Multifactorial Risk Prediction Analysis of Liver Metastasis in Colorectal Cancer: Incorporating PD-L1 CPS and Other Factors.

BACKGROUND: The occurrence of liver metastasis significantly impacts the prognosis of colorectal cancer. Existing research indicates that primary tumor location, vascular invasion, lymph node metastasis, and abnormal preoperative tumor markers are risk factors for colorectal cancer liver metastasis. Positive expression of PD-L1(Programmed Cell Death 1 Ligand 1) may serve as a favorable prognostic marker for nasopharyngeal and gastric cancers, where CPS（Combined Positive Score） quantifies the level of PD-L1 expression. Based on previous studies, exploring CPS as a potential risk factor for colorectal cancer liver metastasis and integrating other independent risk factors to establish a novel predictive model for colorectal cancer liver metastasis. METHODS: A retrospective analysis was conducted on 437 colorectal cancer patients pathologically diagnosed at Xiangya Second Hospital, Central South University, from January 1, 2019, to December 31, 2021. Data were collected, including CPS, age, gender, primary tumor location, Ki-67 expression, pathological differentiation, neural invasion, vascular invasion, lymph node metastasis, and preoperative tumor markers.The optimal cut-off point for the continuous variable CPS was determined using the Youden index, and all CPS were dichotomized into high and low-risk groups based on this threshold (scores below the threshold were considered high-risk, and those above it were considered low-risk). Univariate logistic regression analysis was employed to identify risk factors for colorectal cancer liver metastasis, followed by the application of multivariate logistic regression analysis to integrate the selected risk factors.The predictive model established was validated through the construction of ROC (Receiver Operating Characteristic) curves, calibration curves, and DCA (Decision Curve Analysis). A nomogram was constructed for visualization purposes. RESULTS: The determined cut-off point for PD-L1 CPS was 4.5, with scores below this threshold indicating high risk for colorectal cancer liver metastasis. Additionally, primary tumor origin other than the rectum, presence of pericolonic lymph node metastasis, and abnormal levels of tumor markers CEA and CA199 were identified as independent risk factors for colorectal cancer liver metastasis. The constructed clinical prediction model demonstrated good predictive ability and accuracy, with an area under the ROC curve of 0.871 (95% CI 0.838-0.904). CONCLUSION: The exploration and validation of CPS as a novel predictor for colorectal cancer liver metastasis were conducted. Based on this, a new clinical prediction model for colorectal cancer liver metastasis was developed by integrating other independent risk factors. The DCA, clinical impact curve, and nomogram graph constructed based on this model have significant clinical implications and provide guidance for clinical practice.

Acute exacerbation prediction of COPD based on Auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification.

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier's performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

[Ecological Risk Assessment and Source Apportionment of Heavy Metals in Mineral Resource Base Based on Soil Parent Materials].

Mineral resource bases have dual properties, e.g., mineral resources and environmental pollution. The latter could be classified into natural and anthropogenic pollution based on identifying the spatial distribution characteristics and sources of heavy metals in the soil. The Hongqi vanadium titano-magnetite mineral resources base in Luanping County, Luanhe watershed, was taken as the research object. The geo-accumulation index (Igeo), Nemerow comprehensive pollution index (PN), and potential ecological risk (Ei) were utilized to assess the soil heavy metal pollution characteristics, and redundancy analysis (RDA) and positive determinate matrix factorization (PMF) were employed to identify sources of the soil heavy metals. The results revealed that the mean contents of Cr, Cu, and Ni in the parent material of medium-basic hornblende metamorphic rock and medium-basic gneisses metamorphic rock were 1-2 times that in other parent materials in the concentrated area of mineral resources. However, the mean contents of Pb and As were lower. Fluvial alluvial-proluvial parent materials had the highest mean content of Hg, and the mean content of Cd was higher in the parent materials of medium-basic gneisses metamorphic rocks, acid rhyolite volcanic rocks, and fluvial alluvial-proluvial facies. The Igeodecreased in the following order:Cd>Cu>Pb>Ni>Zn>Cr>Hg>As. PN ranged from 0.61 to 18.99, and the sample proportion of moderate and severe pollution reached 10.00% and 8.08% respectively. Pishowed that the contents of Cu, Cd, Cr, and Ni were relatively higher in the parent material of intermediate-basic hornblende metamorphic rocks and intermediate-basic gneiss metamorphic rocks. Ei decreased in the order of Hg(58.06)>Cd(39.72)>As(10.98)>Cu(6.56) >Pb(5.60)>Ni(5.43)>Cr(2.01)>Zn(1.10). Samples whose RI was lower than 150 accounted for 84.27%, showing that the research area was predominantly at a slight potential ecological risk level. The sources of soil heavy metals were dominated by parent material weathering, followed by the mixed sources of agricultural activities and transportation, the exploitation of mining, and fossil burning, which accounted for 41.44%, 31.83%, 22.01%, and 4.73%, respectively. The risks of heavy metal pollution in the mineral resource base were characterized as multi-source instead of the single source from the mining industry. These research results provide the scientific basis for regional green mining development and eco-environmental protection.

