Search

Your search keyword '"Han, Rong"' showing total 2,149 results
Start Over Author "Han, Rong"
2,149 results on '"Han, Rong"'

1. VividMed: Vision Language Model with Versatile Visual Grounding for Medicine

Author

Luo, Lingxiao, Tang, Bingda, Chen, Xuanzhong, Han, Rong, and Chen, Ting

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Recent advancements in Vision Language Models (VLMs) have demonstrated remarkable promise in generating visually grounded responses. However, their application in the medical domain is hindered by unique challenges. For instance, most VLMs rely on a single method of visual grounding, whereas complex medical tasks demand more versatile approaches. Additionally, while most VLMs process only 2D images, a large portion of medical images are 3D. The lack of medical data further compounds these obstacles. To address these challenges, we present VividMed, a vision language model with versatile visual grounding for medicine. Our model supports generating both semantic segmentation masks and instance-level bounding boxes, and accommodates various imaging modalities, including both 2D and 3D data. We design a three-stage training procedure and an automatic data synthesis pipeline based on open datasets and models. Besides visual grounding tasks, VividMed also excels in other common downstream tasks, including Visual Question Answering (VQA) and report generation. Ablation studies empirically show that the integration of visual grounding ability leads to improved performance on these tasks. Our code is publicly available at https://github.com/function2-llx/MMMM.

Published
2024

2. CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction

Author

Han, Rong, Liu, Xiaohong, Pan, Tong, Xu, Jing, Wang, Xiaoyu, Lan, Wuyang, Li, Zhenyu, Wang, Zixuan, Song, Jiangning, Wang, Guangyu, and Chen, Ting

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tasks remains unexplored. In this paper, we propose CoPRA to bridge pre-trained language models from different biological domains via Complex structure for Protein-RNA binding Affinity prediction. We demonstrate for the first time that cross-biological modal language models can collaborate to improve binding affinity prediction. We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former's interaction understanding. Meanwhile, we build the largest protein-RNA binding affinity dataset PRA310 for performance evaluation. We also test our model on a public dataset for mutation effect prediction. CoPRA reaches state-of-the-art performance on all the datasets. We provide extensive analyses and verify that CoPRA can (1) accurately predict the protein-RNA binding affinity; (2) understand the binding affinity change caused by mutations; and (3) benefit from scaling data and model size.

Published
2024

3. FocusLLM: Scaling LLM's Context by Parallel Decoding

Author

Li, Zhenyu, Zhang, Yike, Pan, Tengyu, Sun, Yutao, Duan, Zhichao, Fang, Junjie, Han, Rong, Wang, Zixuan, and Wang, Jianyong

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Empowering LLMs with the ability to utilize useful information from a long context is crucial for many downstream applications. However, achieving long context lengths with the conventional transformer architecture requires substantial training and inference resources. In this paper, we present FocusLLM, a framework designed to extend the context length of any decoder-only LLM, enabling the model to focus on relevant information from very long sequences. FocusLLM processes long text inputs by dividing them into chunks based on the model's original context length to alleviate the issue of attention distraction. Then, it appends the local context to each chunk as a prompt to extract essential information from each chunk based on a novel parallel decoding mechanism, and ultimately integrates the extracted information into the local context. FocusLLM stands out for great training efficiency and versatility: trained with an 8K input length with much less training cost than previous methods, FocusLLM exhibits superior performance across downstream long-context tasks and maintains strong language modeling ability when handling extensive long texts, even up to 400K tokens. Our code is available at https://github.com/leezythu/FocusLLM.

Published
2024

4. Photonic realization of chiral hinge states in a Chern-insulator stack

Author

Xia, Han-Rong, Li, Jia-Zheng, Yuan, Si-Yu, and Xiao, Meng

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Higher-order topological insulators, as a novel family of topological phases, are a hot frontier in condensed matter physics due to their adherence to unconventional bulk-boundary correspondence. A three-dimensional second-order topological insulator can support one-dimensional modes along its hinges (dubbed as hinge states). Here, we present a simple and direct method to construct chiral hinge modes based on a Chern-insulator stack. We analyze the existence of the hinge modes through the nontrivial quadrupole indices, and then design a photonic crystal to realize the specific flowing pattern of the hinge mode in our model. The experimental results align well with full-wave simulations, clearly demonstrating the existence of chiral hinge states. We also verify the robustness of these hinge states against defects in our photonic system.

