A synthetic mimic of phosphodiesterase type 5 based on corona phase molecular recognition of single-walled carbon nanotubes

Metal-Ion Optical Fingerprinting Sensor Selection via an Analyte Classification and Feature Selection Algorithm

Accurate analyte classification remains a significant challenge in sensor technologies. We present the Analyte Classification and Feature Selection Algorithm (ACFSA), a computational tool designed to identify optimal sensor combinations from unique fingerprint patterns for analyte classification. We applied the ACFSA to a library of peptide-corona-functionalized single-walled carbon nanotubes (SWCNTs), developed as a near-infrared fluorescent, semiselective fingerprinting sensor set for detecting heavy metal ions. Inspired by natural metal-ion complexation sites, each SWCNT sensor in this library features a unique peptide sequence containing various amino acids for metal binding, revealing diverse optical response patterns to the various metal ions tested. The sensor library was further diversified using different SWCNT chiralities and photochemical modifications of the peptide coronae. The ACFSA was applied to the screening data of the fluorescence response of the 30 resulting SWCNT-peptide sensors to five metal-ion analytes. Through iterative dimensionality reduction and rational sensor selection, the algorithm identified the optimal fingerprinting sensors as a minimal two-sensor set with a 0.02% classification error. The final output of the ACFSA is thus an analyte classifier that serves as a unique analyte fingerprint pattern for the selected sensors. The developed peptide-SWCNT system serves as an effective proof-of-concept, illustrating the potential of our platform as a generally applicable tool for fingerprinting analytes and optimal sensor set selection in other sensor-analyte screening experiments.

High-Throughput Approaches to Engineer Fluorescent Nanosensors

Optical sensors are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. To transduce the chemical interaction with the analyte into an optical signal, the interplay between surface chemistry and nanomaterial photophysics has to be optimized. Understanding these aspects promises major opportunities for tailored sensors with optimal performance. However, this requires methods to create and explore the many chemical permutations. Indeed, many current approaches are limited in throughput. This affects the chemical design space that can be studied, the application of machine learning approaches as well as fundamental mechanistic understanding. Here, an overview of selection-limited and synthesis-limited approaches is provided to create and identify molecular nanosensors. Bottlenecks are discussed and opportunities of non-classical recognition strategies are highlighted such as corona phase molecular recognition as well as the requirements for high throughput and scalability. Fluorescent carbon nanotubes are powerful building blocks for sensors and their huge chemical design space makes them an ideal platform for high throughput approaches. Therefore, they are the focus of this article, but the insights are transferable to any nanosensor system. Overall, this perspective aims to provide a fresh perspective to overcome current challenges in the nanosensor field.

Signal Amplification and Near-Infrared Translation of Enzymatic Reactions by Nanosensors

Enzymatic reactions are used to detect analytes in a range of biochemical methods. To measure the presence of an analyte, the enzyme is conjugated to a recognition unit and converts a substrate into a (colored) product that is detectable by visible (VIS) light. Thus, the lowest enzymatic turnover that can be detected sets a limit on sensitivity. Here, we report that substrates and products of horseradish peroxidase (HRP) and β-galactosidase change the near-infrared (NIR) fluorescence of (bio)polymer modified single-walled carbon nanotubes (SWCNTs). They translate a VIS signal into a beneficial NIR signal. Moreover, the affinity of the nanosensors leads to a higher effective local concentration of the reactants. This causes a non-linear sensor-based signal amplification and translation (SENSAT). We find signal enhancement up to ≈120x for the HRP substrate p-phenylenediamine (PPD), which means that reactions below the limit of detection in the VIS can be followed in the NIR (≈1000 nm). The approach is also applicable to other substrates such as 3,3'-5,5'-tetramethylbenzidine (TMB). An adsorption-based theoretical model fits the observed signals and corroborates the sensor-based enhancement mechanism. This approach can be used to amplify signals, translate them into the NIR and increase sensitivity of biochemical assays.

