Core–Shell Heterojunction of Silicon Nanowire Arrays and Carbon Quantum Dots for Photovoltaic Devices and Self-Driven Photodetectors

Silicon nanostructure-based solar cells have lately intrigued intensive interest because of their promising potential in next-generation solar energy conversion devices. Herein, we report a silicon nanowire (SiNW) array/carbon quantum dot (CQD) core–shell heterojunction photovoltaic device by directly coating Ag-assisted chemical-etched SiNW arrays with CQDs. The heterojunction with a barrier height of 0.75 eV exhibited excellent rectifying behavior with a rectification ratio of 10³ at ±0.8 V in the dark and power conversion efficiency (PCE) as high as 9.10% under AM 1.5G irradiation. It is believed that such a high PCE comes from the improved optical absorption as well as the optimized carrier transfer and collection capability. Furthermore, the heterojunction could function as a high-performance self-driven visible light photodetector operating in a wide switching wavelength with good stability, high sensitivity, and fast response speed. It is expected that the present SiNW array/CQD core–shell heterojunction device could find potential applications in future high-performance optoelectronic devices

Monolayer Graphene/Germanium Schottky Junction As High-Performance Self-Driven Infrared Light Photodetector

We report on the simple fabrication of monolayer graphene (MLG)/germanium (Ge) heterojunction for infrared (IR) light sensing. It is found that the as-fabricated Schottky junction detector exhibits obvious photovoltaic characteristics, and is sensitive to IR light with high <i>I</i>_light/<i>I</i>_dark ratio of 2 × 10⁴ at zero bias voltage. The responsivity and detectivity are as high as 51.8 mA W^–1 and 1.38 × 10¹⁰ cm Hz^1/2 W^–1, respectively. Further photoresponse study reveals that the photovoltaic IR detector displays excellent spectral selectivity with peak sensitivity at 1400 nm, and a fast light response speed of microsecond rise/fall time with good reproducibility and long-term stability. The generality of the above results suggests that the present MLG/Ge IR photodetector would have great potential for future optoelectronic device applications

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability

3‑((<i>R</i>)‑4-(((<i>R</i>)‑6-(2-Bromo-4-fluorophenyl)-5-(ethoxycarbonyl)-2-(thiazol-2-yl)-3,6-dihydropyrimidin-4-yl)methyl)morpholin-2-yl)propanoic Acid (HEC72702), a Novel Hepatitis B Virus Capsid Inhibitor Based on Clinical Candidate GLS4

The inhibition of hepatitis B virus (HBV) capsid assembly is a novel strategy for the development of chronic hepatitis B (CHB) therapeutics. On the basis of the preclinical properties and clinical results of GLS4, we carried out further investigation to seek a better candidate compound with appropriate anti-HBV potency, reduced hERG activity, decreased CYP enzyme induction, and improved pharmacokinetic (PK) properties. To this end, we have successfully found that morpholine carboxyl analogues with comparable anti-HBV activities to that of GLS4 showed decreased hERG activities, but they displayed strong CYP3A4 induction in a concentration-dependent manner, except for morpholine propionic acid analogues. After several rounds of modification, compound <b>58</b> (HEC72702), which had an (<i>R</i>)-morpholine-2-propionic acid at the C6 position of its dihydropyrimidine core ring, was found to display no induction of the CYP1A2, CYP3A4, or CYP2B6 enzyme at the high concentration of 10 μM. In particular, it demonstrated a good systemic exposure and high oral bioavailability and achieved a viral-load reduction greater than 2 log in a hydrodynamic-injected (HDI) HBV mouse model and has now been selected for further development

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of “nonbioavailable substructures”, referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability