useful contributions to biological research. However, the mechanisms underlying their function have remained poorly understood. In this study, we combine experimental and computational approaches to show that ligand binding alone is not sufficient for optical modulation in this class of synthetic biosensors. Instead, the optical response that occurs after ligand binding is highly dependent on the chemical properties of the ligands, resembling mechanisms seen in activity-based biosensors. Specifically, we show that in ssDNA-SWCNT catecholamine sensors, the optical response correlates positively with the electron density on the aryl motif, even among ligands with similar ligand binding affinities. Importantly, despite the strong correlations with electrochemical properties, we find that catechol oxidation itself is not necessary to drive the sensor optical response. We discuss how these findings could serve as a framework for tuning the performance of existing sensors and guiding the development of new biosensors of this class.

Integrating Single-Walled Carbon Nanotubes into Supramolecular Assemblies: From Basic Interactions to Emerging Applications

Integrating single-walled carbon nanotubes (SWCNTs) into supramolecular self-assemblies harnesses the distinctive mechanical, optical, and electronic properties of the nanoparticles alongside the structural and chemical properties of the assemblies. Organic molecules capable of forming supramolecular assemblies through hydrophobic, van der Waals, and π-π interactions have been demonstrated to be particularly effective in dispersing and functionalizing SWCNTs, as these same interactions facilitate the binding to the hydrophobic graphene-like surface of the SWCNTs. This review discusses a variety of self-assembling structures that were shown to integrate SWCNTs, ranging from simple micelles and ring structures to complex DNA origami and three-dimensional hydrogels formed by low-molecular-weight gelators. We explore the integration of SWCNTs into various supramolecular assemblies and highlight emerging applications of these composite materials, such as the mechanical enforcement of self-assembling hydrogels and leveraging the near-infrared (NIR) fluorescence properties of SWCNTs for monitoring the molecular self-assembly process. Notably, the distinctive NIR fluorescence of SWCNTs, which overlaps with the biological transparency window, offers significant opportunities for noninvasive sensing applications within the supramolecular platforms. Future research into a deeper understanding of the interactions between SWCNTs and different supramolecular frameworks will expand the potential applications of SWCNT-integrated supramolecular assemblies in fields like biomedical engineering, electronic devices, and environmental sensing.

Machine Learning as a "Catalyst" for Advancements in Carbon Nanotube Research

The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent multiple complexity nature involved in their production, processing, and analysis. Recent advancements in machine learning (ML) have provided researchers with novel and powerful tools to address these challenges. This review explores the role of ML in the field of CNT research, focusing on how ML has enhanced CNT research by (1) revolutionizing CNT synthesis through the optimization of complex multivariable systems, enabling autonomous synthesis systems, and reducing reliance on conventional trial-and-error approaches; (2) improving the accuracy and efficiency of CNT characterizations; and (3) accelerating the development of CNT applications across several fields such as electronics, composites, and biomedical fields. This review concludes by offering perspectives on the future potential of integrating ML further into CNT research, highlighting its role in driving the field forward.

Systematic Selection of High-Affinity ssDNA Sequences to Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single-stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high-throughput systematic selection, enriching high-affinity ssDNA sequences from a vast random library. Specific base compositions and patterns are identified that govern the binding affinity between ssDNA and SWCNTs. Molecular dynamics simulations validate the stability of ssDNA conformations on SWCNTs and reveal the pivotal role of hydrogen bonds in this interaction. Additionally, it is demonstrated that machine learning could accurately distinguish high-affinity ssDNA sequences, providing an accessible model on a dedicated webpage (http://service.k-medai.com/ssdna4cnt). These findings open new avenues for high-affinity ssDNA-SWCNT constructs for stable and sensitive molecular detection across diverse scientific disciplines.