Single-Molecule Methane Sensing Using Palladium-Functionalized nIR Fluorescent Single-Walled Carbon Nanotubes

There has been considerable interest in detecting atmospheric and process-associated methane (CH4) at low concentrations due to its potency as a greenhouse gas. Nanosensor technology, particularly fluorescent single-walled carbon nanotube (SWCNT) arrays, is promising for such applications because of their chemical sensitivities at single-molecule detection limits. However, the methodologies for connecting the stochastic molecular fluctuations from gas impingement on such sensors require further development. In this work, we synthesize Pd-conjugated ss(GT)15-DNA-wrapped SWCNTas near-infrared (nIR) fluorescent, single-molecule sensors of CH4. The complexes are characterized using X-ray photoelectron spectroscopy (XPS) and spectrophotometry, demonstrating spectral changes between the Pd2+ and Pd0 oxidation states. The nIR fluctuations generated upon exposure from 8 to 26 ppb of CH4 were separated into high- and low-frequency components. Aggregating the low-frequency components for an array of sensors showed the most consistent levels of detection with a limit of 0.7 ppb. These results advance the hardware and computational methods necessary to apply this approach to the challenge of environmental methane sensing.

Biosensing with Fluorescent Carbon Nanotubes

Biosensors are powerful tools for modern basic research and biomedical diagnostics. Their development requires substantial input from the chemical sciences. Sensors or probes with an optical readout, such as fluorescence, offer rapid, minimally invasive sensing of analytes with high spatial and temporal resolution. The near-infrared (NIR) region is beneficial because of the reduced background and scattering of biological samples (tissue transparency window) in this range. In this context, single-walled carbon nanotubes (SWCNTs) have emerged as versatile NIR fluorescent building blocks for biosensors. Here, we provide an overview of advances in SWCNT-based NIR fluorescent molecular sensors. We focus on chemical design strategies for diverse analytes and summarize insights into the photophysics and molecular recognition. Furthermore, different application areas are discussed-from chemical imaging of cellular systems and diagnostics to in vivo applications and perspectives for the future.

Hyperspectral Counting of Multiplexed Nanoparticle Emitters in Single Cells and Organelles

Nanomaterials are the subject of a range of biomedical, commercial, and environmental investigations involving measurements in living cells and tissues. Accurate quantification of nanomaterials, at the tissue, cell, and organelle levels, is often difficult, however, in part due to their inhomogeneity. Here, we propose a method that uses the distinct optical properties of a heterogeneous nanomaterial preparation in order to improve quantification at the single-cell and organelle level. We developed "hyperspectral counting", which employs diffraction-limited imaging via hyperspectral microscopy of a diverse set of fluorescent nanomaterials to estimate particle number counts in live cells and subcellular structures. A mathematical model was developed, and Monte Carlo simulations were employed, to improve the accuracy of these estimates, enabling quantification with single-cell and single-endosome resolution. We applied this nanometrology technique with single-walled carbon nanotubes and identified an upper limit of the rate of uptake into cells─approximately 3,000 nanotubes endocytosed within 30 min. In contrast, conventional region-of-interest counting results in a 230% undercount. The method identified significant heterogeneity and a broad non-Gaussian distribution of carbon nanotube uptake within cells. For example, while a particular cell contained an average of 1 nanotube per endosome, the heterogeneous distribution resulted in over 7 nanotubes localizing within some endosomes, substantially changing the accounting of subcellular nanoparticle concentration distributions. This work presents a method to quantify the cellular and subcellular concentrations of a heterogeneous carbon nanotube reference material, with implications for the nanotoxicology, drug/gene delivery, and nanosensor fields.

Carbon Nanomaterials (CNMs) and Enzymes: From Nanozymes to CNM-Enzyme Conjugates and Biodegradation

Carbon nanomaterials (CNMs) and enzymes differ significantly in terms of their physico-chemical properties-their handling and characterization require very different specialized skills. Therefore, their combination is not trivial. Numerous studies exist at the interface between these two components-especially in the area of sensing-but also involving biofuel cells, biocatalysis, and even biomedical applications including innovative therapeutic approaches and theranostics. Finally, enzymes that are capable of biodegrading CNMs have been identified, and they may play an important role in controlling the environmental fate of these structures after their use. CNMs' widespread use has created more and more opportunities for their entry into the environment, and thus it becomes increasingly important to understand how to biodegrade them. In this concise review, we will cover the progress made in the last five years on this exciting topic, focusing on the applications, and concluding with future perspectives on research combining carbon nanomaterials and enzymes.

