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1. Toposelective vapor deposition of hybrid and inorganic materials inside nanocavities by polymeric templating and vapor phase infiltration

   https://doi.org/10.1039/D2NA00291D  

   Lovikka, Ville A. ; Airola, Konsta ; McGuinness, Emily ; Zhang, Chao ; Vehkamäki, Marko ; Kemell, Marianna ; Losego, Mark ; Ritala, Mikko ; Leskelä, Markku ( January 2022 , Nanoscale Advances)

   Selective deposition of hybrid and inorganic materials inside nanostructures could enable major nanotechnological advances. However, inserting ready-made composites inside nanocavities may be difficult, and therefore, stepwise approaches are needed. In this paper, a poly(ethyl acrylate) template is grown selectively inside cavities via condensation-controlled toposelective vapor deposition, and the polymer is then hybridized by alumina, titania, or zinc oxide. The hybridization is carried out by infiltrating the polymer with a vapor-phase metalorganic precursor and water vapor either via a short-pulse (atomic layer deposition, ALD) or a long-pulse (vapor phase infiltration, VPI) sequence. When the polymer-MO x hybrid material is calcined at 450 °C in air, an inorganic phase is left as the residue. Various suspected confinement effects are discussed. The infiltration of inorganic materials is reduced in deeper layers of the cavity-grown polymer and is dependent on the cavity geometry. The structure of the inorganic deposition after calcination varies from scattered particles and their aggregates to cavity-capping films or cavity-filling low-density porous deposition, and the inorganic deposition is often anisotropically cracked. A large part of the infiltration is achieved already during the short-pulse experiments with a commercial ALD reactor. Furthermore, the infiltrated polymer is more resistant to dissolution in acetone whereas the inorganic component can still be heavily affected by phosphoric acid. 

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2. Capillary Assembly of Liquid Particles

   https://doi.org/10.1002/smll.201907523  

   Shillingford, Cicely ; Kim, Brandon M. ; Weck, Marcus ( March 2020 , Small)

   Capillary assembly is a versatile method for depositing colloidal particles within templates, resulting in nano/microarrays and colloidal superstructures for optical, plasmonic, and sensory applications. Liquid particles (LPs), comprised of oligomerized 3‐(trimethoxysilyl)propyl methacrylate, are herein shown to deposit into patterned cavities via capillary assembly. In contrast to solid colloids, LPs coalesce upon solvent evaporation and assume the geometry of the template. Incorporating small molecules such as dyes followed by LP solidification generates fluorescent polymer microarrays of any geometry. The LP size is inversely proportional to the quantity of deposited material and the convexity of the final polymer array. Cavity filling can be tuned by increasing the assembly temperature. Extraction of the polymerized regions produces solidified particles with faceted shapes including square prisms, trapezoids, and ellipsoids with sizes up to 14 µm that retain the shape of the cavity in which they are initially held. LP deposition thus presents a highly controllable fabrication scheme for geometrically diverse polymer microarrays and anisotropic colloids of any conceivable polygonal shape due to space filling of the template. The extension of capillary assembly to LPs that can be doped with small molecule dyes and analytes invaluably expands the synthetic toolbox for top‐down, scalable, hierarchically engineered materials.

    

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3. Emission of R6G dye in Fabry–Perot cavities in weak and strong coupling regimes

   https://doi.org/10.1364/JOSAB.403612  

   Faruk, Md Omar ; Jerop, Nelly ; Noginov, Mikhail A. ( October 2020 , Journal of the Optical Society of America B)

   We have studied spectra and angular distribution of emission in Fabry–Perot cavities formed by two silver mirrors separated by a layer of poly (methyl methacrylate) polymer doped with rhodamine 6G (R6G) dye in low ($20\mathrm{g}/\mathrm{l}$) and high ($200\mathrm{g}/\mathrm{l}$) concentrations. The frequency of emission radiated to a cavity mode was larger at large outcoupling angles—the “rainbow” effect. At the same time, the angle of the strongest emission was also determined by the cavity size: the larger the cavity, the larger the angle. The angular distribution of emission is commonly dominated by two symmetrical lobes (located at the intersection of the three-dimensional emission cone with a horizontal plane) pointing to the left and to the right of the normal to the sample. Despite the strong Stokes shift in R6G dye, the branch of the cavity dispersion curve obtained in the emission experiment is positioned above the one obtained in the reflection (extinction) experiment. Some dye molecules are poorly coupled to cavity modes. Their emission has very broad angular distribution with the maximum at$\mathrm{\theta <\#comment/>}=0^{\circ <\#comment/>}$. The signatures of strong cavity–exciton coupling were observed at high dye concentration ($200\mathrm{g}/\mathrm{l}$) but not at low concentration ($20\mathrm{g}/\mathrm{l}$). The evidence of the effect of strong coupling on emission is exemplified by a strong difference in the angular distribution of emission in two almost identical cavities, one with and another without strong coupling. Most importantly, we have demonstrated the possibility to control the ground state concentration, the coupling strength, and the dye emission spectra with Q-switched laser pulses.

