Ligand-assisted synthesis and cytotoxicity of ZnSe quantum dots stabilized by N-(2-carboxyethyl)chitosans

Bratskaya, Svetlana; Сергеева, К. А.; Konovalova, M. V.; Modin, Evgeny; Svirshchevskaya, E. V.; Sergeev, Alexander A.; Mironenko, Aleksandr; Пестов, А. В.

doi:10.1016/j.colsurfb.2019.06.071

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…[19][20][21][22][23][24][25] Among II-VI metal chalcogenide (ME) QDs, however, the synthesis of ZnE QDs in aqueous environments has received limited attention. [26][27][28] We know that Zn and Se are two imperative, indispensable elements within the human body, Table S1 summarizes the key formulations that were developed to synthesize aqueousphase ZnSe QDs; usually, air-sensitive NaHSe was applied with a reaction temperature of 100 °C. A relatively high reaction temperature means that the induction period, which takes place prior to the nucleation and growth of QDs, is relatively short.…”

Section: Introductionmentioning

confidence: 99%

“…An advantage of an aqueous‐phase approach to QDs is that it is more eco‐friendly than the organic‐phase approach due to the use of fewer organic chemicals, and aqueous‐phase QDs have potential in a broad spectrum of applications in biomedicine, also extending to energy production [19–25] . Among II–VI metal chalcogenide (ME) QDs, however, the synthesis of ZnE QDs in aqueous environments has received limited attention [26–28] . We know that Zn and Se are two imperative, indispensable elements within the human body, Table S1 summarizes the key formulations that were developed to synthesize aqueous‐phase ZnSe QDs; usually, air‐sensitive NaHSe was applied with a reaction temperature of 100 °C.…”

Section: Introductionmentioning

confidence: 99%

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Manipulating Reaction Intermediates to Aqueous‐Phase ZnSe Magic‐Size Clusters and Quantum Dots at Room Temperature

Zhang

et al. 2022

Angewandte Chemie

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It is not resolved which model describes better the aqueous-phase nucleation and growth of semiconductor quantum dots (QDs), the classical one-step one or the nonclassical multi-step one. Here, we design a room-temperature reaction to trap reaction intermediates in the prenucleation stage of ZnSe QDs (as a model system). We show that the trapped intermediate can transform to magic-size clusters (MSCs) via intra-molecular reorganization and can fragment to enable the growth of QDs. The MSCs exhibit a sharp optical absorption peaking at 299 nm, labelled MSC-299. The intermediate, the precursor compound (PC-299) of MSC-299, is optically transparent at 299 nm and to longer wavelengths. This intermediate forms in various Zn and Se reaction systems. The present study provides unambiguous evidence that the nonclassical and classical pathways are both necessary to explain the nucleation and growth of aqueous-phase QDs, with the former pathway favored more by high reaction concentrations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Manipulating Reaction Intermediates to Aqueous‐Phase ZnSe Magic‐Size Clusters and Quantum Dots at Room Temperature

Zhang

et al. 2022

Angewandte Chemie

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“…As a rule, calculating TOF requires processing data on hundreds of particles from several points of the catalyst, the more the better. A similar task arises for researchers in the synthesis and characterization of quantum dots [ 3 , 4 , 5 , 6 ]. Software products, widely used in probe methods, including scanning tunneling microscopy, such as WSxM [ 7 ], Gwyddion [ 8 ], allow the operator to measure particle parameters manually.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.

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“…Polymers are widely used in fabrication of versatile hybrid materials to control growth, prevent aggregation, and drive assembly of metal [ 1 , 2 , 3 ], metal oxides [ 4 , 5 ], and other types [ 6 , 7 , 8 , 9 ] of nanoparticles. Interactions between nanoparticles and polymers, especially those with chelating and redox active moieties, can significantly affect functional properties of their hybrids.…”

Section: Introductionmentioning

confidence: 99%

Polyethylenimine as a Non-Innocent Ligand for Hexacyanoferrates Immobilization

et al. 2022

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To understand how polyethyleneimine (PEI), as a ligand, affects structure and properties of the transition metals hexacyanoferrates (HCFs) immobilized in cross-linked PEI matrix, we have synthesized Cu(II), Zn(II), and Fe(III) HCFs via successive ion-exchange reactions with metal salts and K4[FeII(CN)6] or K3[FeIII(CN)6]. The structure and properties of the obtained materials in comparison with the crystalline HCF analogs were investigated with FT-IR, Mössbauer, and UV–Vis spectroscopy. Complete reduction of Fe(III) to Fe(II) by PEI in HCF(III) was confirmed. When synthesis was performed at pH favoring binding of precursor metal ions by PEI, cyano-bridged hybrids rather than polymer—HCFs composites were formed. Although the obtained hybrids did not demonstrate sorption activity toward cesium ions, known for crystalline HCFs, they are of interest for the other applications. SQUID measurements revealed a significant difference in magnetic properties of PEI–HCFs hybrids in comparison with crystalline HCFs. Due to the Fe(III) to Fe(II) reduction in HCF ions, Cu(II) and Fe(III) HCFs(III) lost the molecular magnets properties in PEI matrix, but magnetic ordering, including ferromagnet-antiferromagnet interactions, was observed in all hybrids over the broad temperature range.

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Ligand-assisted synthesis and cytotoxicity of ZnSe quantum dots stabilized by N-(2-carboxyethyl)chitosans

Cited by 12 publications

References 27 publications

Manipulating Reaction Intermediates to Aqueous‐Phase ZnSe Magic‐Size Clusters and Quantum Dots at Room Temperature

Manipulating Reaction Intermediates to Aqueous‐Phase ZnSe Magic‐Size Clusters and Quantum Dots at Room Temperature

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

Polyethylenimine as a Non-Innocent Ligand for Hexacyanoferrates Immobilization

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