Published
2024

5. HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning

Author

Han, Rong, Huang, Wenbing, Luo, Lingxiao, Han, Xinyan, Shen, Jiaming, Zhang, Zhiqiang, Zhou, Jun, and Chen, Ting

Subjects
Computer Science - Machine Learning
Abstract

Understanding and leveraging the 3D structures of proteins is central to a variety of biological and drug discovery tasks. While deep learning has been applied successfully for structure-based protein function prediction tasks, current methods usually employ distinct training for each task. However, each of the tasks is of small size, and such a single-task strategy hinders the models' performance and generalization ability. As some labeled 3D protein datasets are biologically related, combining multi-source datasets for larger-scale multi-task learning is one way to overcome this problem. In this paper, we propose a neural network model to address multiple tasks jointly upon the input of 3D protein structures. In particular, we first construct a standard structure-based multi-task benchmark called Protein-MT, consisting of 6 biologically relevant tasks, including affinity prediction and property prediction, integrated from 4 public datasets. Then, we develop a novel graph neural network for multi-task learning, dubbed Heterogeneous Multichannel Equivariant Network (HeMeNet), which is E(3) equivariant and able to capture heterogeneous relationships between different atoms. Besides, HeMeNet can achieve task-specific learning via the task-aware readout mechanism. Extensive evaluations on our benchmark verify the effectiveness of multi-task learning, and our model generally surpasses state-of-the-art models.

Published
2024

6. DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance

Author

Wang, Zixuan, Jia, Jia, Sun, Shikun, Wu, Haozhe, Han, Rong, Li, Zhenyu, Tang, Di, Zhou, Jiaqing, and Luo, Jiebo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Choreographers determine what the dances look like, while cameramen determine the final presentation of dances. Recently, various methods and datasets have showcased the feasibility of dance synthesis. However, camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus, we present DCM, a new multi-modal 3D dataset, which for the first time combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community, covering 4 music genres. With this dataset, we uncover that dance camera movement is multifaceted and human-centric, and possesses multiple influencing factors, making dance camera synthesis a more challenging task compared to camera or dance synthesis alone. To overcome these difficulties, we propose DanceCamera3D, a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy. For evaluation, we devise new metrics measuring camera movement quality, diversity, and dancer fidelity. Utilizing these metrics, we conduct extensive experiments on our DCM dataset, providing both quantitative and qualitative evidence showcasing the effectiveness of our DanceCamera3D model. Code and video demos are available at https://github.com/Carmenw1203/DanceCamera3D-Official., Comment: Accept to CVPR 2024

Published
2024

7. Building Universal Foundation Models for Medical Image Analysis with Spatially Adaptive Networks

Author

Luo, Lingxiao, Chen, Xuanzhong, Tang, Bingda, Chen, Xinsheng, Han, Rong, Hu, Chengpeng, Li, Yujiang, and Chen, Ting

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advancements in foundation models, typically trained with self-supervised learning on large-scale and diverse datasets, have shown great potential in medical image analysis. However, due to the significant spatial heterogeneity of medical imaging data, current models must tailor specific structures for different datasets, making it challenging to leverage the abundant unlabeled data. In this work, we propose a universal foundation model for medical image analysis that processes images with heterogeneous spatial properties using a unified structure. To accomplish this, we propose spatially adaptive networks (SPAD-Nets), a family of networks that dynamically adjust the structures to adapt to the spatial properties of input images, to build such a universal foundation model. We pre-train a spatial adaptive visual tokenizer (SPAD-VT) and then a spatial adaptive Vision Transformer (SPAD-ViT) via masked image modeling (MIM) on 55 public medical image datasets. The pre-training data comprises over 9 million image slices, representing the largest, most comprehensive, and most diverse dataset to our knowledge for pre-training universal foundation models for medical image analysis. The experimental results on downstream medical image classification and segmentation tasks demonstrate the superior performance and label efficiency of our model. Our code is available at https://github.com/function2-llx/PUMIT.

