Hyaluronic Acid-RGD Peptide Conjugated Mesoporous Silica-coated Gold Nanorods for Cancer Dual-targeted Chemo-photothermal Therapy

A multifunctional drug delivery system（GNRs@mSiO_2-HA-RGD） was developed by conjugating targeting ligand hyaluronic acid（HA） and RGD with mesoporous silica-coated gold nanorods（GNRs@mSiO_2） for dual-targeted chemo-photothermal therapy. The physiochemical properties of the prepared nanoparticles were characterized by FTIR, UV-vis spectra, and ~1H NMR. Doxorubicin hydrochloride（DOX）, an anticancer drug, was used as the model drug to investigate the drug loading, in vitro drug release profiles and cytotoxicity. The experimental results show that DOX-GNRs@mSiO_2-HA-RGD is synthesized with a mean diameter of 116 nm and a sufficient load capacity of about 19.8%. It also has p H-enzyme sensitive and NIRtriggered drug release manner. Cellular uptake indicates that DOX-GNRs@mSiO_2-HA-RGD exhibits a higher cellular uptake via CD44 receptor and integrin receptor mediated endocytosis compared with the GNRs@mSiO_2 modified with one receptor or no receptor. In comparison with chemotherapy or photothermal therapy alone, DOX-GNRs@mSiO_2-HA-RGD displayes the synergistic effects and achieves a higher therapeutic efficacy. It can be expected that DOX-GNRs@mSiO_2-HA-RGD is a potential dual-targeted chemo-photothermal therapeutic platform for effective cancer treatment.

Achieving a sub-10 nm nanopore array in silicon by metal-assisted chemical etching and machine learning

Solid-state nanopores with controllable pore size and morphology have huge application potential.However,it has been very challenging to process sub-10 nm silicon nanopore arrays with high efficiency and high quality at low cost.In this study,a method combining metal-assisted chemical etching and machine learning is proposed to fabricate sub-10 nm nanopore arrays on silicon wafers with various dopant types and concentrations.Through a SVM algorithm,the relationship between the nanopore structures and the fabrication conditions,including the etching solution,etching time,dopant type,and concentration,was modeled and experimentally verified.Based on this,a processing parameter window for generating regular nanopore arrays on silicon wafers with variable doping types and concentrations was obtained.The proposed machine-learning-assisted etching method will provide a feasible and economical way to process high-quality silicon nanopores,nanostructures,and devices.