Extraction Performance Research Articles

Multimodal sentiment analysis based on multi-layer feature fusion and multi-task learning

Multimodal sentiment analysis (MSA) aims to use a variety of sensors to obtain and process information to predict the intensity and polarity of human emotions. The main challenges faced by current multi-modal sentiment analysis include: how the model extracts emotional information in a single modality and realizes the complementary transmission of multimodal information; how to output relatively stable predictions even when the sentiment embodied in a single modality is inconsistent with the multi-modal label; how can the model ensure high accuracy when a single modal information is incomplete or the feature extraction performance not good. Traditional methods do not take into account the interaction of unimodal contextual information and multi-modal information. They also ignore the independence and correlation of different modalities, which perform poorly when multimodal sentiment representations are asymmetric. To address these issues, this paper first proposes unimodal feature extraction network (UFEN) to extract unimodal features with stronger representation capabilities; then introduces multi-task fusion network (MTFN) to improve the correlation and fusion effect between multiple modalities. Multilayer feature extraction, attention mechanisms and Transformer are used in the model to mine potential relationships between features. Experimental results on MOSI, MOSEI and SIMS datasets show that the proposed method achieves better performance on multimodal sentiment analysis tasks compared with state-of-the-art baselines.

Applications of Nanomaterial Coatings in Solid-Phase Microextraction (SPME)

This review explores the advances in developing adsorbent materials for solid-phase microextraction (SPME), focusing on nanoparticles, nanocomposites, and nanoporous structures. Nanoparticles, including those of metals (e.g., gold, silver), metal oxides (e.g., TiO2, ZnO), and carbon-based materials (e.g., carbon nanotubes, graphene), offer enhanced surface area, improved extraction efficiency, and increased selectivity compared to traditional coatings. Nanocomposites, such as those combining metal oxides with polymers or carbon-based materials, exhibit synergistic properties, further improving extraction performance. Nanoporous materials, including metal–organic frameworks (MOFs) and ordered mesoporous carbons, provide high surface area and tunable pore structures, enabling selective adsorption of analytes. These advanced materials have been successfully applied to various analytes, including volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), pesticides, and heavy metals, demonstrating improved sensitivity, selectivity, and reproducibility compared to conventional SPME fibers. The incorporation of nanomaterials has significantly expanded the scope and applicability of SPME, enabling the analysis of trace-level analytes in complex matrices. This review highlights the significant potential of nanomaterials in revolutionizing SPME technology, offering new possibilities for sensitive and selective analysis in environmental monitoring, food safety, and other critical applications.

SS-DETR: a strong sensing DETR road obstacle detection model based on camera sensors for autonomous driving

Abstract As a key step in obstacle avoidance and path planning, obstacle detection via camera sensors is crucial for autonomous driving. The real traffic road environment is complex and variable, and the existing obstacle detection algorithms still have the problem of insufficient sensing ability. Therefore, this work suggests a camera sensors-based Strong Sensing DEtection TRansformer (SS-DETR) obstacle detection model for autonomous driving. Firstly, receptive-field attention ResNet is designed to improve feature analysis and extraction performance by considering the importance of receptive field spatial features and channels. Then, an intra-scale feature interaction module based on multiple information fusion attention is created to strengthen the representation of advanced feature maps. Furthermore, the cross-scale feature-fusion module is optimized to extract more detailed information from multi-scale feature maps. Finally, a localization loss function based on L1 and Powerful Intersection over Union v2 is implemented to further boost the detection performance. To verify the efficacy of the suggested model, the KITTI dataset containing camera sensors-based road obstacle images is adopted. The experimental results reveal that compared to real-time DETR, SS-DETR improves mean average precision (mAP)@50:95 and mAP@50 by 2.4% and 1.9%, respectively, and has a real-time inference speed of 33.7 frames per second. To further confirm the generalization ability of the approach, experiments are conducted on the camera sensors-based Cityscapes dataset. The results divulge that the suggested strategy can effectively raise the detection accuracy of obstacles, and offer a fresh perspective on obstacle identification.

AT4CTIRE: Adversarial Training for Cyber Threat Intelligence Relation Extraction

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Additionally, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation called Cyber Threat Intelligence Analysis (CTIA). Inspired by Generative Adversarial Networks, we integrate contextual semantics to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context-embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect in the cybersecurity domain. We also took general knowledge graph completion tests. The results demonstrate that AT4CTIRE outperforms existing methods with improved extraction accuracy and a remarkable expedited training convergence rate.

