Backbone Framework Research Articles

Enhanced recognition of insulator defects on power transmission lines via proposal-based detection model with integrated improvement methods

Deep learning-driven transmission line inspection is a critical area for smart power grid development. Despite advances in deep learning for insulator defect detection, challenges remain in model robustness and adaptability for the varying real-world adaptability, especially for insignificant defects in complex backgrounds. This study presents a comprehensive improvement strategy for detecting insulators and cross-scale broken defects on transmission lines, employing a proposal-based detection model. The model introduces a holistic pipeline of improved methods, including backbone modification, anchor box scale recalibration, and improvements in Region of Interest (RoI) downsampling alignment and Intersection over Union (IoU) loss function. Various backbone networks, including convolutional network (ConvNet) and Vision Transformer (ViT) structures, are constructed and integrated with attention modules, specifically designed to amplify the perception of insulators and defective regions. The geometric scale of anchor boxes is reconstructed using a developed clustering algorithm, considering the elongated characteristics of insulator strings to improve the adaptability of anchor boxes. Bilinear interpolation is utilized to mitigate spatial misalignment issues during the downsampling process of Region Proposal Network (RPN)-based proposals. The experimental results indicate that the improved models with the Swin Transformer (Swin-T) backbone framework achieve the mean Average Precision (mAP)@0.5 of 88.42% and mAP@0.7 of 60.52%, with a defect recall rate of 81.94%. Additionally, the improved IoU loss function contributes to the performance of the model at higher IoU thresholds. The results of this study contribute to the further development of defect detection frameworks for power vision applications.

3MTox: A motif-level graph-based multi-view chemical language model for toxicity identification with deep interpretation

Toxicity identification plays a key role in maintaining human health, as it can alert humans to the potential hazards caused by long-term exposure to a wide variety of chemical compounds. Experimental methods for determining toxicity are time-consuming, and costly, while computational methods offer an alternative for the early identification of toxicity. For example, some classical ML and DL methods, which demonstrate excellent performance in toxicity prediction. However, these methods also have some defects, such as over-reliance on artificial features and easy overfitting, etc. Proposing novel models with superior prediction performance is still an urgent task. In this study, we propose a motifs-level graph-based multi-view pretraining language model, called 3MTox, for toxicity identification. The 3MTox model uses Bidirectional Encoder Representations from Transformers (BERT) as the backbone framework, and a motif graph as input. The results of extensive experiments showed that our 3MTox model achieved state-of-the-art performance on toxicity benchmark datasets and outperformed the baseline models considered. In addition, the interpretability of the model ensures that the it can quickly and accurately identify toxicity sites in a given molecule, thereby contributing to the determination of the status of toxicity and associated analyses. We think that the 3MTox model is among the most promising tools that are currently available for toxicity identification.

DBHF: A double fusion backbone and bidirectional feature hybrid fusion detector for high-precision inspection of avionics solder joint defects

The quality of avionics thermistor wire solder joints has a critical impact on the performance of in-orbit spacecraft. However, it is still hard to simultaneously realize a high-precision inspection of the multi-scale internal and external defects of avionics solder joints. Herein, in combination with active infrared thermography technique, an innovative DBHF detector with a double fusion backbone and a learnable weighted multi-scale bidirectional feature hybrid fusion pyramid network (BdFPN) is demonstrated based on RT-DETR framework. Specifically, the double fusion backbone framework comprises with two lightweight convolutional neural network-based backbones. Compared with conventional single backbone, the double-backbone design forms a multi-level Resnet-like structure, helping to enrich the extracted multi-dimension feature information and retain rich detail information of small-scale defects. The complex learnable weighted multi-scale BdFPN is designed for cross fusion of deep features. One or more additional multi-scale inputs are added to each node involved in feature fusion to aggregate more features from multiple scales and strengthen the interaction between low-level details and high-level semantic information, enabling the detector to make full use of multi-level feature information. Meanwhile, a learnable weight coefficient is set on each node involved in feature fusion and the importance of the input to each node is estimated by training, so that the effective information can be deeply aggregated at different levels. Our DBHF detector achieves a 92.7 % mAP@0.5 on a self-made avionics solder joint dataset with a body size of only 27.2 MB and FPS of 149, outperforming all the typical advanced real-time detectors.

