Self-Adaptive Aromatic Cation-π Driven Dimensional Polymorphism in Supramolecular Polymers for the Photocatalytic Oxidation and Separation of Aromatic/Cyclic Aliphatic Compounds.

The phenomenon of polymorphism is ubiquitous in nature, the controlled manipulation of which not only increases our ontological understanding of nature but also facilitates the conceptualization and realization of novel functional materials. However, achieving targeted polymorphism in supramolecular assemblies (SAs) remains a formidable challenge, largely because of the constraints inherent in controlling the specific binding motifs of noncovalent interactions. Herein, we propose self-adaptive aromatic cation-π binding motifs to construct polymorphic SAs in both the solid and solution states. Using distinct discrete cation-π-cation and long-range cation-π binding motifs enables control of the self-assembly directionality of a C2h-symmetric bifunctional monomer, resulting in the successful formation of both two-dimensional and three-dimensional crystalline SAs (2D-CSA and 3D-CSA). The differences in the molecular packing of 3D-CSA compared with that of 2D-CSA significantly improve the charge separation and carrier mobility, leading to enhanced photocatalytic activity for the aerobic oxidation of thioanisole to methyl phenyl sulfoxide (yield of 99 % vs 57 %). 2D-CSA, which has a vertical extended structure with favorable stronger interaction with toluene though face-to-face cation-π interactions than methylcyclohexane, shows higher toluene/methylcyclohexane separation efficiency than 3D-CSA (96.9 % for 2D-CSA vs 56.3 % for 3D-CSA).

Intramolecular Cation-π Interactions Organize Bowl-Shaped, Luminescent Molecular Containers.

Molecules with nonplanar architectures are highly desirable due to their unique topological structures and functions. We report here the synthesis of two molecular containers (1 ⋅ 3Br- and 1 ⋅ 3Cl-), which utilize intramolecular cation-π interactions to enforce macrocylic arrangements and exhibit high binding affinity and luminescent properties. Remarkably, the geometry of the cation-π interaction can be flexibly tailored to achieve a precise ring arrangement, irrespective of the angle of the noncovalent bonds. Additionally, the C-H⋅⋅⋅Br- hydrogen bonds within the container are also conducive to stabilizing the bowl-shaped conformation. These bowl-shaped conformations were confirmed both in solution through NMR spectroscopy and in the solid state by X-ray studies. 1 ⋅ 3Br- shows high binding affinity and selectivity: F->Cl-, through C-H⋅⋅⋅X- (X=F, Cl) hydrogen bonds. Additionally, these containers exhibited blue fluorescence in solution and yellow room-temperature phosphorescence (RTP) in the solid state. Our findings illustrate the utility of cation-π interactions in designing functional molecules.

Oxide Ion Conduction in Ca-Doped Yb3Ga5O12 Garnet.

Developing oxide ion conductors with new structural families is important for many energy conversion and storage techniques. Herein, a series of Ca-doped Yb3Ga5O12 garnet-type materials are prepared through a traditional solid-state reaction method, with their oxide ion conduction properties being reported for the first time. The results revealed that Ca substitution for Yb would significantly improve the conductivity of Yb3Ga5O12 from 3.57 × 10-7 S/cm at 900 °C under air to 1.66 × 10-4 S/cm, with an oxide ion transporting number of ∼0.52. The oxygen vacancy defect formation energy (∼0.127 eV) and the local structure around an oxygen vacancy were studied by atomic-level static lattice simulations based on the interatomic potential method. The oxide ion conducting mechanism was studied by the bond-valence-based method, which revealed three-dimensional pathways for oxide ion migration in both the parent and Ca-doped structures. The simulated activation energy of oxide ion migration decreased slightly from ∼0.358 eV in the parent structure to 0.346 eV in the doped one. These discoveries in the Ca-doped Yb3Ga5O12 will stimulate extensive exploitation and fundamental research on garnet-type materials.

Applications of the non-negative least-squares deconvolution method to analyze energy-dispersive x-ray fluorescence spectra.

We used the Monte Carlo simulation method to establish a detector response matrix and the non-negative least-squares method to deconvolute x-ray spectra. The simulation and experimental data verified the effectiveness of this method, and the influence of full-width at the half of the maximum calibration accuracy on the deconvolution results was investigated. The non-negative least-squares method had high accuracy and efficiency compared with others. The results showed that, except for Zn, the relative errors between the inversion and the standard values were less than 0.1% for the simulated spectra. For the experimental data, the relative errors were within 0.2%. The peaks with similar characteristic energies can be better distinguished in the deconvolution spectra, reducing the errors caused by overlapping peaks in subsequent analysis.