Experimental and molecular thermodynamics insights into separating bicyclic aromatics from diesel oil with ionic liquids

AbstractThe presence of aromatics in diesel oil significantly reduces its overall quality. Ionic liquids (ILs) combined with extraction technology were proposed to separate three typical bicyclic aromatics and two monocyclic aromatics from diesel oils. The solubility of IL in raffinate phase was introduced as a crucial factor in IL screening process. The accuracy of COSMO‐RS model was evaluated using 2942 LLE data points. 1,3‐dimethylimidazolium methylsulfate was considered a suitable extractive solvent. The optimal operating conditions were identified by varying the extraction temperatures, solvent ratios, and stages. Heptane was used as a back‐extractive solvent to recover IL; the regenerative and humidity stability of selected IL were proven. The separation mechanisms were explored through molecular dynamic simulation and quantum chemistry calculation. Cation plays a more critical role in the extraction process than anion. The extraction performance mainly depends on the π–π stacking and C–H···O H‐bond formed between BAHs molecules and IL.

A scientific-article key-insight extraction system based on multi-actor of fine-tuned open-source large language models

The exponential growth of scientific articles has presented challenges in information organization and extraction. Automation is urgently needed to streamline literature reviews and enhance insight extraction. We explore the potential of Large Language Models (LLMs) in key-insights extraction from scientific articles, including OpenAI’s GPT-4.0, MistralAI’s Mixtral 8 × 7B, 01AI’s Yi, and InternLM’s InternLM2. We have developed an article-level key-insight extraction system based on LLMs, calling it ArticleLLM. After evaluating the LLMs against manual benchmarks, we have enhanced their performance through fine-tuning. We propose a multi-actor LLM approach, merging the strengths of multiple fine-tuned LLMs to improve overall key-insight extraction performance. This work demonstrates not only the feasibility of LLMs in key-insight extraction, but also the effectiveness of cooperation of multiple fine-tuned LLMs, leading to efficient academic literature survey and knowledge discovery.

A Novel Ensemble Meta-Model for Enhanced Retinal Blood Vessel Segmentation Using Deep Learning Architectures

Background: Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. Methods: In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block. Each architecture is customized to enhance feature extraction and segmentation performance. The models are trained on the DRIVE and STARE datasets to improve the degree of generalization and evaluated using the performance metric accuracy, F1-Score, sensitivity, specificity, and AUC. Results: The ensemble meta-model integrates predictions from these architectures using a stacking approach, achieving state-of-the-art performance with an accuracy of 0.9778, an AUC of 0.9912, and an F1-Score of 0.8231. These results demonstrate the performance of the proposed technique in identifying thin retinal blood vessels. Conclusions: A comparative analysis using qualitative and quantitative results with individual models highlights the robustness of the ensemble framework, especially under conditions of noise and poor visibility.

Multiresidue Method for Quantification of Nitroimidazoles, Anthelmintics, Amphenicols, and Dyes in Meat, Shrimp, and Fish by Using a Modified QuEChERS-type Extraction and Isotope Dilution Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry

This paper presents an analytical multiresidue method for the quantification of 7 nitroimidazoles, 19 anthelmintics, 3 amphenicols, and 4 dyes in poultry, pork, beef, horse, shrimp, and fish samples by isotope dilution ultra-high performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) in a single 6 min run. Samples were prepared using a modified quick, easy, cheap, effective, rugged and safe (QuEChERS)-type extraction with ethyl acetate and Na2SO4-NaCl (2:1), concentration into dimethyl sulfoxide (DMSO), and defatting by liquid-liquid extraction (LLE) with n-hexane. No clean-up using solid-phase extraction (SPE) cartridges or dispersive solid-phase extraction (DSPE) was necessary. The linearity ranges (in μg kg−1) were 0.10-0.60 for chloramphenicol, 0.50-3.0 for dyes, 1.5-9.0 for nitroimidazoles, and 5.0-30.0 for the other drugs. The limits of quantitation (LOQ) were established at the lowest calibration curve levels. Except for chloramphenicol in the horse matrix, analyte recovery and reproducibility were satisfactory (recovery (fREC): 75.0 to 116.6%; coefficient of variation (CV): 1.2 to 29.9%). For the unapproved substances and the substances with a maximum residue level (MRL), respectively, the decision limits (CCα) were 0.14-4.10 and 5.28-1003.60 μg kg−1, while the detection capabilities (CCβ) were 0.13-3.33 and 5.46-11.56 μg kg−1.

