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Dynamic light scattering (DLS) is an essential technique for nanoparticle size analysis and has been employed extensively for decades, but despite its long history and popularity, the choice of weighting and mean of the size distribution often appears to be picked ad hoc to bring the results into agreement with other methods and expectations by any means necessary. Here, we critically discuss the application of DLS for nanoparticle characterization and provide much-needed clarification for ambiguities in the mean-value practice of commercial DLS software and documentary standards. We address the misleading way DLS size distributions are often presented, that is, as a logarithmically scaled histogram of measured relative quantities. Central values obtained incautiously from this representation often lead to significant interpretation errors. Through the measurement of monomodal nanoparticle samples having an extensive range of sizes (5 to 250 nm) and polydispersity, we similarly demonstrate that the default outputs of a frequently used DLS inversion method are ill chosen, as they are regularizer-dependent and significantly deviate from the cumulant z-average size. The resulting discrepancies are typically larger than 15% depending on the polydispersity index of the samples. We explicitly identify and validate the harmonic mean as the central value of the intensity-weighted DLS size distribution that expresses the inversion results consistently with the cumulant results. We also investigate the extent to which the DLS polydispersity descriptors are representative of the distributional quality and find them to be unreliable and misleading, both for monodisperse reference materials and broad-distribution biomedical nanoparticles. These results overall are intended to bring essential improvements and to stimulate reexamination of the metrological capabilities and role of DLS in nanoparticle characterization.

Light-emitting diodes (LEDs) based on perovskite quantum dots have shown external quantum efficiencies (EQEs) of over 23% and narrowband emission, but suffer from limited operating stability 1 . Reduced-dimensional perovskites (RDPs) consisting of quantum wells (QWs) separated by organic intercalating cations show high exciton binding energies and have the potential to increase the stability and the photoluminescence quantum yield 2 , 3 . However, until now, RDP-based LEDs have exhibited lower EQEs and inferior colour purities 4 – 6 . We posit that the presence of variably confined QWs may contribute to non-radiative recombination losses and broadened emission. Here we report bright RDPs with a more monodispersed QW thickness distribution, achieved through the use of a bifunctional molecular additive that simultaneously controls the RDP polydispersity while passivating the perovskite QW surfaces. We synthesize a fluorinated triphenylphosphine oxide additive that hydrogen bonds with the organic cations, controlling their diffusion during RDP film deposition and suppressing the formation of low-thickness QWs. The phosphine oxide moiety passivates the perovskite grain boundaries via coordination bonding with unsaturated sites, which suppresses defect formation. This results in compact, smooth and uniform RDP thin films with narrowband emission and high photoluminescence quantum yield. This enables LEDs with an EQE of 25.6% with an average of 22.1 ±1.2% over 40 devices, and an operating half-life of two hours at an initial luminance of 7,200 candela per metre squared, indicating tenfold-enhanced operating stability relative to the best-known perovskite LEDs with an EQE exceeding 20% 1 , 4 – 6 . The efficiency and operating lifetimes of perovskite light-emitting diodes is improved by using a fluorinated triphenylphosphine oxide additive to control the cation diffusion during film deposition and passivate the surface.

Lignin is the by-product of pulp and paper industries and bio-refining operations. It is available as the leading natural phenolic biopolymer in the market. It has chromophore functional groups and can absorb a broad spectrum of UV light in range of 250–400 nm. Using lignin as a natural ingredient in sunscreen cream, transparent film, paints, varnishes and microorganism protection has been actively investigated. Both in non-modified and modified forms, lignin provides enhancing UV protection of commercial products with less than a 10% blend with other material. In mixtures with other synthetic UV blockers, lignin indicated synergic effects and increased final UV blocking potential in compare with using only synthetic UV blocker or lignin. However, using lignin as a UV blocker is also challenging due to its complex structure, polydispersity in molecular weight, brownish color and some impurities that require more research in order to make it an ideal bio-based UV blocker.

We present here a perspective detailing the current state-of-the-art technologies for the characterisation of nanoparticles (NPs) in liquid suspension. We detail the technologies involved and assess their applications in the determination of NP size and concentration. We also investigate the parameters that can influence the results and put forward a cause and effect analysis of the principle factors influencing the measurement of NP size and concentration by NP tracking analysis and dynamic light scattering, to identify areas where uncertainties in the measurement can arise. Also included are technologies capable of characterising NPs in solution, whose measurements are not based on light scattering. It is hoped that the manuscript, with its detailed description of the methodologies involved, will assist scientists in selecting the appropriate technology for characterising their materials and enabling them to comply with regulatory agencies' demands for accurate and reliable NP size and concentration data.

Successful computer studies of glass-forming materials need to overcome both the natural tendency to structural ordering and the dramatic increase of relaxation times at low temperatures. We present a comprehensive analysis of eleven glass-forming models to demonstrate that both challenges can be efficiently tackled using carefully designed models of size polydisperse supercooled liquids together with an efficient Monte Carlo algorithm where translational particle displacements are complemented by swaps of particle pairs. We study a broad range of size polydispersities, using both discrete and continuous mixtures, and we systematically investigate the role of particle softness, attractivity, and nonadditivity of the interactions. Each system is characterized by its robustness against structural ordering and by the efficiency of the swap Monte Carlo algorithm. We show that the combined optimization of the potential’s softness, polydispersity, and nonadditivity leads to novel computer models with excellent glass-forming ability. For such models, we achieve over 10 orders of magnitude gain in the equilibration time scale using the swap Monte Carlo algorithm, thus paving the way to computational studies of static and thermodynamic properties under experimental conditions. In addition, we provide microscopic insight into the performance of the swap algorithm, which should help optimize models and algorithms even further.