Engineering polyhedral high entropy oxide with high-index facets via mechanochemistry-assisted strategy for efficient oxidative desulfurization.

High entropy oxides are promising catalysts for numerous catalytic oxidation processes with oxygen as the oxidant. However, most of them often show bulk morphologies, which hinders the full exposure of active sites. In this work, a unique 26-faceted polyhedral high entropy oxide MnNiCuZnCoOx-1000 (P-HEO) with highly active site exposure is fabricated via a mechanochemistry-assisted strategy. By employing such a strategy, the supersaturation of P-HEO during the crystal growth process is effectively reduced to form high-index facets, which is proved to be beneficial to the formation of high-index facets. Characterization results indicate that more oxygen vacancies are generated in P-HEO compared with the bulk counterparts. Density functional theory calculations reveal that the high-index facets {-211} can facilitate adsorption and activation of O2 because of the higher adsorption energy -2.23 eV compared with that of (111) surfaces (-1.79 eV), which induces significantly enhanced activity for organic sulfides oxidation. Interestingly, the synthesized P-HEO with high-index facets shows a 98.4% removal rate of dibenzothiophene from model oil within 8 h at 120 °C, which is much higher than that of the bulk counterparts (33.5%).

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network.

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier.

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%).

Oxygen vacancy triggering the broad-spectrum photocatalysis of bismuth oxyhalide solid solution for ciprofloxacin removal.

The construction of a broad-spectrum photocatalytic system is of great significance for maximizing the utilization of solar energy. Herein, a surface oxygen vacancy triggering high-efficient broad-spectrum BiOCl0.5I0.5 solid solution photocatalyst was successfully fabricated via a one-pot solvothermal process. The UV-vis diffuse reflectance spectra revealed that the introduced oxygen vacancy appears to extend the absorption region of BiOCl0.5I0.5 to a wider wavelength range. Under λ > 580 nm light irradiation for 5 h, nearly 85.6% ciprofloxacin was degraded by BiOCl0.5I0.5 with rich oxygen vacancy, the ciprofloxacin removal efficiency was 3.4 times higher than that with less oxygen vacancy. Moreover, the density functional theory calculations and photoelectrochemical characterizations indicated the excited electrons would preferentially transfer to the new defect level induced by oxygen vacancy, thus greatly reducing the recombination of photogenerated carriers. This work tends to deepen the understanding of defect engineering in steering the construction of broad-spectrum Bi-based solid solution photocatalysts as well as its application in environmental remediation.

DFT Investigation of Triarylamine-α-cyanoacrylic Acid Compounds: Structural, Electronic, and Nonlinear Optical Properties.

Using the density functional theory and finite field method, nonlinear optical properties of nine triarylamine-α-cyanocinnamic acid derivatives were investigated at the M06-2X/6-311++ G(d,p) and ωB97X-D/6-311++ G(d,p) levels of theory. Except for (E)-2-cyano-3-(4-(di([1,1'-biphenyl]-4-yl)amino)phenyl)acrylic acid (a), which had a D-π-A electronic structure, all the other eight derivatives had an A-π-D-π-A structure. The results suggest that the lowest energy transition of the nine triarylamine derivatives was the π-π* transition from the HOMO to LUMO. The absorption maxima of the derivatives in their ethanol solution were redshifted with respect to those in the gas phase. The introduction of conjugated C = C or C≡C bonds between the biphenyl unit of molecule a had a minor effect on the second-order nonlinear optical properties of the molecule. However, the introduction of C = C bond into the parent molecule improved the third-order nonlinear optical properties. The introduction of a heterocyclic ring (furan ring or thiophene ring) between the triarylamine moiety and the branched chain containing the cyanocinnamic acid group enhanced the second- and third-order nonlinear optical properties; especially, the second- and third-order polarisabilities of molecules b3 and c3, which were obtained by introducing a thiophene ring, were the highest. The second- and third-order polarisabilities of b3 were 0.13 × 105 and 27.13 × 105 a.u., respectively, while those of c3 were 0.14 × 105 and 28.10 × 105 a.u., respectively. This suggests that b3 and c3 have desirable second- and third-order nonlinear optical properties and can be used for designing efficient second- and third-order nonlinear optical materials.