Numerical study of asymmetric pitching amplitude effects on energy extraction performance of a semi-activated hydrofoil in shear flows

Numerical study of asymmetric pitching amplitude effects on energy extraction performance of a semi-activated hydrofoil in shear flows

Comparative analysis of thermal extraction performance of EGS model with N2O, H2O and CO2 as working fluids based on Voronoi fractures

Comparative analysis of thermal extraction performance of EGS model with N2O, H2O and CO2 as working fluids based on Voronoi fractures

Development of Natural Feature-Detection and -Matching Techniques for Monitoring Actual Retaining-Wall Displacements

The detection and monitoring of key points on a retaining-wall surface using a vision camera is crucial for measuring displacement. Researchers have previously proposed adding several artificial makers to a retaining wall to monitor displacement. However, the use of artificial markers presents several disadvantages. Additionally, vision-based displacement monitoring under field conditions are typically affected by problems such as illumination variation and strong wind-induced nonstationary cameras. Therefore, this study proposes a natural-target-based feature-matching method to extract natural features from retaining walls, thus enabling displacement calculation without artificial targets. Histogram equalization and homography algorithms are used to enhance the performance of the feature-matching algorithms and improve the accuracy of displacement calculation. An image dataset from a monitoring camera installed on an actual retaining wall at Seobu Mountain, Busan, Korea, is used to evaluate the performance of natural-feature extraction and displacement calculation. The results show that the feature-matching algorithm performed well in extracting and matching the natural features of the retaining wall compared with artificial markers. Additionally, the calculated horizontal displacement, settlement, and facing displacement of the actual retaining wall based on the single-camera and natural-feature-based methods indicate a maximum overall error of 3.8 mm.

Can all variations within the unified mask-based beamformer framework achieve identical peak extraction performance?

This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF; and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.

Enhancement of all-inorganic perovskite solar cell performance through the addition of CuI as a hole transport layer additive

Abstract The transport layer is one of the main factors affecting the stability and efficiency of all-inorganic perovskite solar cells (PSCs). It is still difficult to produce an HTL with the required properties using the present production methods. Based on the solubility, a new porous transport layer of CuI doped on the surface of inorganic perovskite is proposed. CuI inclusion promotes an energy level alignment that reduces ionic loss, inhibits charge carrier recombination, and improves hole extraction efficiency. CuI addition corrects surface imperfections of the perovskite and avoids defects caused by Spiro-OMeTAD pinholes, leading to excellent hole extraction performance and fast hole mobility rates. Due to this adjustment, power conversion efficiency (PCE) is improved by 26%, resulting in an optimized PCE of 12.39%. The filling factor and the short circuit current density (J sc) were increased to 17.93 mA cm−2 and 0.71, respectively. In addition, the stability of CuI is improved due to the barrier effect of inorganic Cul on air and water entering the perovskite layer. The results show that CuI doped hole transport layer film is a promising method to realize high performance and air-stable PSC.

New insights on the role of auxiliary information in target speaker extraction

Speaker extraction (SE) aims to isolate the speech of a target speaker from a mixture of interfering speakers with the help of auxiliary information. Several forms of auxiliary information have been employed in single-channel SE, such as a speech snippet enrolled from the target speaker or visual information corresponding to the spoken utterance. The effectiveness of the auxiliary information in SE is typically evaluated by comparing the extraction performance of SE with uninformed speaker separation (SS) methods. Following this evaluation procedure, many SE studies have reported performance improvement compared to SS, attributing this to the auxiliary information. However, recent advancements in deep neural network architectures, which have shown remarkable performance for SS, suggest an opportunity to revisit this conclusion. In this paper, we examine the role of auxiliary information in SE across multiple datasets and various input conditions. Specifically, we compare the performance of two SE systems (audio-based and video-based) with SS using a unified framework that utilizes the commonly used dual-path recurrent neural network architecture. Experimental evaluation on various datasets demonstrates that the use of auxiliary information in the considered SE systems does not always lead to better extraction performance compared to the uninformed SS system. Furthermore, we offer new insights into how SE systems select the target speaker by analyzing their behavior when provided with different and distorted auxiliary information given the same mixture input.