Exploration of commercial cyclen-based chelators for mercury-197m/g incorporation into theranostic radiopharmaceuticals.

A comprehensive investigation of the Hg2+ coordination chemistry and 197m/gHg radiolabeling capabilities of cyclen-based commercial chelators, namely, DOTA and DOTAM (aka TCMC), along with their bifunctional counterparts, p-SCN-Bn-DOTA and p-SCN-Bn-TCMC, was conducted to assess the suitability of these frameworks as bifunctional chelators for the 197m/gHg2+ theranostic pair. Radiolabeling studies revealed that TCMC and DOTA exhibited low radiochemical yields (0%-6%), even when subjected to harsh conditions (80°C) and high ligand concentrations (10-4M). In contrast, p-SCN-Bn-TCMC and p-SCN-Bn-DOTA demonstrated significantly higher 197m/gHg radiochemical yields (100% ± 0.0% and 70.9% ± 1.1%, respectively) under the same conditions. The [197m/gHg]Hg-p-SCN-Bn-TCMC complex was kinetically inert when challenged against human serum and glutathione. To understand the differences in labeling between the commercial chelators and their bifunctional counterparts, non-radioactive natHg2+ complexes were assessed using NMR spectroscopy and DFT calculations. The NMR spectra of Hg-TCMC and Hg-p-SCN-Bn-TCMC suggested binding of the Hg2+ ion through the cyclen backbone framework. DFT studies indicated that binding of the Hg2+ ion within the backbone forms a thermodynamically stable product. However, competition can form between isothiocyanate binding and binding through the macrocycle, which was experimentally observed. The isothiocyanate bound coordination product was dominant at the radiochemical scale as, in comparison, the macrocycle bound product was seen at the NMR scale, agreeing with the DFT result. Furthermore, a bioconjugate of TCMC (TCMC-PSMA) targeting prostate-specific membrane antigen was synthesized and radiolabeled, resulting in an apparent molar activity of 0.089 MBq/nmol. However, the complex demonstrated significant degradation over 24h when exposed to human serum and glutathione. Subsequently, cell binding assays were conducted, revealing a Ki value ranging from 19.0 to 19.6nM. This research provides crucial insight into the effectiveness of current commercial chelators in the context of 197m/gHg2+ radiolabeling. It underscores the necessity for the development of specific and customized chelators to these unique "soft" radiometals to advance 197m/gHg2+ radiopharmaceuticals.

Boosting Semantic Segmentation by Conditioning the Backbone with Semantic Boundaries.

In this paper, we propose the Semantic-Boundary-Conditioned Backbone (SBCB) framework, an effective approach to enhancing semantic segmentation performance, particularly around mask boundaries, while maintaining compatibility with various segmentation architectures. Our objective is to improve existing models by leveraging semantic boundary information as an auxiliary task. The SBCB framework incorporates a complementary semantic boundary detection (SBD) task with a multi-task learning approach. It enhances the segmentation backbone without introducing additional parameters during inference or relying on independent post-processing modules. The SBD head utilizes multi-scale features from the backbone, learning low-level features in early stages and understanding high-level semantics in later stages. This complements common semantic segmentation architectures, where features from later stages are used for classification. Extensive evaluations using popular segmentation heads and backbones demonstrate the effectiveness of the SBCB. It leads to an average improvement of 1.2% in IoU and a 2.6% gain in the boundary F-score on the Cityscapes dataset. The SBCB framework also improves over- and under-segmentation characteristics. Furthermore, the SBCB adapts well to customized backbones and emerging vision transformer models, consistently achieving superior performance. In summary, the SBCB framework significantly boosts segmentation performance, especially around boundaries, without introducing complexity to the models. Leveraging the SBD task as an auxiliary objective, our approach demonstrates consistent improvements on various benchmarks, confirming its potential for advancing the field of semantic segmentation.

A Deep Neural Network-Based Intelligent Detection Model for Manufacturing Defects of Automobile Parts

Image defect detection of casting parts is a key part of the production process in the machinery manufacturing industry. The traditional methods are ineffective because traditional computer image processing methods require a large number of manual features to be set artificially, and the detection time is too long. In order to save human resources and improve the efficiency of image defect detection, this paper proposes a deep learning-based defect detection method for automobile parts. This paper selects EfficientNetB0 as the backbone framework of the target detection network, which significantly reduces the memory usage of the model and shortens the model inference time, while improving the model detection accuracy. Facing the problem of small samples of defect image dataset, we analyze the image characteristics of the dataset and introduce shape transformation and scale scaling as the basic online data enhancement method according to the industrial field image projection law. Then, it is expected to combine the traditional image processing algorithms according to the characteristics of casting parts with different depth distribution and multiple morphological changes, and develop a special image defect data enhancement method. This further improves the performance of the model and increases the detection accuracy of the algorithm by 22.3% without increasing the data.

An Improved YOLOv5-Based Underwater Object-Detection Framework

To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

Surgical Gesture Recognition in Laparoscopic Tasks Based on the Transformer Network and Self-Supervised Learning.

In this study, we propose a deep learning framework and a self-supervision scheme for video-based surgical gesture recognition. The proposed framework is modular. First, a 3D convolutional network extracts feature vectors from video clips for encoding spatial and short-term temporal features. Second, the feature vectors are fed into a transformer network for capturing long-term temporal dependencies. Two main models are proposed, based on the backbone framework: C3DTrans (supervised) and SSC3DTrans (self-supervised). The dataset consisted of 80 videos from two basic laparoscopic tasks: peg transfer (PT) and knot tying (KT). To examine the potential of self-supervision, the models were trained on 60% and 100% of the annotated dataset. In addition, the best-performing model was evaluated on the JIGSAWS robotic surgery dataset. The best model (C3DTrans) achieves an accuracy of 88.0%, a 95.2% clip level, and 97.5% and 97.9% (gesture level), for PT and KT, respectively. The SSC3DTrans performed similar to C3DTrans when training on 60% of the annotated dataset (about 84% and 93% clip-level accuracies for PT and KT, respectively). The performance of C3DTrans on JIGSAWS was close to 76% accuracy, which was similar to or higher than prior techniques based on a single video stream, no additional video training, and online processing.

Self‐Intercalated Magnetic Heterostructures in 2D Chromium Telluride

Emerging 2D magnetic heterojunctions attract substantial interest due to their potential applications in spintronics. Achieving magnetic phase engineering with structural integrity in 2D heterojunctions is of paramount importance for their magnetism manipulation. Herein, starting with chromium ditelluride (CrTe2) as the backbone framework, various lateral and vertical magnetic heterojunctions are obtained via self‐intercalated 2D chromium telluride (CrxTey). A Cr2Te3‐Cr5Te8 lateral heterojunction prototype is demonstrated for the manipulation of magnetic moments under different magnitudes of magnetic excitation, showing a sharply stepped hysteresis loop with a dual spin‐flip transition at high Curie temperatures up to 150 and 210 K by magneto‐optical Kerr measurement. High‐resolution scanning transmission electron microscopy and first‐principles calculations reveal a preferred random location of Cr intercalants at the phase boundary, allowing lowering energy associated with crystal field splitting. The overall structural rigidity of chromium‐telluride heterostructure with magnetic phase decoupled behaviors is promising for 2D spintronic devices.