Published
2023

13. Pain, Function and Trunk/Hip Flexibility Changes Immediately after Clinical Pilates Exercises in Young Adults with Mild Chronic Low Back Pain

Author

Li Yi Tan, Ryan Han Rong Teh, and Boon Chong Kwok

Subjects
lumbopelvic, hamstring flexibility, strength, disability, rehabilitation, physiotherapy, Biotechnology, TP248.13-248.65, Medicine
Abstract

Background: Pilates is among the best conservative management strategies for chronic low back pain. However, several variations of Pilates exist. This study aimed to investigate physical measures that would detect immediate changes after a brief session of Clinical Pilates exercises. Changes in self-reported clinical outcomes, pain and function were also evaluated. Methods: A prospective cohort study was conducted. Eighteen young adults with chronic low back pain participated in this study. Participants were assessed for pain and function subjectively, and hip/knee strength and trunk/hip flexibility objectively, followed by a session of Clinical Pilates assessment and exercises. After the exercises, the participants were immediately reassessed for pain, function, strength, and flexibility. Results: Trunk/hip flexibility showed statistically significant changes after exercise, which were measured with the sit-and-reach test (−3.44 cm, 95% CI [−5.10, −1.79], p < 0.001) and the finger-to-floor test (−6.29 cm [−9.51, −3.06], p = 0.001). Statistical significance was not found in detecting strength changes in hip extension, hip abduction, and knee extension. Changes in pain (1.56 points [0.83, 2.28], p < 0.001) and patient-specific functional scale (−1.52 points [−1.93, −1.10], p < 0.001) were also found after exercise. Conclusions: Trunk/hip flexibility measures detected physical changes after Clinical Pilates exercise, as well as self-reported pain and function outcomes, without reducing strength performance.

Published
2024
Full Text
View/download PDF

17. Effect of group cognitive behavioral therapy on clinical outcome of patients with persistent high-risk HPV infection

Author

LONG Xin, JIANG Chenggang, and HAN Rong

Subjects
human papillomavirus, cognitive behavioral therapy, cervix, reversion, Medicine (General), R5-920
Abstract

Objective To explore the effect of group cognitive behavioral therapy (CBT) on the clinical outcome of patients with persistent high-risk human papillomavirus (HR-HPV) infection, and to construct a systematic psychological intervention treatment program for persistent HR-HPV infection patients with emotional disorders. Methods A total of 96 patients with persistent HR-HPV infection complicated with emotional disorder admitted in our hospital from September 2021 to March 2022 were recruited, and then randomly divided into an intervention group (n=44) and a control group (n=45) after 5 of them were excluded due to personal reason. The control group received health education and follow-up monitoring, and the intervention group received group CBT intervention additionally. Their clinical outcomes and psychological scale scores were compared between the 2 groups. Results In a follow-up for 6 and 12 months, the regression rate of HR-HPV infection was higher, and the sustained infection rate and progression rate of HR-HPV in the intervention group than the control group (P < 0.05). The intervention group also obtained lower scores of Nine-Item Patient Health Questionnaire (PHQ-9) and generalized anxiety scale (GAD-7) immediately after the intervention, and 6 and 12 months in follow-up for when compared with the control group (P < 0.05). And the scores at above time points were statistically lower than those before intervention in the intervention group (P < 0.05), but no such differences were observed in the control group (P>0.05). Conclusion Systematic psychological intervention treatment is beneficial to HR-HPV infection subside for patients with persistent HR-HPV infection. [Key words] human papillomavirus , cognitive behavioral therapy , cervix, reversion ,

Published
2024
Full Text
View/download PDF

18. Feature Extraction, Modulation and Recognition of Mixed Signal Based on SVM

Author

Han, Rong and Lin, Zihuai

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper introduces likelihood-based and feature-based modulation recognition methods. In the feature-based modulation simulation part, instantaneous feature, cyclic spectrum, high-order cumulants, and wavelet transform features are used as the entry point, and six digital signals including 2ASK, 4ASK, BPSK, QPSK, 2FSK and 4FSK are simulated, showing the difference of signals in multiple dimensions

Published
2022

23. Protocol for a randomized controlled trial of intensive blood pressure control on cardiovascular risk reduction in patients with atrial fibrillation: Rationale and design of the CRAFT trial

Author

Jiang, Chao, Wang, Zhiyan, Du, Xin, Wang, Yufeng, Gao, Mingyang, Jia, Zhaoxu, Chai, Zhongyi, Yang, Zhiyun, Wang, Chi, He, Liu, Hu, Rong, Lv, Qiang, Wu, Jiahui, Li, Xu, Jia, Changqi, Han, Rong, Arima, Hisatomi, Wang, Xia, Neal, Bruce, Rodgers, Anthony, Hillis, Graham S., Patel, Anushka, Li, Qiang, Dong, Jianzeng, Anderson, Craig S., and Ma, Changsheng

