Temperature-responsive self-assembly (TRSA) of polymer-stabilized nanoparticles is a promising method that is useful for many applications. Currently, polymers ligands with a lower critical solution temperature are used for TRSA, which requires the use of specific polymer–solvent couples. We report a comprehensive study of TRSA of nanoparticles grafted with polymer ligands with an upper critical solution temperature (UCST). Upon cooling the nanoparticle solution below the transition temperature, the nanoparticles assembled in clusters, while upon heating these clusters dissociated into individual nanoparticles. The TRSA was reversible and reproducible. In the heating and cooling steps, the dimensions of nanoparticle clusters were controlled by the superposition of temperature and incubation time. The transition to TRSA was governed by the solvent quality for the polymer ligands and was tuned by varying solvent composition. The utilization of UCST polymer ligands offers an effective method for the preparation of assemblies of polymer-tethered nanoparticles, broadens the range of polymers used for TRSA, and enables control of the degree and temperature of nanoparticle assembly.
Abstract Many applications of inorganic nanoparticles (NPs), including photocatalysis, photovoltaics, chemical and biochemical sensing, and theranostics, are governed by NP optical properties. Exploration and identification of reaction conditions for the synthesis of NPs with targeted spectroscopic characteristics is a time‐, labor‐, and resource‐intensive task, as it involves the optimization of multiple interdependent reaction conditions. Integration of machine learning (ML) and microfluidics (MF) offers accelerated identification and optimization of reaction conditions for NP synthesis. Here, an autonomous ML‐driven, oscillatory MF platform for the synthesis of NPs is reported. The platform utilized multiple recipes and reaction times for the synthesis of NPs with different dimensions, conducted spectroscopic NP characterization, and employed ML approaches to analyze multiple yet prioritized spectroscopic NP characteristics, and identified reaction conditions for the synthesis of NPs with targeted optical properties. The platform is also used to develop an understanding of the relationship between reaction conditions and NP properties. This study shows the strong potential of ML‐driven oscillatory MF platforms in materials science and paves the way for automated NP development.
Nanoparticles (NPs) decorated with topographically or chemically distinct surface patches are an emerging class of colloidal building blocks of functional hierarchical materials. Surface segregation of polymer ligands into pinned micelles offers a strategy for the generation of patchy NPs with controlled spatial distribution and number of patches. The thermodynamic nature of this approach poses a question about the stability of multiple patches on the NP surface, as the lowest energy state is expected for NPs carrying a single patch. In the present work, for gold NPs end-grafted with thiol-terminated polymer molecules, we show that the patchy surface morphology is preserved under conditions of strong grafting of the thiol groups to the NP surface (i.e., up to a temperature of 40 °C), although the patch shape changes over time. At higher temperatures (e.g., at 80 °C), the number of patches per NP decreases, due to the increased lateral mobility and coalescence of the patches as well as the ultimate loss of the polymer ligands due to desorption at enhanced solvent quality. The experimental results were rationalized theoretically, using a scaling approach. The results of this work offer insight into the surface science of patchy nanocolloids and specify the time and temperature ranges of the applications of patchy NPs.
Abstract The scalable, robust, and catalyst‐free method has a broad substrate scope, thus offering access to a broad range of styrene derivatives, α,β‐unsaturated carbonyl compounds as well as 1,2 and 1,2‐disubstituted and even trisubstituted alkenes, including cyclic ones.
Interactions between tumor cells and the extracellular matrix (ECM) are an important factor contributing to therapy failure in cancer patients. Current in vitro breast cancer spheroid models examining the role of mechanical properties on spheroid response to chemotherapy are limited by the use of two-dimensional cell culture, as well as simultaneous variation in hydrogel matrix stiffness and other properties, e.g., hydrogel composition, pore size, and cell adhesion ligand density. In addition, currently used hydrogel matrices do not replicate the filamentous ECM architecture in a breast tumor microenvironment. Here, we report a collagen-alginate hydrogel with a filamentous architecture and a 20-fold variation in stiffness, achieved independently of other properties, used for the evaluation of estrogen receptor-positive breast cancer spheroid response to doxorubicin. The variation in hydrogel mechanical properties was achieved by altering the degree of cross-linking of alginate molecules. We show that soft hydrogels promote the growth of larger MCF-7 tumor spheroids with a lower fraction of proliferating cells and enhance spheroid resistance to doxorubicin. Notably, the stiffness-dependent chemotherapeutic response of the spheroids was temporally mediated: it became apparent at sufficiently long cell culture times, when the matrix stiffness has influenced the spheroid growth. These findings highlight the significance of decoupling matrix stiffness from other characteristics in studies of chemotherapeutic resistance of tumor spheroids and in development of drug screening platforms.