    

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4. Gels: Energetics, Singularities, and Cavitation

   https://doi.org/10.1007/s10659-023-10000-5  

   Calderer, M. Carme ; Henao, Duvan ; Sánchez, Manuel A. ; Siegel, Ronald A. ; Song, Sichen ( March 2023 , Journal of Elasticity)

   This article studies equilibrium singular configurations of gels and addresses open questions concerning gel energetics. We model a gel as an incompressible, immiscible and saturated mixture of a solid polymer and a solvent that sustain chemical interactions at the molecu- lar level. We assume that the energy of the gel consists of the elastic energy of its polymer network plus the Flory-Huggins energy of mixing. The latter involves the entropic energies of the individual components plus that of interaction between polymer and solvent, with the temperature dependent Flory parameter, χ , encoding properties of the solvent. In particular, a good solvent promoting the mixing regime, is found below the threshold value χ = 0.5, whereas the phase separating regime develops above that critical value. We show that cavi- ties and singularities develop in the latter regime. We find two main classes of singularities: (i) drying out of the solvent, with water possibly exiting the gel domain through the bound- ary, leaving behind a core of exposed polymer at the centre of the gel; (ii) cavitation, in response to traction on the boundary or some form of negative pressure, with a cavity that can be either void or flooded by the solvent. The straightforward and unified mathematical approach to treat all such singularities is based on the construction of appropriate test func- tions, inspired by the particular states of uniform swelling or compression. The last topic of the article addresses a statistical mechanics rooted controversy in the research commu- nity, providing an experimental and analytic study in support of the phantom elastic energy versus the affine one. 

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5. A MACHINE LEARNING APPROACH FOR IMPACT DAMAGE QUANTIFICATION IN POLYMER MATRIX COMPOSITES

   https://doi.org/10.12783/asc37/36412  

   BERKOWITZ, KATHERINE ; GUHA, RISHABH D. ; OLUWAJIRE, OLUWATIMILEHIN ; GRACE, LANDON ( September 2022 , American Society for Composites)

   Largely due to superior properties compared to traditional materials, the use of polymer matrix composites (PMC) has been expanding in several industries such as aerospace, transportation, defense, and marine. However, the anisotropy and nonhomogeneity of these structures contribute to the difficulty in evaluating structural integrity; damage sites can occur at multiple locations and length scales and are hard to track over time. This can lead to unpredictable and expensive failure of a safety-critical structure, thus creating a need for non-destructive evaluation (NDE) techniques which can detect and quantify small-scale damage sites and track their progression. Our research group has improved upon classical microwave techniques to address these needs; utilizing a custom device to move a sample within a resonant cavity and create a spatial map of relative permittivity. We capitalize on the inevitable presence of moisture within the polymer network to detect damage. The differing migration inclinations of absorbed water molecules in a pristine versus a damaged composite alters the respective concentrations of the two chemical states of moisture. The greater concentration of free water molecules residing in the damage sites exhibit highly different relative permittivity when compared to the higher ratio of polymer-bound water molecules in the undamaged areas. Currently, the technique has shown the ability to detect impact damage across a range of damage levels and gravimetric moisture contents but is not able to specifically quantify damage extent with regards to impact energy level. The applicability of machine learning (ML) to composite materials is substantial, with uses in areas like manufacturing and design, prediction of structural properties, and damage detection. Using traditional NDE techniques in conjunction with supervised or unsupervised ML has been shown to improve the accuracy, reliability, or efficiency of the existing methods. In this work, we explore the use of a combined unsupervised/supervised ML approach to determine a damage boundary and quantification of single-impact specimens. Dry composite specimens were damaged via drop tower to induce one central impact site of 0, 2, or 3 Joules. After moisture exposure, Entrepreneur Dr, Raleigh, North Carolina 27695, U.S.A. 553 each specimen underwent dielectric mapping, and spatial permittivity maps were created at a variety of gravimetric moisture contents. An unsupervised K-means clustering algorithm was applied to the dielectric data to segment the levels of damage and define a damage boundary. Subsequently, supervised learning was used to quantify damage using features including but not limited to thickness, moisture content, permittivity values of each cluster, and average distance between points in each cluster. A regression model was trained on several samples with impact energy as the predicted variable. Evaluation was then performed based on prediction accuracy for samples in which the impact energies are not known to the model.

    

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