Published
2024
Full Text
View/download PDF

36. Geminga SNR: Possible candidate of local cosmic-ray factory

Author

Zhao, Bing, Liu, Wei, Yuan, Qiang, Hu, Hong-Bo, Bi, Xiao-Jun, Wu, Han-Rong, Zhou, Xun-Xiu, and Guo, Yi-Qing

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

The precise measurements of energy spectra and anisotropy could help us uncover the local cosmic-ray accelerators. Our recent works have shown that spectral hardening above $200$ GeV in the energy spectra and transition of large-scale anisotropy at $\sim 100$ TeV are of local source origin. Less than $100$ TeV, both spectral hardening and anisotropy explicitly indicate the dominant contribution from nearby sources. In this work, we further investigate the parameter space of sources allowed by the observational energy spectra and anisotropy amplitude. To obtain the best-fit source parameters, a numerical package to compute the parameter posterior distributions based on Bayesian inference, which is applied to perform an elaborate scan of parameter space. We find that by combining the energy spectra and anisotropy data, the permissible range of location and age of local source is considerably reduced. When comparing with the current local SNR catalog, only Geminga SNR could be the proper candidate of the local cosmic-ray source., Comment: 9 pages, 9 figures, 2 tables

Published
2021
Full Text
View/download PDF

45. MAVEN: A Massive General Domain Event Detection Dataset

Author

Wang, Xiaozhi, Wang, Ziqi, Han, Xu, Jiang, Wangyi, Han, Rong, Liu, Zhiyuan, Li, Juanzi, Li, Peng, Lin, Yankai, and Zhou, Jie

Subjects
Computer Science - Computation and Language
Abstract

Event detection (ED), which means identifying event trigger words and classifying event types, is the first and most fundamental step for extracting event knowledge from plain text. Most existing datasets exhibit the following issues that limit further development of ED: (1) Data scarcity. Existing small-scale datasets are not sufficient for training and stably benchmarking increasingly sophisticated modern neural methods. (2) Low coverage. Limited event types of existing datasets cannot well cover general-domain events, which restricts the applications of ED models. To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 118,732 event mention instances, and 168 event types. MAVEN alleviates the data scarcity problem and covers much more general event types. We reproduce the recent state-of-the-art ED models and conduct a thorough evaluation on MAVEN. The experimental results show that existing ED methods cannot achieve promising results on MAVEN as on the small datasets, which suggests that ED in the real world remains a challenging task and requires further research efforts. We also discuss further directions for general domain ED with empirical analyses. The source code and dataset can be obtained from https://github.com/THU-KEG/MAVEN-dataset., Comment: Accepted at EMNLP 2020

Published
2020

47. Enhanced putamen functional connectivity underlies altered risky decision-making in age-related cognitive decline

Author

Ping Ren, Gangqiang Hou, Manxiu Ma, Yuchuan Zhuang, Jiayin Huang, Meiling Tan, Donghui Wu, Guozhi Luo, Zhiguo Zhang, and Han Rong

Subjects
Medicine, Science
Abstract

Abstract Risky decision-making is critical to survival and development, which has been compromised in elderly populations. However, the neural substrates of altered financial risk-taking behavior in aging are still under-investigated. Here we examined the intrinsic putamen network in modulating risk-taking behaviors of Balloon Analogue Risk Task in healthy young and older adults using resting-state fMRI. Compared with the young group, the elderly group showed significantly different task performance. Based on the task performance, older adults were further subdivided into two subgroups, showing young-like and over-conservative risk behaviors, regardless of cognitive decline. Compared with young adults, the intrinsic pattern of putamen connectivity was significantly different in over-conservative older adults, but not in young-like older adults. Notably, age-effects on risk behaviors were mediated via the putamen functional connectivity. In addition, the putamen gray matter volume showed significantly different relationships with risk behaviors and functional connectivity in over-conservative older adults. Our findings suggest that reward-based risky behaviors might be a sensitive indicator of brain aging, highlighting the critical role of the putamen network in maintaining optimal risky decision-making in age-related cognitive decline.

