In this work, a novel g-C
N
filled, phosphoric-crosslinked chitosan gel bead (P-CS@CN) was successfully prepared to adsorb U(VI) from water. The separation performance of chitosan was improved by ...introducing more functional groups. At pH 5 and 298 K, the adsorption efficiency and adsorption capacity could reach 98.0 % and 416.7 mg g
, respectively. After adsorption, the morphological structure of P-CS@CN did not change and adsorption efficiency remained above 90 % after 5 cycles. P-CS@CN exhibited an excellent applicability in water environment based on dynamic adsorption experiments. Thermodynamic analyses demonstrated the value of ΔG, manifesting the spontaneity of U(VI) adsorption process on P-CS@CN. The positive values of ΔH and ΔS showed that the U(VI) removal behavior of P-CS@CN was an endothermic reaction, indicating that the increase of temperature was great benefit to the removal. The adsorption mechanism of P-CS@CN gel bead could be summarized as the complexation reaction with the surface functional groups. This study not only developed an efficient adsorbent for the treatment of radioactive pollutants, but also provided a simple and feasible strategy for the modification of chitosan-based adsorption materials.
Responsive core-shell nanoparticles at fluid interfaces offer great potential for realizing controllable self-assembly that can benefit various applications, ranging from 2D nanomaterials synthesis, ...switchable emulsions and microdroplet reactor. The intricate interplay of various forces acting on particles gives rise to interesting interfacial dynamics of these responsive and deformable core-shell particles. The responsive core-shell particles could not only change interparticle interactions at interfaces but also droplet-droplet interactions when adsorbed at water-oil interfaces in Pickering emulsions. Thus, a novel mechanism of achieving switchable emulsions can also be obtained.In this dissertation, the electrostatic dissipative particle dynamics (EDPD) was developed to model the interfacial dynamics of pH-responsive core-shell nanoparticles at water-oil interfaces and probe the direct interactions between emulsion droplets stabilized with these active nanoparticles. The model nanoparticle is functionalized with weak polyelectrolytes to render the pH-sensitivity. During changing the degree of ionization α (i.e., pH value in water), the adsorption of PGNPs was prohibited and the morphology of particles was spreading. The introduction of electrolytes can screen the image charge effect and mitigate the adsorption prohibition. Besides the study of adsorption behavior, qualitative and quantitative analysis of the monolayer microstructure showed a disorder-order phase transition, which is driven by the modulation of interparticle electrostatic interactions subject to pH changes. Different regimes of particle self-diffusion in the monolayer were identified and correlated with the structural transition. We further modeled the head-on collision and coalescence of two emulsion droplets covered by PGNPs. The maximal resistance forces were measured to quantitatively discriminate the efficacy of particles in stabilizing emulsions at different degrees of ionization. Moreover, the influence of ionization state was studied in various surface coverage regimes and shows two different mechanisms of preventing coalescence. The findings of these numerical simulations provide greater insights into the interfacial behavior of active polymer-grafted nanoparticles at water-oil interfaces and open a new avenue for achieving responsive emulsions by tuning direct interactions between emulsions.
Nickel molybdate (NiMoO4) nanowires were prepared by hydrothermal method on chemical-vapor-deposition-grown three-dimensional graphene skeletons. The X-ray diffraction and Raman results show NiMoO4 ...nanowires are α phase. This binder-free and ultralight graphene/ NiMoO4 composite was used as a positive electrode for supercapacitors. This electrode presents a high specific capacitance of 1194Fg−1 at 12mAcm−2 and good stability with a cycling efficiency of 87.3% after 2000 cycles. Further, the deduced energy density reaches 41Whkg−1 at a steady power density of 1319Wkg−1. These results demonstrate the potential application of the designed composite for future flexible and lightweight energy storage devices.
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•Three dimensional graphene networks were prepared.•The graphene/NiMoO4 nanowire composite was fabricated by hydrothermal method.•The composite presents a specific capacitance of 1194Fg−1 at 12mAcm−2.•The composite electrode shows a good cycling efficiency of 87% after 2000 cycles.
Graph neural networks (GNNs) have been widely used for predicting molecular properties, especially for single molecules. However, when treating multi-component systems, GNNs have mostly used simple ...data representations (concatenation, averaging, or self-attention on features of individual components) that might fail to capture molecular interactions and potentially limit prediction accuracy. In this work, we propose a GNN architecture that captures molecular interactions in an explicit manner by combining atomic-level (local) graph convolution and molecular-level (global) message passing through a molecular interaction network. We tested the architecture (which we call SolvGNN) on a comprehensive phase equilibrium case study that aims to predict activity coefficients for a wide range of binary and ternary mixtures; we built this large dataset using the COnductor-like Screening MOdel for Real Solvation (COSMO-RS). We show that SolvGNN can predict composition-dependent activity coefficients with high accuracy and show that it outperforms a previously-developed GNN used for predicting only infinite-dilution activity coefficients. We performed counterfactual analysis on the SolvGNN model that allowed us to explore the impact of functional groups and composition on equilibrium behavior. We also used the SolvGNN model for the development of a computational framework that automatically creates phase diagrams for a diverse set of complex mixtures. All scripts needed to reproduce the results are shared as open-source code.
