arXiv subjects

Qixin Guo

Publications and source records attributed to Qixin Guo.

6 recordsLinked to original sources

arXiv2026

Sustainable photocatalytic CO2 conversion using microalgae as a carbon-negative scavenger

Photocatalytic CO2 conversion driven by solar energy is a highly promising approach in addressing rising atmospheric CO2 levels; however, its practical application remains limited by low conversion efficiency. In this study, a new strategy to enhance CO2 reduction toward CO and CH4 is proposed through the employment of microalgae as a sacrificial agent, and the efficiency is compared with conventional CO2 conversion without and with the use of microplastics as sacrificial agents. To realize this strategy, an AB2O6-type high-entropy oxide (HEO), (Cs1/7Ba4/7Bi2/7)(Nb1/2Ta1/2)2O6, with bi-polymorphy of layered perovskite and pyrochlore, is rationally designed. The HEO incorporates alkali metal cesium and alkaline earth metal barium to increase surface basicity for CO2 chemisorption, bismuth with its stereochemically active lone pairs for localized polarization and charge separation, and tantalum and niobium to form octahedral crystalline frameworks for charge transport. The utilization of microalgae during photocatalytic reactions leads to a remarkable enhancement in CO2 conversion compared to catalysis with or without using microplastics, with CO and CH4 production increasing by 10- and 4-fold, respectively, compared to the system using only HEO. These findings not only demonstrate a new family of polymorphic AB2-type HEOs for photocatalysis but also show the potential of microalgae as a sustainable sacrificial agent, offering an environmentally friendly pathway for efficient CO2 capture (through photosynthesis by microalgae) and CO2 conversion (through photocatalysis by HEOs).

physics.chem-ph↗

arXiv2026

DeRes: Decoupling Residual Stability and Adaptivity for Scalable CTR Prediction

Transformer-based CTR models face a growing bottleneck at the residual connection: under Pre-Norm, early user-interest signals are diluted layer by layer; the identity skip cannot forget stale interests; and each layer sees only its immediate predecessor, losing long-range cross-layer dependencies. Recent attention-based residual variants (AttnRes) address parts of this in language models, but drop the protective identity skip and have not been tried in recommendation. Drawing on Dual Path Networks (DPN) and the HORNN view of residuals, we present DeRes, which routes each layer through two parallel paths -- an Identity residual path that preserves first-order feature reuse and gradient flow, and a Block Attention Residual path that attends over compressed outputs of all earlier blocks for high-order recall. A vector-wise gate decides, per hidden dimension, the weight given to each path. We further propose Pointwise AttnRes, replacing the Softmax in the cross-layer attention with SiLU so that multiple past blocks can be activated simultaneously and irrelevant ones receive negative (forgetting) weights -- better aligned with CTR's parallel multi-interest patterns. On a large-scale industrial dataset (331M interactions from a major social-media platform), Criteo (45M), and Avazu (40M), DeRes outperforms twelve baselines including OneTrans, TokenMixer-Large, UniMixer, mHC, and AttnRes, achieving up to +0.32% AUC at under 5% extra FLOPs. Beyond a single operating point, DeRes fits a markedly steeper compute-AUC scaling law (gamma=0.118 vs. 0.071 for OneTrans, a 1.66x gap), so an 8-layer DeRes matches a 16-layer OneTrans -- about 2x compute saving at equivalent AUC. Ablations confirm that the dual-path design outperforms either single path, Identity beats learnable residuals, and SiLU beats Softmax.

