arXiv subjects

Lijie Guo

Publications and source records attributed to Lijie Guo.

3 recordsLinked to original sources

arXiv2025

Silk-Nano-Fibroin Aerogels: A Bio-Derived, Amine-Rich Platform for Rapid and Reversible CO2 Capture

Despite growing interest in bio-based materials, rapid, low-temperature CO2 capture using amine-rich natural sorbents has received limited attention. In recent years, various porous solid sorbents have drawn significant research interest as promising carbon capture materials. However, high synthesis cost, limited CO2 adsorption capacity, sluggish adsorption-desorption kinetics, high sorbent regeneration temperature, and poor operational stability remain major challenges for their practical implementation. Here, we present silk-nano-fibroin aerogels derived from natural mulberry silk as a sustainable, amine-rich, and support-free sorbent platform for energy-efficient CO2 capture. The aerogels exhibit a CO2 adsorption capacity competitive with state-of-the-art amino acid and amino acid ionic liquid-based solid sorbents. Thermogravimetric analysis confirms high thermal stability up to 250C, substantially higher than that of conventional amine sorbents, while complete sorbent regeneration occurs at only 60 C. Furthermore, the silk-nano-fibroin aerogels demonstrate rapid adsorption-desorption kinetics, excellent multicycle stability, and full retention of CO2 adsorption capacity under humid conditions. Spectroscopic analyses (XPS, FTIR, Raman, and solid-state 13C NMR) confirm reversible CO2 chemisorption through intrinsic amine sites within the silk-fibroin backbone. Overall, this work establishes silk-nano-fibroin aerogels as a sustainable and low-cost route toward energy-efficient CO2 capture.

physics.chem-ph↗

arXiv2022

Triangle Graph Interest Network for Click-through Rate Prediction

Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.

cs.AI↗

arXiv2019

Revealing the Atomic Structure of Silicate Glasses by Force-Enhanced Atomic Refinement

Although experiments can offer some fingerprints of the atomic structure of glasses (coordination numbers, pair distribution function, etc.), atomistic simulations are often required to directly access the structure itself (i.e., the positions of the atoms). On the one hand, molecular dynamics (MD) simulations can be used to generate by quenching a liquid - but MD simulations remain plagued by extremely high cooling rates. On the other hand, reverse Monte Carlo (RMC) modeling bypasses the melt-quenching route - but RMC often yields non-unique glass structures. Here, we adopt the force-enhanced atomic refinement (FEAR) method to overcome these limitations and decipher the atomic structure of a sodium silicate glass. We show that FEAR offers an unprecedented description of the atomic structure of sodium silicate. The FEAR-generated glass structure simultaneously exhibits (i) enhanced agreement with experimental neutron diffraction data and (ii) higher energetic stability as compared to those generated by MD or RMC. This result allows us to reveal new insights into the atomic structure of sodium silicate glasses. Specifically, we show that sodium silicate glasses exhibit a more ordered medium-range order structure than previously suggested by MD simulations. These results pave the way toward an increased ability to accurately describe the atomic structure of glasses.

cond-mat.mtrl-sci↗