Sequence-Dependent Surface Coverage of ssDNA Coatings on Single-Wall Carbon Nanotubes

A combination of experimental measurements and molecular dynamics (MD) simulations was used to investigate how the surfaces of single-wall carbon nanotubes (SWCNTs) are covered by adsorbed ssDNA oligos with different base compositions and lengths. By analyzing the UV absorption spectra of ssDNA-coated SWCNTs before and after coating displacement by a transparent surfactant, the mass ratios of adsorbed ssDNA to SWCNTs were determined for poly-T, poly-C, GT-containing, and AT-containing ssDNA oligos. Based on the measured mass ratios, it is estimated that an average of 20, 22, 26, or 32 carbon atoms are covered by one adsorbed thymine, cytosine, adenine, or guanine nucleotide, respectively. In addition, the UV spectra revealed electronic interactions of varying strengths between the nucleobase aromatic rings and the nanotube π-systems. Short poly-T DNA oligos show stronger π-π stacking interactions with SWCNT surfaces than do short poly-C DNA oligos, whereas both long poly-C and poly-T DNA oligos show strong interactions. These experiments were complemented by MD computations on simulated systems that were constrained to match the measured ssDNA/SWCNT mass ratios. The surface coverages computed from the MD results varied with oligo composition in a pattern that correlates higher measured yields of nanotube fluorescence with greater surface coverage.

Encapsulation and Evolution of Polyynes Inside Single-Walled Carbon Nanotubes

Polyyne is an sp-hybridized linear carbon chain (LCC) with alternating single and triple carbon-carbon bonds. Polyyne is very reactive; thus, its structure can be easily damaged through a cross-linking reaction between the molecules. The longer the polyyne is, the more unstable it becomes. Therefore, it is difficult to directly synthesize long polyynes in a solvent. The encapsulation of polyynes inside carbon nanotubes not only stabilizes the molecules to avoid cross-linking reactions, but also allows a restriction reaction to occur solely at the ends of the polyynes, resulting in long LCCs. Here, by controlling the diameter of single-walled carbon nanotubes (SWCNTs), polyynes were filled with high yield below room temperature. Subsequent annealing of the filled samples promoted the reaction between the polyynes, leading to the formation of long LCCs. More importantly, single chiral (6,5) SWCNTs with high purity were used for the successful encapsulation of polyynes for the first time, and LCCs were synthesized by coalescing the polyynes in the (6,5) SWCNTs. This method holds promise for further exploration of the synthesis of property-tailored LCCs through encapsulation inside different chiral SWCNTs.

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

Expanding the Horizons of Machine Learning in Nanomaterials to Chiral Nanostructures

Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design and discovery, and reducing the need for time-consuming and labor-intensive experiments and simulations. In contrast to their achiral counterparts, the application of machine learning for chiral nanomaterials is still in its infancy, with a limited number of publications to date. This is despite the great potential of machine learning to advance the development of new sustainable chiral materials with high values of optical activity, circularly polarized luminescence, and enantioselectivity, as well as for the analysis of structural chirality by electron microscopy. In this review, an analysis of machine learning methods used for studying achiral nanomaterials is provided, subsequently offering guidance on adapting and extending this work to chiral nanomaterials. An overview of chiral nanomaterials within the framework of synthesis-structure-property-application relationships is presented and insights on how to leverage machine learning for the study of these highly complex relationships are provided. Some key recent publications are reviewed and discussed on the application of machine learning for chiral nanomaterials. Finally, the review captures the key achievements, ongoing challenges, and the prospective outlook for this very important research field.

Mixed-Solvent Engineering as a Way around the Trade-Off between Yield and Purity of (7,3) Single-Walled Carbon Nanotubes Obtained Using Conjugated Polymer Extraction

The inability to purify nanomaterials such as single-walled carbon nanotubes (SWCNTs) to the desired extent hampers the progress in nanoscience. Various SWCNT types can be purified by extraction, but it is challenging to establish conditions giving rise to the isolation of high-purity fractions. The problem stems from the fact that common organic solvents or water cannot provide an optimal environment for purification. Consequently, one must often decide between the separation yield and purity of the product. This article reports how through the self-synthesis of poly(9,9-dioctylfluorene-alt-benzothiadiazole) with tailored characteristics, in-depth elucidation of the extraction process, and mixed-solvent engineering, a high-yield isolation of monochiral (7,3) SWCNTs is developed. The combination of toluene and tetralin affords a separation medium of unique properties, wherein both high yield and exceptional purity can be attained simultaneously. The reported results pave the way for further research on this rare chirality, which, as illustrated herein, is much more reactive than any of the previously separated SWCNTs.