三元AGa5S8 (A = K, Rb, Cs): 通过“一替多取代”策略制备的红外非线性光学材料

Commercially available infrared (IR) nonlinear optical (NLO) materials, such as diamond-like AgGaQ2 (Q = S, Se), display large NLO coefficients but relatively low laser-induced damage thresholds (LIDTs), which seriously hinder their widespread laser applications. Herein, the “one-for-multiple substitution” strategy, namely, [SZn46+ + 5Zn2+ ⇒ A+ + 5Ga3+] (A = K, Rb and Cs), is applied on the diamond-like zinc-blende ZnS, and affords three new polar ternary crystals AGa5S8 (A = K, Rb and Cs) through the solid-state method. These compounds inherit the diamond-like anionic backbone framework in ZnS where the NLO functional motifs GaS4 are arranged in a parallel manner. This characteristic accounts for the remarkable phase-matchable second-harmonic generation intensities (1.1–1.2 × AgGaS2). In addition, the inclusion of the highly electropositive A+ cations affords a band gap ranging from 3.10 to 3.37 eV, which facilitates the improvement of LIDT (9.3–12.4 × AgGaS2). To the best our knowledge, crystals AGa5S8 (A = K, Rb and Cs) are the first series of ternary A-inclusion chalcogenides with large second-harmonic generation responses (≥1.0 × AgGaS2) and wide band gaps (≥3.0 eV), fulfilling the rigorous requirements of outstanding IR NLO materials. In addition, the “one-for-multiple substitution” strategy presents the great significance of diamondlike structure evolution and provides a remarkable opportunity to achieve NLO materials.

Palladium, iridium, and rhodium complexes bearing chiral N‐heterocyclic carbene ligands applied in asymmetric catalysis

The field of asymmetric catalysis is rapidly developing and the chiral ligands play a key role in enantioselective transition‐metal catalysis. The electron‐rich chiral N‐heterocyclic carbenes (NHCs) have established themselves as a popular class of stereodirecting ancillary ligands to catalyze enantioselective organic transformations in more efficient ways. Several novel transition‐metal complexes in combination with tailored ligand design have emerged during last few decades in asymmetric catalysis. The tailor‐made NHCs can easily be accessed due to the modular synthesis of their parent azolium salt precursors. Their donor capability and the molecular shape can easily be tuned by changing substituent at N‐atom or by changing the cyclic backbone framework. This review article aims to describe the recent advances in this rapidly evolving research area of enantioselective catalysis using well‐defined transition‐metal complexes possessing chiral NHC donor ligands.

Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder

Community detection in attributed graph is of great application value and many related methods have been continually presented. However, existing methods for community detection in attributed graph still cannot well solve three key problems simultaneously: link information and attribute information fusion, prior information integration and overlapping community detection. Aiming at these problems, in this paper we devise a semi-supervised overlapping community detection method named SSGCAE which is based on graph neural networks. This method is composed of three modules: graph convolutional autoencoder (GCAE), semi-supervision and modularity maximization, which are respectively utilized to fuse link information and attribute information, integrate prior information and detect overlapping communities. We treat GCAE as the backbone framework and train it by using the unified loss from these three modules. Through this way, these three modules are jointly correlated via the community membership representation, which is very beneficial to improve the overall performance. SSGCAE is comprehensively evaluated on synthetic and real attributed graphs, and experiment results show that it is very effective and outperforms state-of-the-art baseline approaches.

An attention involved network stacked by dual-channel residual block for hyperspectral image classification

To achieve high-precision hyperspectral image (HSI) classification with less parameters under limited training samples. A lightweight convolutional neural network (CNN) based backbone framework is constructed by stacking a dual-channel residual block multiple times. The design of the stackable block combines the two mechanisms including dual-channel feature extraction, and residual connection. The dual-channel consists the main part of the block, in which 3D convolutions with (X × X × 1), (1 × 1 × X) kernel sizes are adopted to parallel extract spatial and spectral features. Kernel size X can be flexibly adjusted with different values to analyze features at multiple scales, due to the multi-scale extraction strategy. Additionally, a simple residual connection is applied in the block to alleviate the gradient vanishing problem, and is then replaced by an improved residual block to further enhance network performance. When stacking the block to construct the network backbone, a cross-channel interaction based multi-scale channel attention module is designed and adopted in each interconnection to further enhance the extracted features. Experimental results on three HSI datasets show that the proposed network can achieve state-of-the-art performance with less parameters compared to several other modern networks.

