Benjamin Lambert

Semiconducting single-walled carbon nanotubes (SWCNTs) exhibit a photostable excitonic fluorescence in the short wave near-infrared. Recently, the implantation of color centers on carbon nanotube (CCNTs) opened new avenues to manipulate and enhance their excitonic fluorescence to generate single photon sources or innovative nanoprobes. These color centers act as localized potential wells on the nanotube surface, trapping locally otherwise diffusing excitons. Exciton trapping is thought to results in brighter emission from the CCNT as compared to SWCNTs. This effect is still debated, and its origin would need to be elucidated. To this aim, we are developing a set of single-molecule based super-resolution imaging strategies to allow the direct visualization of excitonic localization on CCNTs, the precise quantification of their luminescent properties in direct comparison with unfunctionalized SWCNTs.

The ability to determine the precise structure of nano-objects is essential for a multitude of applications. This is particularly true of single-walled carbon nanotubes (SWCNTs), which are produced as heterogeneous samples. Current techniques used for their characterization require sophisticated instrumentation, such as atomic force microscopy (AFM), or a compromise on accuracy. In this paper, we propose to use super-resolution microscopy (SRM) to accurately determine the morphology (orientation, length and shape) of individual luminescent SWCNTs. We generate super-resolved images using three recently published SRM analytical software packages (DPR, eSRRF and MSSR) and metrologically compare their performance to determine the morphological properties of SWCNTs. For this, ground-truth information on nanotube morphologies were obtained using polarization measurements and AFM to directly correlate the results from SRM at the single particle level. We show a more than 4-fold improvement in resolution over standard photoluminescence imaging, revealing hidden morphologies as efficiently as AFM. We finally demonstrate that DPR, and eventually eSRRF, can effectively assess SWCNT length distribution in a much faster and more accessible way than AFM. We believe that this approach can be generalized to other types of luminescent nanostructures and thus become a standard for rapid and accurate characterization of samples.

Near-infrared (NIR) fluorescence microscopy has attracted significant attention for in vivo deep tissue imaging due to the combination of low light scattering and absorption by the tissues, resulting in good light penetration depth, especially in the NIR-II window (~1000–1350 nm). In this context, NIR photoluminescence (PL) (λ_em ~ 900–1400 nm) of single-walled carbon nanotubes overlapping with the so-called “tissue transparency window” laid the foundation for their use in bioimaging and sensing applications. Interestingly, although ultrashort carbon nanotubes (usCNTs) had long been sought for their advantages of mimicking bio-molecular dimensions, it is only when sp³ defects introduced bright fluorescent usCNTs could be generated [1]. In this work, we demonstrate the efficient preparation of bright fluorescent usCNTs specifically designed for biological applications, and we show how to create bright usCNTs wrapped by a biocompatible encapsulating agent (phospholipid-polyethylene glycol, PL-PEG). More precisely, we adapted a synthesis route where the sp³ functionalization is followed by chemical cutting [2]. We will present their full characterization and demonstrate their performance for deep tissue imaging in neuroscience. More precisely, our results performed on live brain tissue allow us to analyze their length specific behaviors in different compartments of mouse brains in comparison to the long sp³ functionalized CNTs [3]. References [1] N. Danne, M. Kim, A. G. Godin, H. Kwon, Z. Gao, X. Wu, N. F. Hartmann, S. K. Doorn, B. Lounis, Y. Wang, L. Cognet, ACS Nano, 2018, 12, 6059−6065. [2] Y. Li, X. Wu, M. Kim, J. Fortner, H. Qu, Y. Wang,Chem. Mater ., 2019, 31, 4536-4544. [3] Nandi et al, in preparation.

Semiconducting single-walled carbon nanotubes (SWCNTs) exhibit a photostable excitonic fluorescence in the short wave near-infrared (SWIR). Recently, the covalent functionalization of nanotubes opened new avenues to manipulate their excitonic fluorescence [1]. These “organic color centers (OCCs)” act as localized potential wells on the nanotube surface, trapping locally otherwise diffusing excitons. This effectively results in an increase in the SWCNT emission, the trapped excitons avoiding possible quenching sites along the nanotube surface (structural defects, nanotube ends) [2]. This new radiative pathway results in a shifting of the SWCNT emission towards longer wavelengths and allows SWCNT excitation on their first-order excitonic transition (ca. SWIR) [3]. Altogether, these properties make OCC-functionalized SWCNTs particularly attractive for various applications ranging from biological imaging to quantum information. Yet, the functionalization reaction of SWCNTs with OCC is still poorly understood, stochastic and, as a result, poorly controlled. To gain knowledge and control over these reactions, we developed a two-color imaging platform to image simultaneously both the SWCNT and OCC emissions in situ during the functionalization reaction [4]. We will present how this platform allows to monitor the kinetics of the functionalization reaction and further discuss our findings which provide insights on the interactions existing between OCCs and the SWCNT surface. [1] A. H. Brozena et al., Nat. Rev. Chem., vol. 3, no. 6, pp. 375–392, 2019 [2] N. Danné et al., ACS Nano, vol. 12, no. 6, pp. 6059–6065, 2018 [3] A. K. Mandal et al., Sci. Rep., vol. 10, no. 1, pp. 1–9, 2020 [4] B.P. Lambert et al., in preparation

