Porous organic polymers with labile leaving groups offer direct access to reactive functional groups, otherwise not permissible during network formation. In a one-step, open air, self-coupling reaction of tris bromomethyl benzene, we report highly porous, bromine rich C–C bonded porous polymers. Due to the steric nature of the monomer, restrictive crosslinking allowed pendent bromine groups to remain unreacted and provided rapid exchange into amines, nitriles, and thiols. This simple but powerful strategy yielded two isostructural but varying porosity and pendent group density polymers, allowing a comparative gas uptake study. Despite having lower surface area, the porous polymer formed at low temperature showed higher amination due to higher density of bromine groups. The polymers with more pendant groups resulted better CO2 uptake performances than higher porosity polymers with less pendant groups. Although post-modification decreased surface area of materials, amine functionalization greatly improved the CO₂ uptake capacity. The ethylenediamine appended version exhibited 4.7 times increase in CO₂ uptake capacity with highest CO₂/N₂ selectivity of 729 (298 K), and with an isosteric heat of 97 kJ mol−1 at zero coverage.

Thermoelectric (TE) materials could provide an efficient means for recovering waste heat energy if a low cost, scalable, and high figure-of-merit material could be fabricated. Here, we report, for the first time, a wet ball-milling method to achieve high-performance two-dimensional (2D) semi-metallic TiS2 nanoplatelets. TiO2 is milled, annealed, and sintered with sulfur under high pressure. The addition of a small amount of sulfur (S) powder during the annealing period prevents sulfur deficiency in the sintered compact, resulting in the formation of a near-stoichiometric TiS2 composition. The formation of 2D TiS2 nanoplatelets was confirmed by X-ray diffraction, field emission scanning electron microscopy with energy-dispersive spectroscopy, and X-ray photoelectron spectroscopy. The TE properties were measured in the temperature range of 25–100 °C. Further, we obtain that the prepared TiS2 has as high figure of merit as 0.35 at 100 °C. Novel wet ball mill processing strategies for the development of high-performance 2D materials such as TiS2 make it possible to incorporate these materials for scaled-up device fabrication.

Ordered mesoporous carbon materials offer robust network of organized pores for energy storage and catalysis applications, but suffer from time-consuming and intricate preparations hindering their widespread use. Here we report a new and rapid synthetic route for a N-doped ordered mesoporous carbon structure through a preferential heating of iron oxide nanoparticles by microwaves. A nanoporous covalent organic polymer is first formed in situ covering the hard templates of assembled nanoparticles, paving the way for a long-range order in a carbonaceous nanocomposite precursor. Upon removal of the template, a well-defined cubic mesoporous carbon structure was revealed. The ordered mesoporous carbon was used in solid state hydrogen storage as a host scaffold for NaAlH4, where remarkable improvement in hydrogen desorption kinetics was observed. The state-of-the-art lowest activation energy of dehydrogenation as a single step was attributed to their ordered pore structure and N-doping effect.

Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory–Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.

A structurally characterized novel dual–pocketed tetra–conjugated bisphenol–based chromophore (fluorescence = 652 nm) was synthesized in gram scale in ~90% yield from its tetraaldehyde. Highly selective, naked-eye detection of CN− (DMSO/H2O) was confirmed by interferent testing. A detection limit of 0.38 µM, within the permissible limit of CN− concentration in drinking water was achieved as mandated by WHO. The “reversibility” study shows potential applicability and reusability of Sen. Moreover, cost-effective and on-site interfaces, application tools such as fabricated cotton swabs, plastic Petri dishes, and filter papers further demonstrated the specific selectivity of Sen for the toxic CN−. In addition, an easily available and handy smartphone-assisted “Color Picker” app was utilized to help estimate the concentration of CN− ion present. A dual phenol deprotonation mechanism is active and supported by 1H NMR spectroscopic data and DFT calculation results.