Double Cation-π Directed Two-Dimensional Metallacycle-Based Hierarchical Self-Assemblies for Dual-Mode Catalysis.

Hierarchical self-assembly of Pt(II) metallacycles for the construction of functional materials has received considerable research interest, owing to their potential to meet increasing complexity and functionality demands while being based on well-defined scaffolds. However, the fabrication of long-range-ordered Pt(II) metallacycle-based two-dimensional hierarchical self-assemblies (2D HSAs) remains a challenge, primarily because of the limitations of conventional orthogonal noncovalent interaction (NCI) motifs and the intrinsic characteristics of Pt(II) metallacycles, making the delicate self-assembly processes difficult to control. Herein, we prepare well-regulated Pt(II)-metallacycle-based 2D HSAs through a directed strategy involving double cation-π interactions derived from C3-symmetric hexagonal Pt(II) metallacycles and C2-symmetric sodium phenate monomers. Spatially confined arrays of planar Pt(II) metallacycles and the selective growth of self-assemblies at desired locations are achieved by employing strong cation-π driving forces with well-defined directionality as the second orthogonal NCI, realizing the bottom-up, three-stage construction of Pt(II)-metallacycle-based 2D HSAs. The resultant 2D HSAs are applied as dual-mode catalysis platforms, which are loaded with two different nanocatalysts, one promoting catalytic oxidation and the other promoting photocatalytic reduction.

Ground Radioactivity Distribution Reconstruction and Dose Rate Estimation Based on Spectrum Deconvolution.

Estimating the gamma dose rate at one meter above ground level and determining the distribution of radioactive pollution from aerial radiation monitoring data are the core technical issues of unmanned aerial vehicle nuclear radiation monitoring. In this paper, a reconstruction algorithm of the ground radioactivity distribution based on spectral deconvolution was proposed for the problem of regional surface source radioactivity distribution reconstruction and dose rate estimation. The algorithm estimates unknown radioactive nuclide types and their distributions using spectrum deconvolution and introduces energy windows to improve the accuracy of the deconvolution results, achieving accurate reconstruction of multiple continuous distribution radioactive nuclides and their distributions, as well as dose rate estimation of one meter above ground level. The feasibility and effectiveness of the method were verified through cases of single-nuclide (137Cs) and multi-nuclide (137Cs and 60Co) surface sources by modeling and solving them. The results showed that the cosine similarities between the estimated ground radioactivity distribution and dose rate distribution with the true value were 0.9950 and 0.9965, respectively, which could prove that the proposed reconstruction algorithm would effectively distinguish multiple radioactive nuclides and accurately restore their radioactivity distribution. Finally, the influences of statistical fluctuation levels and the number of energy windows on the deconvolution results were analyzed, showing that the lower the statistical fluctuation level and the more energy window divisions, the better the deconvolution results.

A New Temperature Correction Method for NaI(Tl) Detectors Based on Pulse Deconvolution.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at various temperatures from -20 °C to 50 °C. Pulse processing and spectrum synthesis showed that the position drift of the 137Cs 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranged from 6.91% to 10.60% with the trapezoidal width set from 1000 ns to 100 ns. The DTSAC method corrects the temperature effect via pulse processing, and needs no reference peak, reference spectrum or additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at a high counting rate.

A specific fine-grained identification model for plasma-treated rice growth using multiscale shortcut convolutional neural network.

As an agricultural innovation, low-temperature plasma technology is an environmentally friendly green technology that increases crop quality and productivity. However, there is a lack of research on the identification of plasma-treated rice growth. Although traditional convolutional neural networks (CNN) can automatically share convolution kernels and extract features, the outputs are only suitable for entry-level categorization. Indeed, shortcuts from the bottom layers to fully connected layers can be established feasibly in order to utilize spatial and local information from the bottom layers, which contain small distinctions necessary for fine-grain identification. In this work, 5000 original images which contain the basic growth information of rice (including plasma treated rice and the control rice) at the tillering stage were collected. An efficient multiscale shortcut CNN (MSCNN) model utilizing key information and cross-layer features was proposed. The results show that MSCNN outperforms the mainstream models in terms of accuracy, recall, precision and F1 score with 92.64%, 90.87%, 92.88% and 92.69%, respectively. Finally, the ablation experiment, comparing the average precision of MSCNN with and without shortcuts, revealed that the MSCNN with three shortcuts achieved the best performance with the highest precision.