Early COPD Risk Decision for Adults Aged From 40 to 79 Years Based on Lung Radiomics Features.

Background: Chronic obstructive pulmonary disease (COPD), a preventable lung disease, has the highest prevalence in the elderly and deserves special consideration regarding earlier warnings in this fragile population. The impact of age on COPD is well known, but the COPD risk of the aging process in the lungs remains unclear. Therefore, it is necessary to understand the COPD risk of the aging process in the lungs, providing an early COPD risk decision for adults. Methods: COPD risk is evaluated for adults to make an early COPD risk decision from the perspective of lung radiomics features. First, the subjects are divided into four groups according to the COPD stages. Their ages are divided into eight equal age intervals in each group. Second, four survival Cox models are established based on the lung radiomics features to evaluate the risk probability from COPD stage 0 to suffering COPD and COPD stages. Finally, four risk ranks are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk at different stages is evaluated with varying age intervals to provide an early COPD risk decision. Results: The evaluation metrics area under the curve (AUC)/C index of four survival Cox models are 0.87/0.94, 0.84/0.83, 0.94/0.89, and 0.97/0.86, respectively, showing the effectiveness of the models. The risk rank levels up every 5 years for the subjects who had suffered COPD after 60. For the subjects with COPD stage 0, the risk rank of suffering COPD stage I levels up every 5 years after the age of 65 years, and the risk rank of suffering COPD stages II and III & IV levels up every 5 years after the age of 70 years. Conclusion: Once the age is above 60 years, the patients with COPD need to take action to prevent the progress and deterioration of COPD. Once the age is above 65 years, the patients with COPD stage 0 need to take precautions against COPD.

Edge-Site-Rich Ordered Macroporous BiOCl Triggers CO Activation for Efficient CO2 Photoreduction.

Endowing a semiconductor with tunable edge active sites will effectively enhance catalytic performance. Herein, an edge-site-rich ordered macroporous BiOCl (BiOCl-P) with abundant dangling bonds is constructed via the colloidal crystal template method. The edge-site-rich ordered macroporous structure provides abundant adsorption sites for CO2 molecules, as well as forms numerous localized electron enrichment areas, accelerating charge transfer. DFT calculations reveal that the dangling bonds-rich configuration can effectively reduce the CO2 activation energy barrier, boost the CO double bond dissociation, and facilitate the proton electron coupling reaction. As a result, the BiOCl-P achieves a higher CO and CH4 generation rate of 78.07 and 3.03 µmol g-1 under 4 h Xe lamp irradiation in a solid-gas system. Finally, the CO2 molecules' conversion process is further investigated by in situ Fourier-transform infrared spectroscopy. This work realizes a new avenue toward the design of vibrant semiconductors on the nanoscale to boost inert CO2 photoreduction.

Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD.

Introduction: Because of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier. Methods: First, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier. Results: Our proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively. Discussion: Compared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.

Event-Based Control for Networked T-S Fuzzy Systems via Auxiliary Random Series Approach.

This paper presents an auxiliary random series approach to model the effect of network induced problems, such as data losses and transmission delay subject to event-based communication scheme for nonlinear continuous time systems. T-S fuzzy model is employed to describe the nonlinear systems. In order to save the bandwidth and energy, we introduce the event-triggered mechanism to reduce the number of data for transmission and computation. Thus, it is necessary to consider the influence of data losses, data disorder, and transmission delay since the transmitted data packets become more important. Consequently, it is very complicated to analyze the performance of such networked system and one of the most difficult part, in the authors' opinion, is to construct the mathematical model of closed-loop systems. In this paper, we present an auxiliary random series approach to describe the data transmitted in the system, and therefore, the closed-loop systems can be obtained. Associated with a tailor-made Lyapunov-Krasovskii functional, the stability analysis is processed and a fuzzy controller is designed. Asynchronous membership functions are considered to obtain more relaxed stability conditions. To clarify the effectiveness of the proposed method, a cart-damper-spring system is employed for simulation.

Event-Triggered Fault Detection of Nonlinear Networked Systems.

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

[Analysis of cytochrome P450 2E1 RsaI/PstI and DraI polymorphisms in workers exposed to benzene].