Many properties of nanoparticles are governed by their shape, size, polydispersity and surface chemistry. To apply nanoparticles in chemical sensing, medical diagnostics, catalysis, thermoelectrics, photovoltaics or pharmaceutics, they have to be synthesized with precisely controlled characteristics. This is a time-consuming, laborious and resource-intensive task, because nanoparticle syntheses often include multiple reagents and are conducted under interdependent experimental conditions. Machine learning (ML) offers a promising tool for the accelerated development of efficient protocols for nanoparticle synthesis and, potentially, for the synthesis of new types of nanoparticles. In this Review, we discuss ML algorithms that can be used for nanoparticle synthesis and highlight key approaches for the collection of large datasets. We examine ML-guided synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and conclude with a discussion of current limitations, advantages and perspectives in the development of ML-assisted nanoparticle synthesis. Machine learning can be applied for the controlled synthesis of nanoparticles with precise properties. This Review discusses different machine learning approaches for the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles, and highlights key approaches for the collection of large datasets.

Debris flows are dense and fast-moving complex suspensions of soil and water that threaten lives and infrastructure. Assessing the hazard potential of debris flows requires predicting yield and flow behavior. Reported measurements of rheology for debris flow slurries are highly variable and sometimes contradictory due to heterogeneity in particle composition and volume fraction (ϕ) and also inconsistent measurement methods. Here we examine the composition and flow behavior of source materials that formed the postwildfire debris flows in Montecito, CA, in 2018, for a wide range of ϕ that encapsulates debris flow formation by overland flow. We find that shear viscosity and yield stress are controlled by the distance from jamming, Δϕ = ϕm − ϕ, where the jamming fraction ϕm is a material parameter that depends on grain size polydispersity and friction. By rescaling shear and viscous stresses to account for these effects, the data collapse onto a simple nondimensional flow curve indicative of a Bingham plastic (viscoplastic) fluid. Given the highly nonlinear dependence of rheology on Δϕ, our findings suggest that determining the jamming fraction for natural materials will significantly improve flow models for geophysical suspensions such as hyperconcentrated flows and debris flows.

Over recent decades, poly(lactic-co-glycolic acid) (PLGA) based nano- and micro- drug delivery vehicles have been rapidly developed since PLGA was approved by the Food and Drug Administration (FDA). Common factors that influence PLGA particle properties have been extensively studied by researchers, such as particle size, polydispersity index (PDI), surface morphology, zeta potential, and drug loading efficiency. These properties have all been found to be key factors for determining the drug release kinetics of the drug delivery particles. For drug delivery applications the drug release behavior is a critical property, and PLGA drug delivery systems are still plagued with the issue of burst release when a large portion of the drug is suddenly released from the particle rather than the controlled release the particles are designed for. Other properties of the particles can play a role in the drug release behavior, such as the glass transition temperature (Tg). The Tg, however, is an underreported property of current PLGA based drug delivery systems. This review summarizes the basic knowledge of the glass transition temperature in PLGA particles, the factors that influence the Tg, the effect of Tg on drug release behavior, and presents the recent awareness of the influence of Tg on drug delivery applications.

Melamine foam, categorized as an open-cell foam structure, absorbs sound through its three-dimensional network of thin struts. The pore size polydispersity within the open-cell melamine microstructure is evidenced from a top-down approach and confirmed by scanning electron microscope (SEM)-image analysis. The remarkable ability of melamine foams to mitigate sound energy is attributed to the pore size distribution, which encompasses co-existing pores of distinct characteristic sizes. Consequently, low-frequency and high-frequency fluid flows will follow different paths within the pore structure. A poly-sized model, which provides a connection between microstructure polydispersity and macroscopic properties, is successfully applied to three different melamine foams. This work highlights the significance and implications of polydispersity effects on the acoustic behavior of open-cell foams.

The capability to graft synthetic polymers onto the surfaces of live cells offers the potential to manipulate and control their phenotype and underlying cellular processes. Conventional grafting-to strategies for conjugating preformed polymers to cell surfaces are limited by low polymer grafting efficiency. Here we report an alternative grafting-from strategy for directly engineering the surfaces of live yeast and mammalian cells through cell surface-initiated controlled radical polymerization. By developing cytocompatible PET-RAFT (photoinduced electron transfer-reversible addition-fragmentation chain-transfer polymerization), synthetic polymers with narrow polydispersity ( M w / M n  < 1.3) could be obtained at room temperature in 5 minutes. This polymerization strategy enables chain growth to be initiated directly from chain-transfer agents anchored on the surface of live cells using either covalent attachment or non-covalent insertion, while maintaining high cell viability. Compared with conventional grafting-to approaches, these methods significantly improve the efficiency of grafting polymer chains and enable the active manipulation of cellular phenotypes. A cytocompatible controlled radical polymerization method has now been developed that initiates polymer synthesis directly on the surface of living cells. This method achieves significantly enhanced polymer grafting and enables active manipulation of cellular phenotypes.