Ionic-Functionalized Polymers of Intrinsic Microporosity for Gas Separation Applications

Anthracene and tetraphenylsilane based conjugated porous polymer nanoparticles for sensitive detection of nitroaromatics in water

Conjugated porous polymers (CPPs) are a kind of promising sensing materials for the detection of nitroaromatic compounds, but their sensing applications in aqueous media are limited because of their poor dispersity or solubility in water. In this study, we prepared anthracene and tetraphenylsilane based CPPs named PSiAn by conventional Suzuki coupling and Suzuki-miniemulsion polymerization, respectively. The structure, morphology and porosity of the CPPs were characterized by Fourier Transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (1H NMR), transmission electron microscope (TEM) and N2 sorption isotherm, respectively. Both of the CPPs have porous structure which is beneficial for the adsorption and diffusion of the analytes within them. The particle size of PSiAn nanoparticles prepared by Suzuki-miniemulsion polymerization is 10-40 nm from the TEM image, which facilitates the dispersion in the aqueous phase. Combined with the porosity and nanoparticle morphology, PSiAn nanoparticles realized the efficient photoluminescence (PL) sensing of nitroaromatic explosives in aqueous phase. The limit of detection (LOD) and limit of quantitation (LOQ) of PSiAn nanoparticles for 2,4,6-trinitrophenol (TNP) detection in the pure aqueous phase are 0.33 μM and 1.11 μM, respectively. Meanwhile, the good selectivity and anti-interference in presence of other nitro-compounds were observed. Furthermore, the spike/recovery test for the TNP detection in real water samples by PL sensing based on PSiAn nanoparticles indicates the quantitative recovery of TNP from 100.74 % to 101.00 %. The electrochemical test, ultraviolet-visible absorption spectra, excitation and emission spectra, and time-resolved PL spectra were investigated to explore the PL sensing mechanism. As a result, it is found that the fluorescence inner filter effect might be the predominant quenching mechanism during the detection of nitrophenolic compounds such as TNP and 4-nitrophenol (4-NP).

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

A modelling algorithm for amorphous covalent triazine-based polymers

Rational and purposeful designs of amorphous materials with desirable structures are difficult to implement due to the complex and unordered nature of such materials. In this work, a modelling algorithm was proposed for amorphous covalent triazine-based polymers to construct atomistic representative models that can reproduce the experimentally measured properties of experimental samples. The constructed models were examined through comparisons of simulated and experimental properties, such as surface area, pore volume, and structure factor, and further validated by the good consistency observed among these properties. To assess the predictive capability of the modelling algorithm, we used a new covalent triazine-based polymer and predicted its porosity by constructing a simulated model. The predicted results on the surface area and pore volume of the simulated model were quantitatively consistent with the experimental data derived from the experimentally synthesized sample. This consistency reveals the predictive capacity of the proposed modelling algorithm. The algorithm could be a promising approach to predict and develop advanced covalent triazine-based polymers for multiple applications.

Atomistic structure generation of covalent triazine-based polymers by molecular simulation

The structures of amorphous materials are generally difficult to characterize and comprehend due to their unordered nature and indirect measurement techniques. However, molecular simulation has been considered as an alternative method that can provide molecular-level information supplementary to experimental techniques. In this work, a new approach for modelling the atomistic structures of amorphous covalent triazine-based polymers is proposed and employed on two experimentally synthesized covalent triazine-based polymers. To examine the proposed modelling approach, the properties of the established models, such as surface areas, pore volumes, structure factors and N₂ adsorption isotherms, were calculated and compared with the experimental data. Excellent consistencies were observed between the simulated models and experimental samples, consequently validating the proposed models and the modelling approach. Moreover, the proposed modelling approach can be applied to new covalent triazine-based polymers for predictive purposes and to provide design strategies for future synthesis works.

A well-established modelling approach to construct and predict the structures of amorphous covalent triazine-based polymers is proposed.

Quantitative Assessment of Vapor Molecule Adsorption to Solid Surfaces by Flow Rate Monitoring in Microfluidic Channels

Measuring parameters related to gas adsorption on the effective surfaces of solid samples is important in catalyst studies. Further attention on the subject has appeared due to the materials and methods required to concentrate the gaseous biomarkers for detection. The conventional methods are mainly based on the volumetric and gravimetric analyses, which are applicable to bulk samples. No standard method has yet been provided for such measurements on thin films, which are the most commonly used samples for material screening. Here, a novel method is presented for the adsorption coefficient measurement on thin-film samples. This method comprises coating of the inner walls of a microfluidic channel with the thin film under test. The recorded diffusion rates for a trace gas along this microchannel are compared with the solutions of the adsorption-diffusion equation of the channel for determining the adsorption coefficient of the gas molecule to the inner walls of the channel. The high ratio of surface-to-volume in such channels magnifies the gas sorption effects and improves accuracy. The method is fast, versatile, and cost-effective, allowing measurements at different temperatures and atmospheric pressures. The adsorption coefficients of different isomers of butanol on poly(methyl methacrylate) sheets, zinc oxide thick films, and gold thin films are determined as examples.

Structure Design of Low-Temperature Regenerative Hyperbranched Polyamine Adsorbent for CO2 Capture

A novel low-temperature regenerative hydroxy-functionalized hyperbranched polyamine adsorbent (0.16OH-HBPA) for CO₂ capture was readily prepared using glutaraldehyde to cross-link amino-terminated hyperbranched polymers (HBP) and functionalized with glycidol, followed by the reduction of the imino groups of 0.16OH-HBPA to alkyl aminos using NaBH₄. Here, the HBP has been prepared through the one-pot reaction between pentaethylenehexamine and methyl acrylate. The as-prepared 0.16OH-HBPA adsorbent showed a high adsorption capacity (4.05 mmol/g) for CO₂ (concentration, 10%) in the presence of water at 25 °C, and the alkyl amino utilization efficiency reached 73%. More importantly, the CO₂-adsorbed 0.16OH-HBPA showed excellent regenerative performance at low temperatures (85 °C, under pure CO₂ gas) due to the introduced hydroxyl that can cooperatively adsorb CO₂ via the amino groups to form stable carbamic acid. This process suppressed the formation of open-chain urea and cyclic urea and could overcome the disadvantages of high regeneration temperatures (≥90 °C, under pure inert gas) of CO₂-adsorbed traditional solid amine adsorbents.

A nano-silicate material with exceptional capacity for CO2 capture and storage at room temperature

In order to mitigate climate change driven by the observed high levels of carbon dioxide (CO₂) in the atmosphere, many micro and nano-porous materials are being investigated for CO₂ selectivity, capture and storage (CCS) purposes, including zeolites, metal organic frameworks (MOFs), functionalized polymers, activated carbons and nano-silicate clay minerals. Key properties include availability, non-toxicity, low cost, stability, energy of adsorption/desorption, sorbent regeneration, sorption kinetics and CO₂ storage capacity. Here, we address the crucial point of the volumetric capture and storage capacity for CO₂ in a low cost material which is natural, non-toxic, and stable. We show that the nano-silicate Nickel Fluorohectorite is able to capture 0.79 metric tons of CO₂ per m³ of host material - one of the highest capacities ever achieved - and we compare volumetric and gravimetric capacity of the best CO₂ sorbent materials reported to date. Our results suggest that the high capture capacity of this fluorohectorite clay is strongly coupled to the type and valence of the interlayer cation (here Ni²⁺) and the high charge density, which is almost twice that of montmorillonite, resulting in the highest reported CO₂ uptake among clay minerals.