OBJECTIVE: To investigate the cytochrome P450 2E1 (CYP2E1) RsaI/PstI and DraI polymorphisms in workers exposed to benzene. METHODS: A cross-sectional survey was carried out. A total of 71 workers exposed to benzene were included in observation group and the same number of people without occupational benzene exposure were included in control group. Blood samples from the two groups were collected and genotyping for CYP2E1 RsaI/PstI and DraI were conducted using the polymerase chain reaction-restriction fragment length polymorphism. RESULTS: There were no significant differences in CYP2E1 DraI genotype and allele distributions between the observation group and the control group (χ² = 2.374, P > 0.05; χ² = 2.113, P > 0.05). Significant differences in CYP2E1 RsaI/PstI genotype and allele distributions between the two groups were observed (χ² = 9.129, P < 0.01; χ² = 6.028, P < 0.05). CONCLUSION: Mutations at CYP2E1 RsaI/PstI can enhance the expression of CYP2E1 and this suggests individuals with the mutated gene have increased susceptibility to chronic benzene poisoning.

Pathway toward large two-dimensional hexagonally patterned colloidal nanosheets in solution.

We report the solution self-assembly of an ABC block terpolymer consisting of a polystyrene-block-poly(ethylene oxide) (PS-b-PEO) diblock copolymer tail tethered to a fluorinated polyhedral oligomeric silsesquioxane (FPOSS) cage in 1,4-dioxane/water. With increasing water content, abundant unconventional morphologies, including circular cylinders, two-dimensional hexagonally patterned colloidal nanosheets, and laterally patterned vesicles, are sequentially observed. The formation of toroids is dominated by two competing free energies: the end-cap energy of cylinders and the bending energy to form the circular structures. Incorporating the superhydrophobic FPOSS cages enhances the end-cap energy and promotes toroid formation. Lateral aggregation and fusion of the cylinders results in primitive nanosheets that are stabilized by the thicker rims to partially release the rim-cap energy. Rearrangement of the parallel-aligned FPOSS cylindrical cores generates hexagonally patterned nanosheets. Further increasing the water content induces the formation of vesicles with nanopatterned walls.

Effects of molecular geometry on the self-assembly of giant polymer-dendron conjugates in condensed state.

A series of giant polymer-dendron conjugates with a dendron head and a linear polymer tail were synthesized via"click" chemistry between azide-functionalized polystyrene (PS(N), N: degree-of-polymerization) and t-butyl protected, alkyne-functionalized second generation dendron (tD), followed by a deprotection process to generate a dendron termini possessing nine carboxylic acid groups. The molecular structures were confirmed by nuclear magnetic resonance, size-exclusion chromatographic analyses, and matrix-assisted laser desorption ionization time-of-flight mass spectra. These well-defined conjugates can serve as a model system to study the effects of the molecular geometries on the self-assembly behaviour, as compared with their linear analogues. Four phase morphologies found in flexible linear diblock copolymer systems, including lamellae, bicontinuous double gyroids, hexagonal packed cylinders, and body-centred cubic packed spheres, were observed in this series of conjugates based on the results of small angle X-ray scattering and transmission electron microscopy. All of the domain sizes in these phase separated structures were around or less than 10 nm. A 'half' phase diagram was constructed based on the experimental results. The geometrical effect was found not only to enhance the immiscibility between the PS(N) tail and dendron head, but also systematically shift all of the phase boundaries towards higher volume fractions of the PS(N) tails, resulting in an asymmetrical phase diagram. This study may provide a pathway to the construction of ordered patterns of sub-10 nm feature size using polymer-dendron conjugates.

Synthesis of Shape Amphiphiles Based on POSS Tethered with Two Symmetric/Asymmetric Polymer Tails via Sequential "Grafting-from" and Thiol-Ene "Click" Chemistry.

A series of shape amphiphiles based on functionalized polyhedral oligomeric silsesquioxane (POSS) head tethered with two polymeric tails of symmetric or asymmetric compositions was designed and synthesized using sequential "grafting-from" and "click" surface functionalization. The monofunctionalization of octavinylPOSS was performed using thiol-ene chemistry to afford a dihydroxyl-functionalized POSS that was further derived into precisely defined homo- and heterobifunctional macroinitiators. Polymer tails, such as polycaprolactone and polystyrene, could then be grown from these POSS-based macroinitiators with controlled molecular weight via ring-opening polymerization and atom transfer radical polymerization (ATRP). The vinyl groups on POSS were found to be compatible with ATRP conditions. These macromolecular precursors were further modified by thiol-ene chemistry to install surface functionalities onto the POSS cage. The polymer chain composition and POSS surface chemistry can thus be tuned separately in a modular and efficient way.