Electrospray deposition tool: Creating compositionally gradient libraries of nanomaterials

Efficient electrospray deposition of surfaces smaller than the spray plume

Electrospray deposition (ESD) is a promising technique for depositing micro-/nano-scale droplets and particles with high quality and repeatability. It is particularly attractive for surface coating of costly and delicate biomaterials and bioactive compounds. While high efficiency of ESD has only been successfully demonstrated for spraying surfaces larger than the spray plume, this work extends its utility to smaller surfaces. It is shown that by architecting the local "charge landscape", ESD coatings of surfaces smaller than plume size can be achieved. Efficiency approaching 100% is demonstrated with multiple model materials, including biocompatible polymers, proteins, and bioactive small molecules, on both flat and microneedle array targets. UV-visible spectroscopy and high-performance liquid chromatography measurements validate the high efficiency and quality of the sprayed material. Here, we show how this process is an efficient and more competitive alternative to other conformal coating mechanisms, such as dip coating or inkjet printing, for micro-engineered applications.

The recent insight in the release of anticancer drug loaded into PLGA microspheres

Cancer is a series of diseases leading to a high rate of death worldwide. Microspheres display specific characteristics that make them appropriate for a variety of biomedical purposes such as cancer therapy. Newly, microspheres have the potentials to be used as controlled drug release carriers. Recently, PLGA-based microspheres have attracted exceptional attention relating to effective drug delivery systems (DDS) because of their distinctive properties for a simple preparation, biodegradability, and high capability of drug loading which might be increased drug delivery. In this line, the mechanisms of controlled drug release and parameters that influence the release features of loaded agents from PLGA-based microspheres should be mentioned. The current review is focused on the new development of the release features of anticancer drugs, which are loaded into PLGA-based microspheres. Consequently, future perspective and challenges of anticancer drug release from PLGA-based microspheres are mentioned concisely.

Controlling morphology in electrosprayed methylcellulose nanowires via nanoparticle addition: coarse-grained modeling and experiments

Electrospray deposition (ESD) has shown great promise for manufacturing micro- and nanostructured coatings at scale on versatile substrates with complex geometries. ESD exhibits a broad spectrum of morphologies depending upon the properties of spray fluids. Among them are nanowire forests or foams obtained via the in-air gelation of electrospray droplets formed from methylcellulose (MC) solutions. In this study, we explored MC ESD loaded with nanoparticles of various shapes and uncovered the effects of particle fillers on morphology evolution using coarse-grained simulations and physical experiments. Utilizing electrostatic dissipative particle dynamics, we modeled the electrohydrodynamic deformation of particle-laden MC droplets undergoing in-flight evaporation. The simulations quantitatively predict the suppression of droplet deformation as the size or concentration of spherical nanoparticles increases. While small particles can be readily encapsulated into the nanowire body, large particles can arrest nanowire formation. The model was extended to nanoparticles with complex topologies, showing MC nanowires emerging from particle edges and vertices due to curvature-enhanced electric stress. In all cases, strong agreements were found between simulation and experimental results. These results demonstrate the efficacy of the coarse-grained model in predicting the morphology evolution of electrosprayed droplets and lay the groundwork for employing MC nanowires for developing nanostructured composites.

Solvent-assisted self-assembly of block copolymer thin films

Solvent-assisted block copolymer self-assembly is a compelling method for processing and advancing practical applications of these materials due to the exceptional level of the control of BCP morphology and significant acceleration of ordering kinetics. Despite substantial experimental and theoretical efforts devoted to understanding of solvent-assisted BCP film ordering, the development of a universal BCP patterning protocol remains elusive; possibly due to a multitude of factors which dictate the self-assembly scenario. The aim of this review is to aggregate both seminal reports and the latest progress in solvent-assisted directed self-assembly and to provide the reader with theoretical background, including the outline of BCP ordering thermodynamics and kinetics phenomena. We also indicate significant BCP research areas and emerging high-tech applications where solvent-assisted processing might play a dominant role.

Thin film block copolymer self-assembly for nanophotonics

The nanophotonic engineering of light-matter interactions has profoundly changed research behind the design and fabrication of optical materials and devices. Metasurfaces-arrays of subwavelength nanostructures that interact resonantly with electromagnetic radiation-have emerged as an integral nanophotonic platform for a new generation of ultrathin lenses, displays, polarizers and other devices. Their success hinges on advances in lithography and nanofabrication in recent decades. While existing nanolithography techniques are suitable for basic research and prototyping, issues of cost, throughput, scalability, and substrate compatibility may preclude their use for many metasurface applications. Patterning via spontaneous self-assembly of block copolymer thin films offers an enticing alternative for nanophotonic manufacturing that is rapid, inexpensive, and applicable to large areas and diverse substrates. This review discusses the advantages and disadvantages of block copolymer-based nanopatterning and highlights recent progress in their use for broadband antireflection, surface enhanced Raman spectroscopy, and other nanophotonic applications. Recent advances in diversification of self-assembled block copolymer nanopatterns and improved processes for enhanced scalability of self-assembled nanopatterning using block copolymers are also discussed, with a spotlight on directions for future research that would enable a wider array of nanophotonic applications.

High-throughput morphology mapping of self-assembling ternary polymer blends

Multicomponent blending is a convenient yet powerful approach to rationally control the material structure, morphology, and functional properties in solution-deposited films of block copolymers and other self-assembling nanomaterials. However, progress in understanding the structural and morphological dependencies on blend composition is hampered by the time and labor required to synthesize and characterize a large number of discrete samples. Here, we report a new method to systematically explore a wide composition space in ternary blends. Specifically, the blend composition space is divided into gradient segments deposited sequentially on a single wafer by a new gradient electrospray deposition tool, and characterized using high-throughput grazing-incidence small-angle X-ray scattering. This method is applied to the creation of a ternary morphology diagram for a cylinder-forming polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) block copolymer blended with PS and PMMA homopolymers. Using “wet brush” homopolymers of very low molecular weight (∼1 kg mol⁻¹), we identify well-demarcated composition regions comprising highly ordered cylinder, lamellae, and sphere morphologies, as well as a disordered phase at high homopolymer mass fractions. The exquisite granularity afforded by this approach also helps to uncover systematic dependencies among self-assembled morphology, topological grain size, and domain period as functions of homopolymer mass fraction and PS : PMMA ratio. These results highlight the significant advantages afforded by blending low molecular weight homopolymers for block copolymer self-assembly. Meanwhile, the high-throughput, combinatorial approach to investigating nanomaterial blends introduced here dramatically reduces the time required to explore complex process parameter spaces and is a natural complement to recent advances in autonomous X-ray characterization.

Compositionally graded electrospray deposition combined with grazing incidence small angle X-ray scattering forms a high-throughput approach for mapping phase behavior in ternary mixtures as demonstrated here using block copolymer blends.

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection.