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In recent years, the synthesis of axially chiral compounds has received considerable attention due to their extensive application as biologically active compounds in medicinal chemistry and as chiral ligands in asymmetric catalysis. Chiral phosphoric acids are recognized as efficient organocatalysts for a variety of enantioselective transformations. In this review, we summarize the recent development of chiral phosphoric acid-catalyzed synthesis of a wide range of axially chiral biaryls, heterobiaryls, vinylarenes, N -arylamines, spiranes, and allenes with high efficiency and excellent stereoselectivity.

Planar chiral bisphosphine ligands based on diphenyl [2.2]paracyclophane (PhPhanePHOS) were successfully synthesized in a practical manner in four steps from commercially available 4,12-bisbromo-[2.2]paracyclophane as a new family of bisphosphine ligands. The novel PhPhanePHOS ligands provide high catalytic activity in Pd-catalyzed asymmetric allylic alkylation reactions in preliminary experiments.

Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been limited in detecting small smoke and smoke with smoke-like interference. In this paper, we propose a lightweight model for forest fire smoke detection that is suitable for UAVs. Firstly, a smoke dataset is created from a combination of forest smoke photos obtained through web crawling and enhanced photos generated by using the method of synthesizing smoke. Secondly, the GSELAN and GSSPPFCSPC modules are built based on Ghost Shuffle Convolution (GSConv), which efficiently reduces the number of parameters in the model and accelerates its convergence speed. Next, to address the problem of indistinguishable feature boundaries between clouds and smoke, we integrate coordinate attention (CA) into the YOLO feature extraction network to strengthen the extraction of smoke features and attenuate the background information. Additionally, we use Content-Aware Reassembly of FEatures (CARAFE) upsampling to expand the receptive field in the feature fusion network and fully exploit the semantic information. Finally, we adopt SCYLLA-Intersection over Union (SIoU) loss as a replacement for the original loss function in the prediction phase. This substitution leads to improved convergence efficiency and faster convergence. The experimental results demonstrate that the LMDFS model proposed for smoke detection achieves an accuracy of 80.2% with a 5.9% improvement compared to the baseline and a high number of Frames Per Second (FPS)—63.4. The model also reduces the parameter count by 14% and Giga FLoating-point Operations Per second (GFLOPs) by 6%. These results suggest that the proposed model can achieve a high accuracy while requiring fewer computational resources, making it a promising approach for practical deployment in applications for detecting smoke.

Concerning the ever-changing wetland environment, the efficient extraction of wetland information holds great significance for the research and management of wetland ecosystems. China’s vast coastal wetlands possess rich and diverse geographical features. This study employs the SegFormer model and Sentinel-2 data to conduct a wetland classification study for coastal wetlands in Yancheng, Jiangsu, China. After preprocessing the Sentinel data, nine classification objects (construction land, Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), Phragmites australis (P. australis), farmland, river system, aquaculture and tidal falt) were identified based on the previous literature and remote sensing images. Moreover, mAcc, mIoU, aAcc, Precision, Recall and F-1 score were chosen as evaluation indicators. This study explores the potential and effectiveness of multiple methods, including data image processing, machine learning and deep learning. The results indicate that SegFormer is the best model for wetland classification, efficiently and accurately extracting small-scale features. With mIoU (0.81), mAcc (0.87), aAcc (0.94), mPrecision (0.901), mRecall (0.876) and mFscore (0.887) higher than other models. In the face of unbalanced wetland categories, combining CrossEntropyLoss and FocalLoss in the loss function can improve several indicators of difficult cases to be segmented, enhancing the classification accuracy and generalization ability of the model. Finally, the category scale pie chart of Yancheng Binhai wetlands was plotted. In conclusion, this study achieves an effective segmentation of Yancheng coastal wetlands based on the semantic segmentation method of deep learning, providing technical support and reference value for subsequent research on wetland values.

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

Support effect is an important issue in heterogeneous catalysis, while the explicit role of a catalytic support is often unclear for catalytic reactions. A systematic density functional theory computational study is reported in this paper to elucidate the effect of a model boron nitride (BN) support on the first N-H bond activation step of NH3 on Run (n = 1, 2, 3) metal clusters. Geometry optimizations and energy calculations were carried out using density functional theory (DFT) calculation for intermediates and transition states from the starting materials undergoing the N-H activation process. The primary findings are summarized as follows. The involvement of the model BN support does not significantly alter the equilibrium structure of intermediates and transition states in the most favorable pathway (MFP). Moreover, the involvement of BN support decreases the free energy of activation, ΔG≠, thus improving the reaction rate constant. This improvement is more obvious at high temperatures like 673 K than low temperatures like 298 K. The BN support effect leading to the ΔG≠ decrease is most significant for the single Ru atom case among all three cases studied. Finally, the involvement of the model BN may change the spin transition behavior of the reaction system during the N-H bond activation process. All these findings provide a deeper insight into the support effect on the N-H bond activation of NH3 for the supported Ru catalyst in particular and for supported transition metal catalysts in general.