The ability to determine the shape, size and orientation of fluorescent single-walled carbon nanotubes is critical for a plethora of applications. Yet the heterogeneity of the nanotube samples is often overlooked due to the lack of investigation approaches which may impact the obtained results. Although techniques such as atomic force microscopy (AFM) and transmission electron microscopy (TEM) could be used to this extent, they require heavy instrumentation and analysis, and do not necessarily allow the temporal and in situ characterization of the samples. On the other hand, super resolution microscopy (SRM) approaches can be used to determine the structure of fluorescent nano-objects with high precision while retaining the possibility to perform in situ studies. In particular, analytical SRM approaches are particularly interesting as they rely only on post-processing and do not require any specific optical component. In this context, we study the performances of three analytical SRM approaches for the determination of SWCNT shape, size and orientation. Using correlative microscopy between optical and AFM approaches this study provides an in-depth comparison of the performances of analytical SRM approaches for the morphological characterization of individual SWCNTs. B.P. Lambert et al., in preparation

DNA-wrapped single-walled carbon nanotubes (SWCNTs) have demonstrated great versatility in their use as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to even the slightest changes in the nanotube environment, enabling sensors that can respond to single-molecule fluctuations within the vicinity of their surfaces. The fluorescence response and surface interactions of these sensors are determined by the DNA wrapping sequence. However, the lack of information on the relationship between the DNA sequence and its effect on the SWCNT fluorescence remains a bottleneck for designing sequences that are specific to analytes of interest. We have recently demonstrated the use of directed volution to control the fluorescence response of SWCNTs through DNA design. Iterative cycles of DNA mutation, screening, and selection allowed us to evolve sequences that yield DNA-wrapped SWCNT sensors with a desired fluorescence response to mycotoxins. In this work, we apply the screening results of the DNA libraries used in this approach to train machine learning (ML) algorithms. Artificial neural network (ANN) and support vector machine (SVM) methods were used to predict the response of ssDNA-SWCNT sensors to a specific mycotoxin. The reliability of these models was further assessed through cross-validation. The ANN and SVM models with cross-validation were able to accurately classify the various DNA sequences as yielding either a high or low fluorescence response with an accuracy of 73 and 81%, respectively. The models were further used to predict the performance of alternative DNA sequences outside the initial training dataset, using the Hierarchy and k-means⁺⁺ clustering methods to evaluate the similarity and dissimilarity of each DNA sequence. Compared to the SVM model, the ANN model showed an improved ability to predict high responses for dissimilar DNA sequences. We further applied a combinatorial approach based on SVM and ANN models to design new DNA sequences for improving sensor performance. The success of this approach was validated experimentally, demonstrating the rational design of improved sensors with 95% prediction accuracy. The application of ML algorithms to directed evolution libraries of DNA thus allows one to accurately map the performances of these sensors within a particular sequence space. The computational success of this mapping provides a framework for replacing current empirical approaches with the rational design of DNA sequences for SWCNT sensing. Keywords: Nano Biosensor, DNA-SWCNT, Mycotoxin, Machine learning, Directed evolution method.