Numerical investigation on the influence of groundwater flow on long-term heat extraction performance of deep borehole heat exchanger array

Groundwater convection is commonly observed in real-world projects, particularly in coastal and groundwater-abundant regions. To accurately evaluate the heat extraction capacity of the deep borehole heat exchanger (DBHE) considering groundwater flow, a conduction–convection coupled numerical model of the DBHE is established by OpenGeoSys (OGS) software. Then, the variation of the DBHE circulation temperature and the heat extraction capacity affected by different groundwater conditions, including Darcy velocity, location of the aquifer, and porosity of the aquifer, are quantitatively analyzed. The results show that the porosity and location of the aquifer have a limited effect on the heat extraction capacity of the DBHE. With the given scenario in this study, when the Darcy velocity reaches more than 1×10-7m/s, it has a distinguishable effect on the heat extraction capacity of DBHE under the influence of groundwater. In addition, long-term simulations of multiple DBHEs considering the characteristics of the ground pipe network are performed in different directions of groundwater flow. The results indicate that groundwater flow can alleviate cold accumulation around the boreholes, and the thermal plume is pushed much towards the downstream direction. The cross-flow groundwater results in a higher circulation temperature than the parallel flow for the DBHE array. The maximum temperature difference between the two configurations is 1.98∘C, which occurs at the end of the 15th operating year based on the given parameters. The results of this study can be used as a guide for project engineers and decision-makers to accurately assess the heat extraction capacity of DBHE and strategize the layout of the DBHE array, taking into account the influence of groundwater flow.

Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data

ABSTRACT Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. However, complex forest stand structures due to dense canopy and abundant understory vegetation can seriously affect the extraction performance of tree parameters. This study selected Chinese fir plantations to examine how different forest stand structure conditions affected tree parameter extraction based on the integration of handheld laser scanning (HLS) and airborne laser scanning (ALS) data. This study also explored the ability of developing models using extracted tree height and crown width, or directly using the calculated variables from point clouds to estimate the diameter at breast height (DBH) and stem volume. The methods mainly consisted of two parts: (1) Selecting appropriate height to identify seed points for tree segmentation, and subsequently extracting tree parameters; (2) Taking tree height, crown width, and tree-level point cloud metrics as independent variables to establish regression and random forest models for estimating tree DBHs and stem volumes. Results showed that (1) tree DBHs were accurately extracted with relative root mean square error (RMSEr) of 7.1%−10.5% and stem volume with an RMSEr of 12.3%−16.8% under different understory conditions; (2) As stand structure became complex, direct extraction of tree DBHs from HLS data became a challenging task. However, developing a DBH estimation model using tree height and crown width proved feasible; (3) In the complex-understory condition, the volume estimation model utilizing point cloud variables achieved good performance with RMSEr of 28.0%. This research provides new insights for extracting tree parameters using HLS and ALS data, offering a potential replacement for field measurements of tree DBH, height, and stem volume for Chinese fir plantations.

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Edition (ICD-10), codes and therefore cannot be reliably extracted from granular fields like "Diagnosis" and "Problem List" entries, which complicates tasks that require identification of patients with these conditions, including clinical trial recruitment and research efforts. Recent advancements in large language models (LLMs) have shown promise in automating the extraction of medical information, offering the potential to improve medical research, diagnosis, and management. However, most LLMs lack professional medical knowledge, especially concerning specific rare diseases, and cannot effectively manage rare disease data in its various ontological forms, making it unsuitable for these tasks. Our aim is to create an end-to-end system called automated rare disease mining (AutoRD), which automates the extraction of rare disease-related information from medical text, focusing on entities and their relations to other medical concepts, such as signs and symptoms. AutoRD integrates up-to-date ontologies with other structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conducted various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods. AutoRD is a pipeline system that involves data preprocessing, entity extraction, relation extraction, entity calibration, and knowledge graph construction. We implemented this system using GPT-4 and medical knowledge graphs developed from the open-source Human Phenotype and Orphanet ontologies, using techniques such as chain-of-thought reasoning and prompt engineering. We quantitatively evaluated our system's performance in entity extraction, relation extraction, and knowledge graph construction. The experiment used the well-curated dataset RareDis2023, which contains medical literature focused on rare disease entities and their relations, making it an ideal dataset for training and testing our methodology. On the RareDis2023 dataset, AutoRD achieved an overall entity extraction F1-score of 56.1% and a relation extraction F1-score of 38.6%, marking a 14.4% improvement over the baseline LLM. Notably, the F1-score for rare disease entity extraction reached 83.5%, indicating high precision and recall in identifying rare disease mentions. These results demonstrate the effectiveness of integrating LLMs with medical ontologies in extracting complex rare disease information. AutoRD is an automated end-to-end system for extracting rare disease information from text to build knowledge graphs, addressing critical limitations of existing LLMs by improving identification of these diseases and connecting them to related clinical features. This work underscores the significant potential of LLMs in transforming health care, particularly in the rare disease domain. By leveraging ontology-enhanced LLMs, AutoRD constructs a robust medical knowledge base that incorporates up-to-date rare disease information, facilitating improved identification of patients and resulting in more inclusive research and trial candidacy efforts.