Many applications of plasmonic nanoparticles require precise control of their optical properties that are governed by nanoparticle dimensions, shape, morphology and composition. Finding reaction conditions for the synthesis of nanoparticles with targeted characteristics is a time-consuming and resource-intensive trial-and-error process, however closed-loop nanoparticle synthesis enables the accelerated exploration of large chemical spaces without human intervention. Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. By targeting spectroscopic nanoparticle properties, the AFION lab identifies reaction conditions for the synthesis of different types of nanoparticles with designated shapes, morphologies, and compositions. Data analysis provides insight into the role of reaction conditions for the synthesis of the targeted nanoparticle type. This work shows that the AFION lab is an effective exploration platform for on-demand synthesis of plasmonic nanoparticles. The automated synthesis of plasmonic nanoparticles with on-demand properties is a challenging task. Here the authors integrate a fluidic reactor, real-time characterization, and machine learning in a self-driven lab for the photochemical synthesis of nanoparticles with targeted properties.
The spatial distribution of polymer ligands on the surface of nanoparticles (NPs) is of great importance because it determines their interactions with each other and with the surrounding environment. Phase separation in mixtures of polymer brushes has been studied for spherical NPs; however, the role of local surface curvature of nonspherical NPs in the surface phase separation of end-grafted polymer ligands remains an open question. Here, we examined phase separation in mixed monolayers of incompatible polystyrene and poly(ethylene glycol) brushes end-capping the surface of gold nanorods in a good solvent. By varying the molar ratio between these polymers, we generated a range of surface patterns, including uniform and nonuniform polystyrene shells, randomly distributed polystyrene surface patches, and, most interestingly, a helicoidal pattern of polystyrene patches wrapping around the nanorods. The helicoidally patterned nanorods exhibited long-term colloidal stability in a good solvent. The helicoidal wrapping of the nanorods was achieved for the mixtures of polymers with different molecular weights and preserved when the quality of the solvent for the polymers was reduced. The helicoidal organization of polymer patches on the surface of nanorods can be used for templating the synthesis or self-assembly of helicoidal multicomponent nanomaterials.
The debromination of vicinal dibromo compounds to generate alkenes usually requires harsh reaction conditions and the addition of catalysts. Just recently the visible-light-induced debromination of vicinal dibromo compounds emerged as a possible alternative to commonly used methods, but the substrate scope of this reaction is limited and a photocatalyst is necessary for the successful conversion of the starting compounds. A catalyst-free visible-light-induced debromination of vicinal dibromo compounds with a base-activated Hantzsch ester as photosensitizer is reported. The method has a wide substrate scope and a broad functional-group compatibility.
Abstract Chiral packing of ligands on the surface of nanoparticles (NPs) is of fundamental and practical importance, as it determines how NPs interact with each other and with the molecular world. Herein, for gold nanorods (NRs) capped with end‐grafted nonchiral polymer ligands, we show a new mechanism of chiral surface patterning. Under poor solvency conditions, a smooth polymer layer segregates into helicoidally organized surface‐pinned micelles (patches). The helicoidal morphology is dictated by the polymer grafting density and the ratio of the polymer ligand length to nanorod radius. Outside this specific parameter space, a range of polymer surface structures was observed, including random, shish‐kebab, and hybrid patches, as well as a smooth polymer layer. We characterize polymer surface morphology by theoretical and experimental state diagrams. The helicoidally organized polymer patches on the NR surface can be used as a template for the helicoidal organization of other NPs, masked synthesis on the NR surface, as well as the exploration of new NP self‐assembly modes.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