Published
2023
Full Text
View/download PDF

48. Upregulation of TMEM40 is associated with the malignant behavior and promotes tumor progression in cervical cancer

Author

Zhen-Fei Zhang, Fang Liu, Han-Rong Zhang, Bing Liu, Shu-Qian Zheng, Wan-Qian Ye, Jia-Nan Ding, Ze-Jie Zhou, Hui-Xian Luo, Fang Wu, Xuan-Min Guo, Jue-Yu Zhou, and Yong-Hui Guo

Subjects
TMEM40, Cervical cancer, Malignant phenotype, Tumor progression, p53 pathway, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Objective Recent studies indicated that transmembrane protein 40 (TMEM40) is associated with several types of cancers but is not clear in cervical cancer (CC). The study aimed to examine the role of TMEM40 in CC and related mechanisms. Methods The expression of TMEM40 in CC tissues and cell lines was studied with western blot and real-time quantitative RT-PCR. The effect of TMEM40 on proliferation was evaluated by CCK-8, EdU and colony formation assay. The migration, invasion, cell cycle and apoptosis of CC cells were studied with wound healing, transwell assays and flow cytometry. Tumor growth was evaluated in vivo using a xenogenous subcutaneously implant model. Results The results revealed that the TMEM40 elevation in CC tissues and cell lines was closely correlated with tumor size and lymph node metastasis in clinical patients. Upregulation of TMEM40 with OE-TMEM40 vector promoted the invasion, migration and proliferation, inhibited the apoptosis and led to distinct S cell cycle arrest in CC cell lines. Silencing TMEM40 with shRNA inhibited the invasion, migration and proliferation, promoted apoptosis and led to a G0/G1 cell cycle arrest in CC cell lines. Silence of TMEM40 downregulated the expression of c-MYC, Cyclin D1, matrix metalloproteinase-1 (MMP-1) and matrix metalloproteinase-9 (MMP-9), but in contrast, activated p53 and several apoptosis related proteins such as p53, Caspase-3, Caspase-9 and PARP1. In addition, TMEM40 silencing dramatically decreased tumor growth in mice models. Conclusion The present study demonstrates that TMEM40 upregulation can be a potential prognostic biomarker and contribute to CC development.

Published
2023
Full Text
View/download PDF

49. Effects of Phosphate on the Adsorption of Heavy Metal Ions onto TiO2 Nanoparticles in Water and Mechanism Analysis

Author

LI Yanxiang, CHEN Jiuyan, HAN Rong, LUAN Rijian, ZHANG Yuqiang, WU Laidong, and QI Zhichong

Subjects
nano-titanium dioxide (ntio2), heavy metal ions, inductively coupled plasma-optical emmission spectrometry, phosphate, anions, adsorption, Geology, QE1-996.5, Ecology, QH540-549.5
Abstract