DNA-wrapped single-walled carbon nanotubes (SWCNTs) have demonstrated great versatility in their use as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to even the slightest changes in the nanotube environment, enabling sensors that can respond to single-molecule fluctuations within the vicinity of their surfaces. The fluorescence response and surface interactions of these sensors are determined by the DNA wrapping sequence. However, the lack of information on the relationship between the DNA sequence and its effect on the SWCNT fluorescence remains a bottleneck for designing sequences that are specific to analytes of interest. We have recently demonstrated the use of directed evolution to control the fluorescence response of SWCNTs through DNA design. Iterative cycles of DNA mutation, screening, and selection allowed us to evolve sequences that yield DNA-wrapped SWCNT sensors with a desired fluorescence response to mycotoxins. In this work, we apply the screening results of the DNA libraries used in this approach to train machine learning (ML) algorithms. Artificial neural network (ANN) and support vector machine (SVM) methods were used to predict the response of ssDNA-SWCNT sensors to a specific mycotoxin. The reliability of these models was further assessed through cross-validation. The ANN and SVM models with cross-validation were able to accurately classify the various DNA sequences as yielding either a high or low fluorescence response with an accuracy of 73 and 81%, respectively. The models were further used to predict the performance of alternative DNA sequences outside the initial training dataset, using the Hierarchy and k-means ⁺⁺ clustering methods to evaluate the similarity and dissimilarity of each DNA sequence. Compared to the SVM model, the ANN model showed an improved ability to predict high responses for dissimilar DNA sequences. We further applied a combinatorial approach based on SVM and ANN models to design new DNA sequences for improving sensor performance. The success of this approach was validated experimentally, demonstrating the rational design of improved sensors with 95% prediction accuracy. The application of ML algorithms to directed evolution libraries of DNA thus allows one to accurately map the performances of these sensors within a particular sequence space. The computational success of this mapping provides a framework for replacing current empirical approaches with the rational design of DNA sequences for SWCNT sensing. Keywords: Nano Biosensor, DNA-SWCNT, Mycotoxin, Machine learning, Directed evolution method

In this study, we develop and apply a directed evolution approach to engineer the optical sensing properties of DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) towards mycotoxins, a class of molecules critical to detect in the food industry. We successfully demonstrate the creation of sensors for the detection of both the aflatoxin B1 (AFB1) and fumonisin B1 (FB1) mycotoxins based on the specific response of the (9,4) and (7,5) SWCNT chirality fluorescence peaks, respectively. The resulting chirality-specific responsivity was used to demonstrate the multimodal detection of both mycotoxins at different wavelengths of light in the presence of complex food medium. Moreover, we show that directed evolution can be used not only to improve the chiral-dependent selectivity of our sensors to the mycotoxins, but also the sensor sensitivity and fluorescence intensity through multiple rounds of evolution. The approach demonstrated in this study is versatile and could be generalized to other SWCNT sensors as well as other nanosensors comprising a biological element.

The distinctive properties of single-walled carbon nanotubes (SWCNTs) have inspired the development of many novel applications in the field of cell nanobiotechnology. However, studies thus far have not explored the effect of SWCNT functionalization on transport across the cell walls of prokaryotes. We explore the uptake of SWCNTs in Gram-negative cyanobacteria and demonstrate a passive length-dependent and selective internalization of SWCNTs decorated with positively charged biomolecules. We show that lysozyme-coated SWCNTs spontaneously penetrate the cell walls of a unicellular strain and a multicellular strain. A custom-built spinning-disc confocal microscope was used to image the distinct near-infrared SWCNT fluorescence within the autofluorescent cells, revealing a highly inhomogeneous distribution of SWCNTs. Real-time near-infrared monitoring of cell growth and division reveal that the SWCNTs are inherited by daughter cells. Moreover, these nanobionic living cells retained photosynthetic activity and showed an improved photo-exoelectrogenicity when incorporated into bioelectrochemical devices.

The internalization of fluorescent single-walled carbon nanotubes (SWCNTs) in photosynthetic microbes can be exploited for applications ranging from energy conversion and imaging to biomolecule delivery. The internalization of these nanoparticles are largely controlled by the permeability of the surrounding microbial cell walls, which vary in surface charge density, composition, thickness, and elasticity across different species of microbes. Since these properties play a critical role in controlling internalization, the extension of SWCNT-based technologies to prokaryotes thus requires a comparative assessment of uptake across different species. Herein, we extend a technology previously developed for internalizing SWCNTs in prokaryotes, specifically unicellular Synechocystis sp. PCC 6803, to Nostoc punctiforme, a filamentous cyanobacterial strain known to differentiate into heterocysts under nitrogen starvation. Heterocysts are specialized compartments within the filament that are capable of nitrogen fixation. These compartments possess cell walls that are distinct from vegetative cells, conferring heterocysts with relatively reduced permeability and increased resistance to mechanical and osmotic stress. Using a combination of near-infrared (NIR) fluorescence, scanning electron microscopy (SEM), and Raman spectroscopy, we investigate the effects of different cell-wall architectures and SWCNT functionalizations on internalization. We specifically examine the effects of these distinct SWCNT-cell interactions on long-term cell integrity, activity, and viability. We further show that local variations in membrane structure can dictate the extent of SWCNT association and uptake in Nostoc cells. This dependency of nanoparticle internalization on cell type can be exploited for the development of novel cell-specific staining and imaging technologies.