Carbon nanotube neurotransistors with ambipolar memory and learning functions

In recent years, neuromorphic computing has gained attention as a promising approach to enhance computing efficiency. Among existing approaches, neurotransistors have emerged as a particularly promising option as they accurately represent neuron structure, integrating the plasticity of synapses along with that of the neuronal membrane. An ambipolar character could offer designers more flexibility in customizing the charge flow to construct circuits of higher complexity. We propose a novel design for an ambipolar neuromorphic transistor, utilizing carbon nanotubes as the semiconducting channel and an ion-doped sol-gel as the polarizable gate dielectric. Due to its tunability and high dielectric constant, the sol-gel effectively modulates the conductivity of nanotubes, leading to efficient and controllable short-term potentiation and depression. Experimental results indicate that the proposed design achieves reliable and tunable synaptic responses with low power consumption. Our findings suggest that the method can potentially provide an efficient solution for realizing more adaptable cognitive computing systems.Comment: 16 pages, 6 pages of supporting information at the end, 6 main figures, 10 supporting figure

Machine Learning-Enabled Smart Gas Sensing Platform for Identification of Industrial Gases

Both ammonia and phosphine are widely used in industrial processes, and yet they are noxious and exhibit detrimental effects on human health. Despite the remarkable progress on sensors development, there are still some limitations, for instance, the requirement of high operating temperatures, and that most sensors are solely dedicated to individual gas monitoring. Herein, an ultrasensitive, highly discriminative platform is demonstrated for the detection and identification of ammonia and phosphine at room temperature using a graphene nanosensor. Graphene is exfoliated and successfully functionalized by copper phthalocyanine derivate. In combination with highly efficient machine learning techniques, the developed graphene nanosensor demonstrates an excellent gas identification performance even at ultralow concentrations: 100 ppb NH3 (accuracy—100.0%, sensitivity—100.0%, specificity—100.0%) and 100 ppb PH3 (accuracy—77.8%, sensitivity—75.0%, and specificity—78.6%). Molecular dynamics simulation results reveal that the copper phthalocyanine derivate molecules attached to the graphene surface facilitate the adsorption of ammonia molecules owing to hydrogen bonding interactions. The developed smart gas sensing platform paves a path to design a highly selective, highly sensitive, miniaturized, low-power consumption, nondedicated, smart gas sensing system toward a wide spectrum of gases

Sybodies as novel bioreceptors toward field-effect transistor-based detection of SARS-CoV-2 antigens

The SARS-CoV-2 pandemic has increased the demand for low-cost, portable and rapid biosensors, driving huge research efforts toward new nanomaterial-based approaches with high sensitivity. Many of them employ antibodies as bioreceptors, which have a costly development process requiring animal facilities. Recently, sybodies emerged as an alternative new class of synthetic binders/receptors with high antigen binding efficiency, improved chemical stability, and lower production costs via animal-free methods. Their smaller size is an important asset to consider in combination with ultrasensitive field-effect transistors (FETs) as transducers, which respond more intensely when the biorecognition occurs in close proximity to their surface. This work demonstrates the immobilization of sybodies against the spike protein of the virus on silicon surfaces, which are often the integral part of the semiconducting channel of FETs. Immobilized sybodies maintain the capability to capture antigens even at low concentrations in the femtomolar range, as observed by fluorescence microscopy. Finally, the first proof-of-concept of sybody-modified FET sensing is provided, using a nanoscopic silicon net as the sensitive area where the sybodies are immobilized. The future development of further sybodies against other biomarkers and their generalization in biosensors could be critical to decrease the cost of biodetection platforms in future pandemics

Engineering crystalline quasi-two-dimensional polyaniline thin film with enhanced electrical and chemiresistive sensing performances

Engineering conducting polymer thin films with morphological homogeneity and long-range molecular ordering is intriguing to achieve high-performance organic electronics. Polyaniline (PANI) has attracted considerable interest due to its appealing electrical conductivity and diverse chemistry. However, the synthesis of large-area PANI thin film and the control of its crystallinity and thickness remain challenging because of the complex intermolecular interactions of aniline oligomers. Here we report a facile route combining air-water interface and surfactant monolayer as templates to synthesize crystalline quasi-two-dimensional (q2D) PANI with lateral size ~50 cm2 and tunable thickness (2.6-30 nm). The achieved q2D PANI exhibits anisotropic charge transport and a lateral conductivity up to 160 S cm-1 doped by hydrogen chloride (HCl). Moreover, the q2D PANI displays superior chemiresistive sensing toward ammonia (30 ppb), and volatile organic compounds (10 ppm). Our work highlights the q2D PANI as promising electroactive materials for thin-film organic electronics