CBNet: A Composite Backbone Network Architecture for Object Detection.

top-performing object detectors depend heavily on backbone networks, whose advances bring consistent performance gains through exploring more effective network structures. In this paper, we propose a novel and flexible backbone framework, namely CBNet, to construct high-performance detectors using existing open-source pre-trained backbones under the pre-training fine-tuning paradigm. In particular, CBNet architecture groups multiple identical backbones, which are connected through composite connections. Specifically, it integrates the high- and low-level features of multiple identical backbone networks and gradually expands the receptive field to more effectively perform object detection. We also propose a better training strategy with auxiliary supervision for CBNet-based detectors. CBNet has strong generalization capabilities for different backbones and head designs of the detector architecture. Without additional pre-training of the composite backbone, CBNet can be adapted to various backbones (i.e., CNN-based vs. Transformer-based) and head designs of most mainstream detectors (i.e., one-stage vs. two-stage, anchor-based vs. anchor-free-based). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNet introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks. Particularly, our CB-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO test-dev under the single-model and single-scale testing protocol, which are significantly better than the state-of-the-art results (i.e., 57.7% box AP and 50.2% mask AP) achieved by Swin-L, while reducing the training time by 6×. With multi-scale testing, we push the current best single model result to a new record of 60.1% box AP and 52.3% mask AP without using extra training data. Code is available at https://github.com/VDIGPKU/CBNetV2.

6G Driven Fast Computational Networking Framework for Healthcare Applications

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone framework to deliberate the time-intensive services. This paper proposes a reference layered communication framework for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

Rotation-Invariant and Relation-Aware Cross-Domain Adaptation Object Detection Network for Optical Remote Sensing Images

In recent years, object detection has shown excellent results on a large number of annotated data, but when there is a discrepancy between the annotated data and the real test data, the performance of the trained object detection model is often degraded when it is directly transferred to the real test dataset. Compared with natural images, remote sensing images have great differences in appearance and quality. Traditional methods need to re-label all image data before interpretation, which will consume a lot of manpower and time. Therefore, it is of practical significance to study the Cross-Domain Adaptation Object Detection (CDAOD) of remote sensing images. To solve the above problems, our paper proposes a Rotation-Invariant and Relation-Aware (RIRA) CDAOD network. We trained the network at the image-level and the prototype-level based on a relation aware graph to align the feature distribution and added the rotation-invariant regularizer to deal with the rotation diversity. The Faster R-CNN network was adopted as the backbone framework of the network. We conducted experiments on two typical remote sensing building detection datasets, and set three domain adaptation scenarios: WHU 2012 → WHU 2016, Inria (Chicago) → Inria (Austin), and WHU 2012 → Inria (Austin). The results show that our method can effectively improve the detection effect in the target domain, and outperform competing methods by obtaining optimal results in all three scenarios.

An efficient framework for generating robust adversarial examples

Recent studies show that deep neural networks (DNNs) suffer adversarial examples. That is, attackers can mislead the output of a DNN by adding subtle perturbation to a benign input image. In addition, researchers propose new generation of technologies to produce robust adversarial examples. Robust adversarial examples can consistently fool DNN models under predefined hyperparameter space, which can break through some defenses against adversarial examples or even generate physical adversarial examples against real-world applications. Behind these achievements, expectation over transformation (EOT) algorithm plays as the backbone framework for generating robust adversarial examples. Though EOT framework is powerful, we know little about why such a framework can generate robust adversarial examples. To address this issue, we do the first work to explain the principle behind robust adversarial examples. Then, based on the findings, we point out that traditional EOT framework has a performance problem and propose an adaptive sampling algorithm to overcome such a problem. By modeling the sampling process as classic Coupon Collector Problem, we prove that our new framework reduces the cost from O ( n ∗ log ⁡ ( n ) ) to O ( n ), where n denotes the number of sampling points. Under the view of computational complexity, the algorithm is optimal for this problem. The experimental results show that our algorithm can save up to 23% overhead in average. This is significant for black-box attack, where the cost is charged by the amount of queries.