Chiroptical switching in the azobenzene-based self-locked [1]rotaxane by solvent and photoirradiation.

Because of its dynamic reversible nature and simple regulation properties, rotaxane systems provided a good route for the construction of responsive supramolecular chiral materials. Here, we covalently encapsulate the photo-responsive guest molecule azobenzene (Azo) in a chiral macrocycle ß-cyclodextrin (ß-CD) to prepare self-locked chiral [1]rotaxane [Azo-CD]. On this basis, the self-adaptive conformation of [Azo-CD] was manipulated by solvent and photoirradiation; meanwhile, dual orthogonal regulation of the [1]rotaxane chiroptical switching could also be realized.

Cholesterol-binding sequence is a key regulatory motif of cellular folding and conformational activation for C-reactive protein.

Human, rat, and mouse C-reactive protein (CRP) possess distinct expression patterns, but have similar conformations and conserved in vivo functions. We have previously demonstrated that this level-function mismatch is delicately tuned by the hidden activities of unfolded CRP. The cholesterol-binding sequence (CBS; a.a. 35-47) is a major functional motif exposed on monomeric CRP, which is the unfolded and activated conformation of CRP. We replaced the CBS of rat CRP with that of either mouse or human CRP, yielding two grafting mutants with unaffected pentameric assembly. However, these mutants exhibited altered cellular foldability and conformational activation efficiency that matched those of the CRP that provided the grafted CBS. These results indicate that CBS is a critical regulatory motif, whose variation maintains the pentameric assembly of CRP but derives distinct cellular foldabilities and conformational activation efficiencies, therefore helping to ensure that CRPs with various expression patterns exhibit overall conserved functions.

The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things.

Under the background of the Internet of things (IoT), the problems between the actual production and the environment are also prominent. The environmental cost control in the production process of manufacturing enterprises are discussed to reduce the environmental cost and promote the improvement of production efficiency. First, the environmental cost under the background of IoT is analyzed. Also, the environmental cost control methods in the production process of traditional manufacturing enterprises are investigated. Second, based on the principle of traditional genetic algorithm, the fast-nondominated sorting genetic algorithm (NSGA-II) of multiobjective genetic algorithm is introduced to complete the optimization of BP neural network (BPNN) algorithm in deep learning (DL), and the multiobjective GA optimization BPNN model is established. Finally, the multiobjective GA algorithm is used to empirically analyze the environmental cost control capability of a paper-making enterprise. It is compared with enterprises with excellent and poor environmental cost control capabilities in the same industry to find out secondary indexes. The results show that environmental costs have long-term and economic characteristics. The global search ability of BPNN optimized by multiobjective GA is improved, and the local optimal dilemma is avoided. Through empirical analysis, it is found that the comprehensive capability of the environmental cost control of the enterprise is better, scored 79 or more, and the indexes of insufficient development and advantages are obtained. As IoT rapidly develops, it is necessary to further improve the ability of enterprises in environmental cost management, which is very important to promote the development of enterprises and enhance their core competitiveness. It is hoped that this investigation can provide certain reference significance for improving the environmental cost management capability of enterprises, increasing production efficiency, and reducing environmental costs.

Comparison of machine learning approaches for radioisotope identification using NaI(TI) gamma-ray spectrum.

This research aims at comparing the performance of different machine learning algorithms used for NaI(TI) gamma-ray detector based radioisotope identification. Six machine learning algorithms were implemented, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), naive Bayes (NB), decision tree (DT), and multilayer perceptron (MLP). The hyper-parameters of each model were elaborately optimized. The effects of data size, statistical fluctuation, and spectrum drift were considered. Results show that for smaller data size (5 types of radioisotopes and 6000 spectra), the support vector machine and the logistic regression classifier exhibit higher identification accuracy with less training/predicting time. Whereas for larger data size (14 types of radioisotopes corresponding to the standard IEC 62327-2017), the multilayer perceptron showed highest accuracy but required the longest time for model training. The naive Bayes classifier and the decision tree were prone to make mistakes when fluctuations and distortions were added to the spectra. The k-nearest neighbor classifier, though showing high accuracy for most data sets, consumed the longest time while making prediction.

