arXiv subjects

Alexei A. Lapkin

Publications and source records attributed to Alexei A. Lapkin.

3 recordsLinked to original sources

arXiv2025

Tailoring the Morphology of Cellulose Nanocrystals via Controlled Aggregation

Cellulose nanocrystals (CNCs) are elongated nanoparticles derived from natural cellulose, with potential applications ranging from rheological modifiers and emulsion stabilizers to photonic pigments and sensors. For most applications, precise control over CNC morphology and surface chemistry is essential, but the relationship between process parameters, CNC characteristics, and their resulting behavior is poorly understood. Here, we investigate the impact of centrifugation and ionic strength on CNC morphology after dialysis using transmission electron microscopy, small-angle X-ray scattering and scanning electron diffraction. We find that the centrifugation step commonly applied during CNC purification promotes the formation of compact composite nanoparticles made of aligned crystallites, referred to as 'bundles', that are associated preferentially along their hydrophobic faces. In stark contrast, transient exposure to high ionic strength leads to fractal-like, irregular composite nanoparticles. We then examine the consequence of these morphological differences on the cholesteric self-organization of the CNCs: aligned bundles reduce the cholesteric pitch in suspension, causing a blue-shift in the color of dish-cast photonic films, while misaligned particles promote gelation, producing colorless films. This study reveals the importance of sample history, in particular, the often-disregarded purification steps, on CNC characteristics and their ensemble behavior, thereby unlocking new routes for tailoring this promising nanomaterial.

cond-mat.soft↗

arXiv2023

Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction

We propose Gibbs-Duhem-informed neural networks for the prediction of binary activity coefficients at varying compositions. That is, we include the Gibbs-Duhem equation explicitly in the loss function for training neural networks, which is straightforward in standard machine learning (ML) frameworks enabling automatic differentiation. In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations. Rather, Gibbs-Duhem consistency serves as regularization, with the flexibility of ML models being preserved. Our results show increased thermodynamic consistency and generalization capabilities for activity coefficient predictions by Gibbs-Duhem-informed graph neural networks and matrix completion methods. We also find that the model architecture, particularly the activation function, can have a strong influence on the prediction quality. The approach can be easily extended to account for other thermodynamic consistency conditions.

physics.chem-ph↗

arXiv2020

Economically viable CO2 electroreduction embedded within ethylene oxide manufacturing

Electrochemical conversion of CO2 (CO2R) into fuels and chemicals can both reduce CO2 emissions and allow for clean manufacturing in the scenario of significant expansion of renewable power generation. However, large-scale process deployment is currently limited by unfavourable process economics resulting from significant up- and down-stream costs for obtaining pure CO2, separation of reaction products and increased logistical effort. We have discovered a method for economically viable recycling of waste CO2 that addresses these challenges. Our approach is based on integration of a CO2R unit into an existing manufacturing process: ethylene oxide (EO) production, which emits CO2 as a by-product. The standard EO process separates waste CO2 from gas stream, hence the substrate for electroreduction is available at an EO plant at no additional cost. CO2 can be converted into an ethylene-rich stream and recycled on-site back to the EO reactor, which uses ethylene as a raw material, and also the anode product (oxygen) can be simultaneously valorized for the EO production reaction. If powered by a renewable electricity source, the process will significantly (ca. 80%) reduce the CO2 emissions of an EO manufacturing plant. A sensitivity analysis shows that the recycling approach can be economically viable in the short term and that its payback time could be as low as 1-2 years in the regions with higher carbon taxes and/or with access to low-cost electricity sources.

physics.chem-ph↗