cs.IR↗

arXiv2026

CapsID: Soft-Routed Variable-Length Semantic IDs for Generative Recommendation

Generative recommendation maps each item to a sequence of Semantic IDs (SIDs) and recasts retrieval as autoregressive token generation. In this paradigm the main bottleneck is the tokenizer rather than the Transformer: residual vector quantization with a hard nearest-neighbor assignment at every layer collapses multi-faceted item semantics at cluster boundaries and propagates early errors to later SID positions. A common workaround is to append a dense vector or attribute prefix to the SID, but this dual-representation design inflates inference cost and gives up the simplicity of a generative interface. We address the bottleneck at the tokenizer itself. CAPSID replaces hard residual quantization with capsule routing: at each layer an item probabilistically routes to several semantic capsules, the residual is updated by the routed reconstruction rather than by a single winning code, and the SID terminates once the active capsule's confidence is high enough. On top of CAPSID, SEMANTICBPE composes adjacent SID tokens into reusable subwords by combining their co-occurrence with their embedding compatibility. On Amazon Beauty, Sports, Toys, and a 35M-item proprietary industrial catalog, CAPSID+SEMANTICBPE improves Recall at 10 by 9.6% on average over ReSID, the strongest single-representation baseline, and matches or exceeds a COBRA-style sparse-dense system on every public benchmark while running at 51% of its inference latency. Ablations show that soft routing, iterative agreement, and confidence-driven length each contribute independently, and the gains are largest on tail items where boundary semantics dominate.

cs.IR↗

arXiv2025

Phase and sulfur vacancy engineering in cadmium sulfide for boosting hydrogen production from catalytic plastic waste photoconversion

Cadmium sulfide (CdS) is a well-known low-bandgap photocatalyst, but its efficiency is often hindered by rapid photo-generated carrier recombination and a limited number of active catalytic sites. To overcome these challenges, this study introduces an efficient CdS photocatalyst through a novel strategy combining metastable-to-stable phase transformation and sulfur vacancy generation. This strategy integrates hydrothermal treatment and a high-pressure process to create sulfur vacancies, which serve as active catalytic sites, within a thermodynamically stable wurtzite (hexagonal) phase known for its superior photocatalytic properties. The resulting CdS photocatalyst demonstrates exceptional performance in photoreforming for hydrogen production and the conversion of polyethylene terephthalate (PET) plastic into valuable materials. Compared to commercial CdS catalysts, this new material shows a 23-fold increase in both hydrogen production and plastic degradation without the need for co-catalysts. Quenching experiments reveal that holes and hydroxyl radicals play crucial roles in the photoreforming process of this vacancy-rich CdS. First-principles calculations via density functional theory (DFT) indicate that the hexagonal phase possesses a lower bandgap and it exhibits further bandgap narrowing with the introduction of sulfur vacancies. These findings not only present an innovative approach to CdS processing but also highlight the critical role of sulfur vacancies as effective defects for the catalytic photoreforming of microplastics.

cond-mat.mtrl-sci↗

arXiv2023

Dynamic Offloading Loading Optimization in distributed Fault Diagnosis system with Deep Reinforcement Learning Approach

Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes.

cs.IT↗

arXiv2022

Facile synthesis of Cu2O nanorods in the presence of NaCl by SILAR method and its characterizations

Cu2O nanorods have been deposited on soda-lime glass (SLG) substrates by the modified SILAR technique by varying the concentration of NaCl electrolyte into the precursor complex solution. The structural, electrical, and optical properties of synthesized Cu2O nanorod films have been studied by a variety of characterization tools. Structural analyses by XRD confirmed the polycrystalline Cu2O phase with (111) preferential growth. Raman scattering spectroscopic measurements conducted at room temperature also showed characteristic peaks of the pure Cu2O phase. The surface resistivity of the Cu2O nanorod films decreased from 15,142 to 685 Ohm.cm with the addition of NaCl from 0 to 4 mmol, and then exhibited an opposite trend with further addition of NaCl. The optical bandgap of the synthesized Cu2O nanorod films was observed as 1.88 - 2.36 eV, while the temperature-dependent activation energies of the Cu2O films were measured as about 0.14 - 0.21 eV. SEM morphologies demonstrated Cu2O nanorod as well as closely packed spherical grains with the alteration of NaCl concentration. The Cu2O phase of nanorods was found stable up to 230 0C corroborating the optical bandgap results of the same. The film fabricated in presence of 4 mmol of NaCl showed the lowest resistivity and activation energy as well comparatively uniform nanorod morphology. Our studies demonstrate that the nominal presence of NaCl electrolytes in the pre-cursor solutions has a significant impact on the physical properties of Cu2O nanorod films which could be beneficial in optoelectronic research.

physics.chem-ph↗