Migration behavior and leaching ability of radioactive uranium during incineration of uranium-containing strippable coating wastes.

It is currently thought that the incineration approach is an effective method to minimize the volume of radioactive wastes. In this paper, we used an incinerator to burn uranium-containing strippable coating waste. The migration behavior of radioactive uranium during the incineration process were investigated based on hierarchical sampling and mass spectrometry. Results shows that the radioactive uranium is more easily to adhere to the particles with smaller size. The leaching abilities of radioactive uranium in the bottom ash and the fly ash were analyzed. The leaching rate of the uranium from the fly ash and bottom ash were 1% and 6%, respectively, indicating that most of the radioactive uranium was fixed in the ash and the same storage/disposal methods can be used for both the fly ash and bottom ash. According to x-ray spectrometry and SEM-EDS, mineral compositions of the original uranium ore and the bottom ash were mostly the same. Calcium plays an important role in uranium fixation during incineration. The potential mechanism of the uranium special transformation during uranium-containing strippable coating waste combustion was revealed. Our research results can provide technical support for nuclear emergency waste treatment and disposal.

Adsorption behavior of Cs(I) on natural soils: Batch experiments and model-based quantification of different adsorption sites.

Understanding the adsorption behavior of radiocesium (RCs) in natural soils is crucial for remediation and evaluation of radioactive contaminated sites. In this study, we investigated the adsorption behavior of Cs(I) onto natural soils collected in Beijing by batch adsorption experiments and sequential extraction. A multi-site adsorption model was built to quantitatively analyze the adsorption capacities of soil clay minerals and predict of Cs(I) adsorption ratio of different adsorption sites. Linear programming calculations show that illite/smectite (I/S) mixture and illite(I) are the mainly clay mineral composition. Batch adsorption experiment results show that soils adsorption of Cesium ions is an exothermic process, and the order of influence of competitive cations on the competitive adsorption strength of Cs(I) is:K+>Mg2+≈Ca2+>Na+. HA (Humic Acid)has little effect on soil adsorption. SEM-EDS analysis shows that Cs+ is mainly distributed on the surface (PS) of soil particles. Based on the above results, the adsorption of Cs(I) onto clay minerals in soils is well predicts in both linear programming calculations and a multi-site adsorption model. The multi-site adsorption model can quantitatively describe and predict the adsorption behavior of Cs(I) on different clay sites in the soils. Frayed edge sites (FES) in the soil can effectively fix trace RCs. The higher concentration of cesium ions is mainly adsorbed on the PS and TIIS. Sequential extraction experiment further proved the adsorption form of cesium in soil under trace and high concentration conditions.

Platinum-Containing Supramolecular Drug Self-Delivery Nanomicelles for Efficient Synergistic Combination Chemotherapy.

Supramolecular drug self-delivery systems (SDSDSs) involving active drugs as building blocks linked by supramolecular interactions have been well defined as an advanced chemotherapy strategy. However, the lack of detecting release of drugs from SDSDSs at specific tumor sites inevitably leads to unsatisfactory therapeutic effects, owing to the lack of information regarding the administration of these drugs. In this work, predesigned platinum-containing supramolecular drug self-delivery nanomicelles (SDSDNMs) were employed to synchronously realize drug monitoring by computed tomography imaging, immediately reflecting the evolution of drug release and real-time treatment at the tumor site. The appropriate administration dosage (1.2 mg mL-1,100 µL) and the injection interval (once every 3 days) needed to guide the antitumor activity of SDSDNMs were then defined, thereby attaining the aim of efficient synergistic combination chemotherapy. In vivo tumor inhibition and histological analyses showed that SDSDNMs exhibited a strong tumor inhibition effect and good safety with respect to normal organs. Such a supramolecular drug self-delivery strategy with monitored functions may offer new potential opportunities for application in the field of synergistic combination chemotherapy.

ß-Cyclodextrin modified Pt(II) metallacycle-based supramolecular hyperbranched polymer assemblies for DOX delivery to liver cancer cells.