BACKGROUND Nano-titanium dioxide (nTiO2) is widely used to remove heavy metals from water. Phosphate, a common inorganic anion in the aquatic environment, can affect the adsorption characteristics of heavy metal ions on nTiO2. However, the current state of knowledge on the influences of phosphate on the adsorption behaviors of heavy metal ions onto TiO2 nanoparticles (nTiO2) is inadequate. Herein, batch adsorption experiments were conducted to investigate the effects of phosphate on the adsorption of heavy metal ions (i.e., Zn2+ and Cd2+) onto suspended nTiO2. OBJECTIVES To elucidate the primary mechanisms controlling the adsorption behaviors of Zn2+ and Cd2+ on suspended nTiO2 in the presence of phosphate under different solution chemistry conditions. METHODS In order to determine the effects of phosphate on the adsorption of heavy metal ions onto nTiO2, batch experiments were conducted by mixing background electrolyte ions, nTiO2, and phosphate, which contained various concentrations of Zn2+ or Cd2+ in 20mL-amber glass vials at room temperature. The pH of solution was adjusted to target 7.0 using 0.1mol/L HCl or 0.1mol/L NaOH accordingly. Then, the mixtures were rotated end-over-end for 24h. After equilibration, the liquid and solid phases were separated by centrifugation at 15000r/min for 20min, and then the supernatants were filtered through 0.22μm pore-size cellulose ester membrane filter (the loss of metal ions can be neglected). The concentrations of Zn2+ or Cd2+ in the filtrate were measured by an inductively coupled plasma-optical emission spectrometry (ICP-OES). The adsorbed metal ions were then determined by the difference between the initial and final concentrations of metal ions in the aqueous phase. Furthermore, the classic Langmuir and Freundlich sorption models were used to correlate the adsorption isotherms. RESULTS (1) Adsorption isotherms showed that the presence of phosphate could enhance the adsorption of metal ions onto nTiO2, the maximum adsorption capacity of Zn2+ and Cd2+ increased from 121.1mg/g and 84.7mg/g to 588.3mg/g and 434.8mg/g, respectively. We propose that phosphate probably enhance the adsorption of Zn2+ and Cd2+ onto nTiO2 by the following mechanisms. Firstly, the ζ-potential of nTiO2 surface becomes more negative with the increase of phosphate concentration in aqueous phase. Consequently, the electrostatic attraction between negatively charged nanoparticles and positively charged metal ions generally increases with increasing phosphate content. Secondly, the phosphate added into nTiO2 suspension inhibits the aggregation of nanoparticles. In this case, more nTiO2 could sufficiently contact metal ions, thus increasing the adsorption sites. Thirdly, phosphate could form an inner-sphere surface complex on the nTiO2 surface, which can greatly influence the surface chemistry of nTiO2[55-57]. These products could strongly immobilize heavy metal ions[40-41]. These results might account for enhanced Zn2+ and Cd2+ adsorption on nTiO2 by forming metal-phosphate-surface ternary complexes in the presence of phosphate.(2) The adsorption of heavy metals onto nTiO2 decreased when concentration of NaCl increased from 0 to 10 mmol/L. It is likely that ionic strength can affect the attachment of nanoparticles via three major mechanisms. Firstly, the presence of competing cations (Na+) of salt reduces the adsorption of metal ions (i.e., Zn2+ and Cd2+) and this effect may have more significant roles with increasing Na+ concentration. Secondly, this could be related to the fact that an increase in ionic strength interferes with the electrostatic attraction between nTiO2 and metal ions. Thus, adsorption of metal ions is suppressed. Thirdly, increasing ionic strength significantly enhances aggregation of nTiO2, and consequently decreases the active surface sites of nTiO2.(3) The coexistence of competing anions (such as Cl-, NO3- and SO42-) weaken the enhancement effect of phosphate on the adsorption of metal ions onto nTiO2, and the order of inhibition is: SO42- >NO3->Cl-. This may be because anions with higher ionic radii (i.e., SO42-) may occupy more surface reactive sites. On the other hand, the competitive adsorption is related to the valence. It is well known that Cl- and NO3- are more likely to form "outer sphere" complexes with binding surfaces. Meanwhile, the electrostatic adsorption and the ion energy of monovalent anions (e.g., Cl- and NO3-) are weaker than those of divalent anions (e.g., SO42-). For this reason, the competitive influence of Cl- and NO3- during the adsorption of metal ions is negligible. In comparison, the divalent anion has a relatively stronger competitiveness on the adsorption of metal ions. CONCLUSIONS The research results show that phosphate can significantly enhance the removal efficiency of nTiO2 to heavy metal ions, but the removal efficiency will be affected by the water chemical conditions in the background solution. Previous studies show that nTiO2 is promising as an adsorbent for the removal of metal ions from aqueous solution. The present study demonstrates that phosphate plays an important role in adsorption of metal ions (e.g., Zn2+ and Cd2+) onto nTiO2. Phosphate significantly enhances adsorption of Zn2+ and Cd2+ onto nTiO2 by forming metal-phosphate-surface ternary complexes and increasing electrostatic attraction. The increase of ionic strength results in the low adsorption of metal ions in the presence of phosphate, resulting from electronic shielding of the negatively charged sites on the nTiO2 surface and competition between Na+ and heavy metal ions for active surface sites. Moreover, the addition of competitive anions inhibits the adsorption of metal ions in the presence of phosphate following the order of SO42->NO3->Cl-. This phenomenon is mainly ascribed to the decrease of phosphate adsorption because of competition between anions and phosphate for adsorption sites on nTiO2 surface, resulting in decreasing the amount of metal-phosphate-surface ternary complexes. Overall, the results obtained from this study indicate that the adsorption of metal ions onto nTiO2 varies greatly with factors such as phosphate, ionic strength, and competitive anions. Therefore, these factors should be well considered to better understand the fate and toxicity of metal ions in the adsorption process for the treatment of wastewater.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results