Reduced graphene oxide/zinc oxide composite as an electrochemical sensor for acetylcholine detection.

Acetylcholine (ACh) plays a pivotal role as a neurotransmitter, influencing nerve cell communication and overall nervous system health. Imbalances in ACh levels are linked to neurodegenerative diseases, such as Alzheimer's and Parkinson's. This study focused on developing electrochemical sensors for ACh detection, utilizing graphene oxide (GO) and a composite of reduced graphene oxide and zinc oxide (rGO/ZnO). The synthesis involved modified Hummers' and hydrothermal methods, unveiling the formation of rGO through deoxygenation and the integration of nano-sized ZnO particles onto rGO, as demonstrated by XPS and TEM. EIS analysis also revealed the enhancement of electron transfer efficiency in rGO/ZnO. Cyclic voltammograms of the electrode, comprising the rGO/ZnO composite in ACh solutions, demonstrated prominent oxidation and reduction reactions. Notably, the composite exhibited promise for ACh detection due to its sensitivity, low detection threshold, reusability, and selectivity against interfering compounds, specifically glutamate and gamma-aminobutyric acid. The unique properties of rGO, such as high specific surface area and electron mobility, coupled with ZnO's stability and catalytic efficiency, contributed to the composite's potential in electrochemical sensor applications. This research, emphasizing the synthesis, fabrication, and characterization of the rGO/ZnO composite, established itself as a reliable platform for detecting the acetylcholine neurotransmitter.

Introducing micropores into carbon nanoparticles synthesized via a solution plasma process by thermal treatment and their charge storage properties in supercapacitors.

Carbon materials synthesized via a solution plasma process (SPP) have recently shown great potential for various applications. However, they mainly possess a meso-macroporous structure with a lack of micropores, which limits their applications for supercapacitors. Herein, carbon nanoparticles (CNPs) were synthesized from benzene via SPP and then subjected to thermal treatment at different temperatures (400, 600, 800, and 1000 °C) in an argon environment. The CNPs exhibited an amorphous phase and were more graphitized at high treatment temperatures. A small content of tungsten carbide particles was also observed, which were encapsulated in CNPs. An increase in treatment temperature led to an increase in the specific surface area of CNPs from 184 to 260 m2 g-1 through the development of micropores, while their meso-macropore structure remained unchanged. The oxygen content of CNPs decreased from 14.72 to 1.20 atom% as the treatment temperature increased due to the degradation of oxygen functionality. The charge storage properties of CNPs were evaluated for supercapacitor applications by electrochemical measurements using a three-electrode system in 1 M H2SO4 electrolyte. The CNPs treated at low temperatures exhibited an electric double layer and pseudocapacitive behavior due to the presence of quinone groups on the carbon surface. With increasing treatment temperature, the electric double layer behavior became more dominant, while pseudocapacitive behavior was suppressed due to the quinone degradation. Regarding cycling stability, the CNPs treated at high temperatures (with a lack of oxygen functionality) were more stable than those treated at low temperatures. This work highlights a way of introducing micropores into CNPs derived from SPP via thermal treatment, which could be helpful for controlling and adjusting their pore structure for supercapacitor applications.

Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.

Large-scale surveys of single-cell gene expression have the potential to reveal rare cell populations and lineage relationships but require efficient methods for cell capture and mRNA sequencing. Although cellular barcoding strategies allow parallel sequencing of single cells at ultra-low depths, the limitations of shallow sequencing have not been investigated directly. By capturing 301 single cells from 11 populations using microfluidics and analyzing single-cell transcriptomes across downsampled sequencing depths, we demonstrate that shallow single-cell mRNA sequencing (~50,000 reads per cell) is sufficient for unbiased cell-type classification and biomarker identification. In the developing cortex, we identify diverse cell types, including multiple progenitor and neuronal subtypes, and we identify EGR1 and FOS as previously unreported candidate targets of Notch signaling in human but not mouse radial glia. Our strategy establishes an efficient method for unbiased analysis and comparison of cell populations from heterogeneous tissue by microfluidic single-cell capture and low-coverage sequencing of many cells.