Biotemplated precise assembly approach toward ultra-scaled high-performance electronics

Structural DNA nanotechnology can be programmed into complex designer structures with molecular precision for directing a wide range of inorganic and biological materials. However, the use of DNA-templated approaches for the fabrication and performance requirements of ultra-scaled semiconductor electronics is limited by its assembly disorder and destructive interface composition. In this protocol, using carbon nanotubes (CNTs) as model semiconductors, we provide a stepwise process to build ultra-scaled, high-performance field-effect transistors (FETs) from micron-scale three-dimensional DNA templates. We apply the approach to assemble CNT arrays with uniform pitches scaled between 24.1 and 10.4 nm with yields of more than 95%, which exceeds the resolution limits of conventional lithography. To achieve highly clean CNT interfaces, we detail a rinsing-after-fixing step to remove residual DNA template and salt contaminations present around the contact and the channel regions, without modifying the alignment of the CNT arrays. The DNA-templated CNT FETs display both high on-state current (4-15 μA per CNT) and small subthreshold swing (60-100 mV per decade), which are superior to previous examples of biotemplated electronics and match the performance metrics of high-performance, silicon-based electronics. The scalable assembly of defect-free three-dimensional DNA templates requires 1 week and the CNT arrays can be synthesized within half a day. The interface engineering requires 1-2 d, while the fabrication of high-performance FET and logic gate circuits requires 2-4 d. The structural and performance characterizations of molecular-precise DNA self-assembly and high-performance electronics requires 1-2 d. The protocol is suited for users with expertise in DNA nanotechnology and semiconductor electronics.

Beyond nature's base pairs: machine learning-enabled design of DNA-stabilized silver nanoclusters

Sequence-encoded biomolecules such as DNA and peptides are powerful programmable building blocks for nanomaterials. This paradigm is enabled by decades of prior research into how nucleic acid and amino acid sequences dictate biomolecular interactions. The properties of biomolecular materials can be significantly expanded with non-natural interactions, including metal ion coordination of nucleic acids and amino acids. However, these approaches present design challenges because it is often not well-understood how biomolecular sequence dictates such non-natural interactions. This Feature Article presents a case study in overcoming challenges in biomolecular materials with emerging approaches in data mining and machine learning for chemical design. We review progress in this area for a specific class of DNA-templated metal nanomaterials with complex sequence-to-property relationships: DNA-stabilized silver nanoclusters (AgN-DNAs) with bright, sequence-tuned fluorescence colors and promise for biophotonics applications. A brief overview of machine learning concepts is presented, and high-throughput experimental synthesis and characterization of AgN-DNAs are discussed. Then, recent progress in machine learning-guided design of DNA sequences that select for specific AgN-DNA fluorescence properties is reviewed. We conclude with emerging opportunities in machine learning-guided design and discovery of AgN-DNAs and other sequence-encoded biomolecular nanomaterials.

Controlled Preparation of Single-Walled Carbon Nanotubes as Materials for Electronics

Single-walled carbon nanotubes (SWCNTs) are of particular interest as channel materials for field-effect transistors due to their unique structure and excellent properties. The controlled preparation of SWCNTs that meet the requirement of semiconducting and chiral purity, high density, and good alignment for high-performance electronics has become a key challenge in this field. In this Outlook, we outline the efforts in the preparation of SWCNTs for electronics from three main aspects, structure-controlled growth, selective sorting, and solution assembly, and discuss the remaining challenges and opportunities. We expect that this Outlook can provide some ideas for addressing the existing challenges and inspire the development of SWCNT-based high-performance electronics.