Theoretical Exploration of the Synergistic Effect of Phosphate and Amidoxime for Uranium Recovery from Seawater.

Highly selective extraction of uranium from seawater is currently extremely challenging. Although the amidoxime group (HAO) is the commonly used ligand in seawater uranium extraction, it also has strong binding capacity for vanadium ion. It has been shown that the introduction of phosphate groups into amidoxime-based adsorbents can improve the adsorption performance of materials through a synergistic effect between functional groups. In this work, we have systematically investigated the selective extraction behavior of the phosphate ligand (methylphosphonic acid, HL1) for uranyl cation and the synergistic effect with amidoxime using density functional theory (DFT). The electron-donor-substituted derivatives of HL1 (aminomethylphosphonic acid (HL2) and methyl phosphate (HL3)) were also considered. Not unexpectedly, the results show that introduction of HL1 into the amidoxime-based adsorbents improves the extraction and selectivity for uranyl cations. This is mainly due to the fact that HL1 is more likely to deprotonate, which promotes the dissociation of [UO2(CO3)3]4-, and the presence of the phosphate groups in the synergistic complexes alters the optimal coordination mode of VO2+. In addition, the HL2 and HL3 ligands further improve the uranium extraction performance, especially for HL3. This work provides guidelines for the rational design of sequestering ligands for uranium extraction from seawater.

Blue Energy Extraction Performance by Polyelectrolyte-Coated Porous Electrodes: Effects of Opposing Polyelectrolyte Interaction in Micropores.

In the capacitive mixing technique, the electrode used to extract blue energy is typically composed of a carbon-based porous electrode material. Polyelectrolyte (PE) surface coating on porous electrodes serves as an intermediate soft layer, which can significantly enhance the energy extraction performance (EEP). Herein, the blue energy extraction performance by using PE-coated electrodes is studied by a statistical thermodynamic theory, with the exploration of the interplay effects between opposing polyelectrolyte interactions and pore size. Because of the interaction between opposing PE coatings, the response of electrostatic properties in the pore to surface potential variations is enhanced during charging/discharging processes, leading to a better EEP. Both the surface charge accumulation and surface potential rise in the charging process can be raised as the results of PE coating. For the cases studied, the extraction efficiency can be promoted up to 45% compared with the cases of bare electrodes. For narrow pores, the PE promotion effects are suppressed by strong pore confinement. The optimal PE coating condition is determined by the competitive results between pore size (H) and polyelectrolyte chain length (N), with that Ĥ ≡ H/(2Nσ) being mostly in [0.1, 0.5], where σ is the PE segment diameter.

Extraction and Complexation of Fission Products with Unsymmetrical Phenanthroline Diamide Ligands from Simulated High-Level Liquid Waste.

The extraction performance and complexation properties between some typical fission products (FPs) with phenanthroline-based ligands N2,N,2N9-triethyl-N9-tolyl-1,10-phenanthroline-2,9-dicarboxamide (DE-ET-DAPhen) and N2-ethyl-N,9N9-dioctyl-N2-tolyl-1,10-phenanthroline-2,9-dicarboxamide (DO-ET-DAPhen) were described in this work. The low distributions of Ln(III) observed in the solvent extraction study showed the potential of the ligands for the separation of actinides and lanthanides in high-level liquid waste (HLLW). Further extraction studies on other FPs showed that the ligands could efficiently extract Pd(II) and Cd(II) using n-octanol as a diluent. Various methods including slope analysis, 1H NMR titration, UV-vis spectroscopic titration, and single-crystal X-ray diffraction were adopted to explore the complexation behavior of the ligands with target metal ions. The complexation study results indicated that the ligands formed 1:1 complexes with Ln(III) in single-phase solutions, while 1:1 and 2:1 complexes with Cd(II) and Pd(II) were detected. Meanwhile, the stability constants (log β) of the complexes measured through UV-vis spectroscopic titration were reported. According to the diffraction data of the single crystals, the complex structures of [Ln(DE-ET-DAPhen)(NO3)3] (Ln = La, Tb, and Lu) and [Cd2(DE-ET-DAPhen)2(NO3)4] were proposed. The systematic study of the extraction and interaction between some typical FPs with the ligands provides a comprehensive understanding of the ligands' properties, which contribute to the development of the application process of the ligands in the reprocessing of HLLW.