Despite the widespread clinical application of chemotherapeutic anticancer drugs, their adverse side effects and inefficient performances remain ongoing issues. A drug delivery system (DDS) designed for a specific cancer may therefore overcome the drawbacks of single chemotherapeutic drugs and provide precise and synergistical cancer treatment by introducing exclusive stimulus responsiveness and combined chemotherapy properties. Herein, we report the design and synthesis of a supramolecular drug delivery assembly 1 constructed by orthogonal self-assembly technique in aqueous media specifically for application in liver cancer therapy. Complex 1 incorporates the ß-cyclodextrin host molecule-functionalized organoplatinum(II) metallacycle 2 with two specific stimulus-responsive motifs to the signaling molecule nitric oxide (NO), in addition to the three-armed polyethylene glycol (PEG) functionalized ferrocene 3 with redox responsiveness. With this molecular design, the particularly low critical aggregation concentration (CAC) of assembly 1 allowed encapsulation of the commercial anticancer drug doxorubicin (DOX). Controlled drug release was also achieved by morphological transfer via a sensitive response to the endogenous redox and NO stimuli, which are specifically related to the microenvironment of liver tumor cells. Upon combination of these properties with the anticancer ability from the platinum acceptor, in vitro studies demonstrated that DOX-loaded 1 is able to codeliver anticancer drugs and exhibit therapeutic effectiveness to liver tumor sites via a synergistic effect, thereby revealing a potential DDS platform for precise liver cancer therapeutics.

Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet.

Emotion recognition and monitoring based on commonly used wearable devices can play an important role in psychological health monitoring and human-computer interaction. However, the existing methods cannot rely on the common smart bracelets or watches for emotion monitoring in daily life. To address this issue, our study proposes a method for emotional recognition using heart rate data from a wearable smart bracelet. A 'neutral + target' pair emotion stimulation experimental paradigm was presented, and a dataset of heart rate from 25 subjects was established, where neutral plus target emotion (neutral, happy, and sad) stimulation video pairs from China's standard Emotional Video Stimuli materials (CEVS) were applied to the recruited subjects. Normalized features from the data of target emotions normalized by the baseline data of neutral mood were adopted. Emotion recognition experiment results approved the effectiveness of 'neutral + target' video pair simulation experimental paradigm, the baseline setting using neutral mood data, and the normalized features, as well as the classifiers of Adaboost and GBDT on this dataset. This method will promote the development of wearable consumer electronic devices for monitoring human emotional moods.

A Color-Tunable Fluorescent Supramolecular Hyperbranched Polymer Constructed by Pillar[5]arene-Based Host-Guest Recognition and Metal Ion Coordination Interaction.

Fluorescent supramolecular polymers are an important kind of smart material. In this work, a new fluorescent supramolecular hyperbranched polymer (FSHP) is constructed by orthogonal self-assembly: pillararene-based host-guest interaction and metal ion complexation interaction. The FSHP exhibits concentration-controllable fluorescence emissions. The photoluminescence spectra and light-emitting colors of FSHP can be effectively tuned by changing metal ion types or using mixed metal ions. The fluorescence quenching of FSHP solutions or FSHP-based films would occur when removing the metal ions from the backbone of FSHP. This study supplies a convenient approach toward the construction of structure-tunable fluorescent supramolecular materials with different colors.

Large eddy simulation and experimental study on vented gasoline-air mixture explosions in a semi-confined obstructed pipe.

In this work, LES simulation coupled with a TFC sub-grid combustion model has been performed in a semi-confined pipe (L/D=10, V=10L) in the presence of four hollow-square obstacles (BR=49.8%) with circular hollow cross-section, in order to study the premixed gasoline-air mixture explosions. The comparisons between simulated results and experimental results have been conducted. It was found that the simulated results were in good agreement with experimental data in terms of flame structures, flame locations and overpressure time histories. Moreover, the interaction between flame propagation process and obstacles, overpressure dynamics were analyzed. In addition, the effects of initial gasoline vapor concentration (lean (ϕ=1.3%), stoichiometric (ϕ=1.7%) and rich (ϕ=2.1%)), and the number of obstacles (from 1 to 4) were also investigated by experiments. Some of the experimental results have been compared with the literature data. It is found that the explosion parameters of gasoline-air mixtures (e.g. the maximum overpressure peaks, average overpressure growth rates, etc.) are different from some other fuels such as hydrogen, methane and LPG, etc.