A Frequency-Domain Convolutional Neural Network Architecture Based on the Frequency-Domain Randomized Offset Rectified Linear Unit and Frequency-Domain Chunk Max Pooling Method

It is of great importance to construct a convolutional neural network architecture in the frequency domain to explore the theory of deep learning in the frequency domain. However, due to the complexity of the construction mechanism of the forward and backward pipelines needed to train the convolutional neural network in the frequency domain, higher requirements are put forward for the representation strategy of the frequency-domain activation function and pooling method in the forward and backward pipelines. Therefore, to construct a full frequency-domain convolutional neural network architecture, it is necessary to construct a frequency-domain representation strategy with a high classification accuracy and excellent time performance. In this paper, based on a chunk decomposition mechanism and the construction principle of the frequency-domain unsaturated activation function, a frequency-domain convolutional neural network architecture is proposed. Two important representation strategies are introduced into the frequency-domain forward/backward pipeline: a frequency-domain randomized offset rectified linear unit and a frequency-domain chunk max pooling method. The former can alleviate the vanishing and exploding gradient phenomena in the frequency-domain forward/backward pipeline and ensure the convergence of the convolutional neural network architecture in the frequency-domain training stage; the latter can capture the partial location information and characteristic strength of the frequency-domain neurons and improve the classification performance of the convolutional neural network in the frequency domain. This full frequency-domain convolutional neural network architecture improves the training accuracy of the convolutional neural network in the frequency-domain pipeline. The results show that on the basis of ResNet-50 as the backbone framework, an NVIDIA GeForce CUDA(Compute Unified Device Architecture) as the training pipeline, and $4\times 4$ as the activation block size of the third-level output neuron's characteristic parameter matrix, the convolutional neural network architecture proposed in this paper can lower the top-1 error from 24.90% to 17.95%, the top-5 error from 12.85% to 9.23%. Furthermore, when the batch size is equal to 128 (in the worst-case bandwidth usage scenario), the acceleration ratio of the proposed architecture can still reach 13.0375 by selecting cuDNN as the reference model. Under the same backbone framework, the proposed architecture is tested on the MetData-1 dataset, and the classification accuracy can reach the maximum value; that is, the average difference is merely 0.18. This finding shows that the proposed architecture can improve the accuracy of the deep learning-based frequency-domain convolutional neural network model without reducing the time performance and expand the frequency-domain representation strategy of the frequency-domain activation function and pooling method.

Synthesis of 6,13-dihydro-6,13-ethanopentacene-15,16-diimine palladium(II) complex and steric effect of the backbone polycyclic aromatic system on palladium-catalyzed olefin polymerization

The synthesis and characterization of a novel 6,13-dihydro-6,13-ethanopentacene-15,16-diimine palladium(II) complex are described. The effect of the backbone framework of the α-diimine metal catalyst on the polymerization reaction of norbornene and 1-octene is investigated. Compared to the 9,10-dihydro-9,10-ethanoanthracene-11,12-diimine palladium(II) complex (C1), the 6,13-dihydro-6,13-ethanopentacene-15,16-diimine palladium(II) complex (C2) with a polycyclic aromatic system on the backbone exhibits relatively high activities and leads to high molecular weights as well as narrow molecular weight distributions depending on the variation in feed ratios for copolymerization of norbornene and 1-octene. Moreover, the copolymers obtained using the 6,13-dihydro-6,13-ethanopentacene-15,16-diimine palladium(II) complex (C2) with large steric hindrance show high thermal stability and high glass transition temperatures.

The stability of group 10 metal POCOP pincer complexes: decomposition/reconstruction pathways of the pincer backbone.

Organometallic chemists usually like to use the word robust to describe pincer ligand frameworks in metal pincer complexes. Although most transition metal pincer complexes are thermally stable, the pincer backbone frameworks can still decompose under certain circumstances. In order to explore the stability of the bis(phosphinite) (POCOP) pincer backbone in transition metal pincer complexes, group 10 metal POCOP pincer complexes were exposed to different nucleophilic and electrophilic conditions, respectively. It was found that the POCOP pincer backbone is stable under intermolecular nucleophilic conditions but cannot survive intramolecular nucleophilic attack; the POCOP pincer backbone is also stable under weak electrophilic conditions but the backbone can be completely destroyed by strong Lewis acids such as AlCl3. Possible decomposition/reconstruction pathways of the POCOP pincer ligand framework were proposed.