Hindered phenol antioxidants and benzophenone UV absorbers are common polymer additives and often used in combination applications to enhance the anti-aging performance of polymer materials. This study primarily aims to incorporate hindered phenol and benzophenone structures into a single molecule to develop a multifunctional polymer additive with good anti-aging performance. Thus, a novel potential polymer anti-aging agent, namely 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionic acid 3-(4-benzoyl-3-hydroxyphenoxy)propyl ester (3C), was synthesized using 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionic acid, 3-bromo-1-propanol, and 2,4-dihydroxybenzophenone as raw materials by two-step procedure. The structure of compound 3C was characterized by nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HRMS), Fourier-transform infrared (FT-IR) spectroscopy, and X-ray single crystal diffraction. Its thermal stability and UV resistance were assessed using thermogravimetric analysis (TGA) and UV absorption spectroscopy (UV). The compound 3C as an additive was incorporated into the preparation of polyolefin elastomer (POE) films. The anti-aging performance of POE films was evaluated by measuring parameters such as oxidation induction time, melt flow index, transmittance, and infrared spectra of the artificially aged POE films. The results indicate that the compound 3C exhibits a promising anti-aging performance in both thermo-oxidative aging and ultraviolet aging tests of POE films and is a potential polymer anti-aging agent.

Lung cancer has become one of the most fatal cancers at present. Traditional treatments showed limited therapeutic effects on lung cancer. The phototherapy has emerged as a powerful approach for lung cancer treatment. Aggregation-induced emission luminogens (AIEgens) exhibit excellent optical performance such as strong fluorescence, enhanced reactive oxygen species (ROS) generation, and effective thermal effect after aggregation, which show great potential in phototherapy. However, the disadvantages including hydrophobicity, low specificity, and short circulation lifetime limited their efficacy on cancer therapy. We developed a biomimetic AIEgens constructed using CD8 T cells membrane to camouflage the AIEgen C H N O S (named BITT) nanoparticles (termed TB). The prepared TB improved the tumor accumulation of AIEgen by PD-1/PD-L1 recognition on the CD8 T and LLC cell membranes, respectively. The prepared TB showed improved binding efficiency, photothermal effects, and ROS generation ability to kill the lung cancer cells. TB also showed improved circulation lifetime and excellent tumor targeting ability, leading to effective phototherapy and immunotherapy in vivo based on BITT and the CD8 T cell-derived membranes. Based on the AIE and immune checkpoint blockade (ICB) strategies, TB enhanced the antitumor activities of lung cancer by phototherapy and immunotherapy. The present work developed a type of biomimetic AIEgens, which overcame the inherent limitations of conventional AIEgens and leveraged immune recognition for targeted tumor accumulation. Furthermore, the integration of AIE-driven phototherapy with immune checkpoint blockade demonstrated potent synergistic antitumor efficacy, establishing a promising combinatorial strategy against aggressive lung malignancies.

We present in this paper an algorithm that is capable of clustering images taken by an unknown number of unknown digital cameras into groups, such that each contains only images taken by the same source camera. It first extracts a sensor pattern noise (SPN) from each image, which serves as the fingerprint of the camera that has taken the image. The image clustering is performed based on the pairwise correlations between camera fingerprints extracted from images. During this process, each SPN is treated as a random variable and a Markov random field (MRF) approach is employed to iteratively assign a class label to each SPN (i.e., random variable). The clustering process requires no a priori knowledge about the dataset from the user. A concise yet effective cost function is formulated to allow different “neighbors” different voting power in determining the class label of the image in question depending on their similarities. Comparative experiments were carried out on the Dresden image database to demonstrate the advantages of the proposed clustering algorithm.

Stereoselective decarboxylative protonation can produce diverse chiral molecules from widely available carboxylic acids. However, general and practical strategies are lacking. Now, a chiral spirocyclic phosphoric acid-catalysed decarboxylation of aminomalonic acids has enabled the modular synthesis of α-amino acids.