A noninvasive, highly sensitive universal immunosensor platform for protein-based biomarker detection is described in this Article. A neutral charged sensing environment is constructed by an antibody ...with an oppositely charged amino acid as surface charge neutralizer. By adjusting the pH condition of the testing environment, this neutral charged immunosensor (NCI) directly utilizes the electrostatic charges of the analyte for quantification of circulating protein markers, achieving a wide dynamic range covering through the whole picomole level. Comparing with previous studies on electrostatic charges characterization, this NCI demonstrates its capability to analyze not only the negatively charged biomolecules but also positively charged analytes. We applied this NCI for the detection of HE4 antigen with a detection limit at 2.5 pM and Tau antigen with a detection limit at 0.968 pM, demonstrating the high-sensitivity property of this platform. Furthermore, this NCI possesses a simple fabrication method (less than 2 h) and a short testing turnaround time (less than 30 min), providing an excellent potential for further clinical point-of-care applications.
Lithium-sulfur battery is an attractive option for next-generation energy storage due to its much higher theoretical energy density than state-of-the-art lithium-ion batteries. However, the massive ...volume changes of the sulfur cathode and the uncontrollable deposition of Li2S2/Li2S significantly deteriorate cycling life and increase voltage polarization. To address these challenges, we develop an ε-caprolactam/acetamide based eutectic-solvent electrolyte, which can dissolve all lithium polysulfides and lithium sulfide (Li2S8-Li2S). With this new electrolyte, high specific capacity (1360 mAh·g-1) and reasonable cycling stability are achieved. Moreover, in contrast to conventional ether electrolyte with a low flash point (~2°C), such low-cost eutectic-solvent-based electrolyte is difficult to ignite, and thus can dramatically enhance battery safety. This research provides a new approach to improving lithium-sulfur batteries in aspects of both safety and performance.
The lithium–sulfur battery is an attractive option for next‐generation energy storage owing to its much higher theoretical energy density than state‐of‐the‐art lithium‐ion batteries. However, the ...massive volume changes of the sulfur cathode and the uncontrollable deposition of Li2S2/Li2S significantly deteriorate cycling life and increase voltage polarization. To address these challenges, we develop an ϵ‐caprolactam/acetamide based eutectic‐solvent electrolyte, which can dissolve all lithium polysulfides and lithium sulfide (Li2S8–Li2S). With this new electrolyte, high specific capacity (1360 mAh g−1) and reasonable cycling stability are achieved. Moreover, in contrast to conventional ether electrolyte with a low flash point (ca. 2 °C), such low‐cost eutectic‐solvent‐based electrolyte is difficult to ignite, and thus can dramatically enhance battery safety. This research provides a new approach to improving lithium–sulfur batteries in aspects of both safety and performance.
Schwer entflammbar: Ein neues, sicheres, auf ϵ‐Caprolactam/Acetamid basierendes eutektisches Lösungsmittel kann als Elektrolyt für Lithium‐Schwefel‐Batterien eingesetzt werden. Es zeigt eine starke Feuerresistenz und kann sämtliche Lithiumsulfide (Li2S8–Li2S) lösen. Zudem ist es günstig und umweltfreundlich. Im Einsatz wurden Kapazitätserhaltungen von 68 % über 200 Zyklen bzw. 95 % über 40 Zyklen (ohne bzw. mit Elektrodenmodifikation) erreicht.
Monocrystalline PERC and multicrystalline Al-BSF cells were fabricated as 4-cell mini-modules with EVA encapsulant, and low, medium, and high vapor transmission rate (VTR) backsheets. Mini-modules ...were subjected to a total of 2500 hours of damp heat (85°C, 85%RH) exposure, and characterized with I-V and Suns-Voc every 500 hours. The results show that PERC mini-modules decreased in power by 10.0% through the exposure, while Al-BSF mini-modules lost 12.7% of their initial power. PERC mini-modules did not show increased sensitivity to any particular packaging materials combinations studied here. However, Al-BSF mini-modules exhibited less power loss when made with fluoropolymer coating/PET/PVDF backsheet. Confocal Raman was used to examine the fluorescence background of encapsulants, and an intense fluorescence background can be seen in mini-modules that experienced water condensation during damp heat exposure. Coupons samples made with EVA and POE encapsulants and the same backsheets were also characterized with FTIR step-wise through DH, revealing crystalline phase changes in the PVDF-containing backsheets.
We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing ...(e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs can exploit color as a key source of information, which enables the use of modern computer vision hardware (e.g., infrared, thermal, and hyperspectral cameras). We illustrate the concepts using diverse case studies arising in spectral analysis, molecule design, sensor design, image-based control, and multivariate process monitoring.
Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify ...uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.
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