Approximate Corona Phase Hamiltonian for Individual Cylindrical Nanoparticle-Polymer Interactions

Nanoparticle surfaces, such as cylindrical nanowires and carbon nanotubes, are commonly coated with adsorbed polymer corona phases to impart solution stabilization and to control molecular interactions. These adsorbed polymer molecules (biological or otherwise), also known as the corona phase, are critical to engineering particle and molecular interactions. However, the prediction of its structure and the corresponding properties remains an unresolved problem in polymer physics. In this work, we construct a Hamiltonian describing the adsorption of an otherwise linear polymer to the surface of a cylindrical nanorod in the form of an integral equation summing up the energetic contributions corresponding to polymer bending, confinement, solvation, and electrostatics. We introduce an approximate functional that allows for the solution of the minimum energy configuration in the strongly bound limit. The functional is shown to predict the pitch and surface area of observed helical corona phases in the literature based on the surface binding energy and persistence length alone. This approximate functional also predicts and quantitatively describes the recently observed ionic strength-mediated phase transitions of charged polymer corona at carbon nanotube surfaces. The Hamiltonian and the approximate functional provide the first theoretical link between the polymer's mechanical and chemical properties and the resulting adsorbed phase configuration and therefore should find widespread utility in predicting corona phase structures around anisotropic nanoparticles.

Dendron-Polymer Hybrids as Tailorable Responsive Coronae of Single-Walled Carbon Nanotubes

Functional composite materials that can change their spectral properties in response to external stimuli have a plethora of applications in fields ranging from sensors to biomedical imaging. One of the most promising types of materials used to design spectrally active composites are fluorescent single-walled carbon nanotubes (SWCNTs), noncovalently functionalized by synthetic amphiphilic polymers. These coated SWCNTs can exhibit modulations in their fluorescence spectra in response to interactions with target analytes. Hence, identifying new amphiphiles with interchangeable building blocks that can form individual coronae around the SWCNTs and can be tailored for a specific application is of great interest. This study presents highly modular amphiphilic polymer-dendron hybrids, composed of hydrophobic dendrons and hydrophilic polyethylene glycol (PEG) that can be synthesized with a high degree of structural freedom, for suspending SWCNTs in aqueous solution. Taking advantage of the high molecular precision of these PEG-dendrons, we show that precise differences in the chemical structure of the hydrophobic end groups of the dendrons can be used to control the interactions of the amphiphiles with the SWCNT surface. These interactions can be directly related to differences in the intrinsic near-infrared fluorescence emission of the various chiralities in a SWCNT sample. Utilizing the susceptibility of the PEG-dendrons toward enzymatic degradation, we demonstrate the ability to monitor enzymatic activity through changes in the SWCNT fluorescent signal. These findings pave the way for a rational design of functional SWCNTs, which can be used for optical sensing of enzymatic activity in the near-infrared spectral range.

Synthetic nanosensors for imaging neuromodulators

Neuromodulation plays a critical role in regulating brain function and its dysregulation is implicated in the pathogenesis of numerous neurological and psychiatric disorders. However, only in the last few years have optical tools become available to probe the spatial and temporal profiles of neuromodulator signaling, including dopamine, with the requisite resolution to uncover mechanisms of neuromodulation. In this review, we summarize the current state of synthetic nanomaterial-based optical nanosensors for monitoring neurotransmission with high spatial and temporal resolution. Specifically, we highlight how synthetic nanosensors can elucidate the spatial distribution of neuromodulator release sites and report the temporal dynamics and spatial diffusion of neuromodulator release. Next, we outline advantages and limitations of currently available nanosensors and their recent application to imaging endogenous dopamine release in brain tissue. Finally, we discuss strategies to improve nanosensors for in vivo use, with implications for translational applications.

Recent Advances in Nanomaterials Based Molecularly Imprinted Electrochemical Sensors

Nanotechnology and molecular imprinting both are omnipresent in the modern scientific world. Molecular recognition in the biological systems was mimicked to an extreme extent with its difficulties through molecular imprinting. Solving the problems related to this mimicking was the goal of science and technology. Some challenges like difficulties with the imprinting of protein, poor compatibility with aqueous environments, template leakage, and heterogeneous populations of binding sites in the polymers that contribute to a high level of nonspecific binding sites were addressed with recent advancement in the modern era. These issues were solved later with nano level instrumentations and inventions. Different types of nanomaterials were employed for this research on molecular recognition through MIPs to enhance selectivity, sensitivity and stability to specific systems such as sensors. This review paper attempts to give all the recent advances in molecular imprinting and the potential of nanomaterials in electrochemical sensors.