Hydrodeoxygenation of Oxygen-Containing Aromatic Plastic Wastes into Cycloalkanes and Aromatics.

Chemical recycling and upcycling offer promising approaches for the management of plastic wastes. Hydrodeoxygenation (HDO) is one of the appealing ways for conversion of oxygen-containing plastic wastes, including PET, PBT, PC, PPO, and PEEK, into cyclic alkanes and aromatics in high yields under mild reaction conditions. The challenge lies in achieving C-O activation while preserving C-C bonds. In this review, we highlight the recent advancements in catalytic strategies and catalysts for the conversion of these oxygen-containing plastic wastes into cycloalkanes and aromatics. The reaction systems, including multi-step routes, direct HDO and transfer HDO methods, are exemplified. The design and performance of HDO catalysts are systematically summarized and compared. We comprehensively discuss the functions of the catalysts' components, reaction pathway and mechanism to gain insights into the HDO process for efficient valorization of oxygen-containing plastic wastes. Finally, we provide perspectives for this field, with specific emphasis on the non-noble metal catalyst design, selectivity control, reaction network and mechanism studies, mixed plastic wastes management and product functionalization. We anticipate that this review will inspire innovations on the catalytic process development and rational catalyst design for the HDO of oxygen-containing aromatic plastics to establish a low-emission circular economy.

Fine-tuned local coordination environment of Pt-N in nanocarbons for efficient propane dehydrogenation.

Based on the disturbance of electronic density, nitrogen-doped nanocarbons show promising properties to anchor metal clusters. However, precisely regulating the coordination mode between N species and the active site remains challenging. Herein, we rationally designed three N types (graphitic N, pyridinic N and pyrrolic N) in nanocarbons to anchor Pt clusters for the benchmark propane dehydrogenation. The specific activity of the pyridinic-N-doped catalyst was 147.54 molC3H6 molPt-1 h-1 at 550 °C, which was 1.3 times higher than those of graphitic- and pyrrolic-N-doped catalysts. Unlike the regular tetrahedron Pt cluster in the graphitic-N catalyst or the distorted three-layered Pt cluster in the pyrrolic-N catalyst, the Pt cluster in the pyridinic-N catalyst was an inverted tetrahedron, which increased the contact degree without geometric repulsion towards C-H bond scission. The geometric parameters of detached H and C atoms in the methylene group for the pyridinic N catalyst was decreased to strengthen the C-H bond scission. After CH3CHCH3* adsorption, the Bader charge of the Pt active site also became highly positive, which tailored the d-band center closer to the Fermi level and provided more vacant orbitals for C-H bond breakage. Therefore, pyridinic N in nanocarbons is promising to anchor small-sized Pt for alkane dehydrogenation in terms of geometric and electronic effects.

Hydrodeoxygenation of Oxygen-Containing Aromatic Plastic Wastes to Liquid Organic Hydrogen Carriers.

To address the global plastic pollution issues and the challenges of hydrogen storage and transportation, we report a system, based on the hydrodeoxygenation (HDO) of oxygen-containing aromatic plastic wastes, from which organic hydrogen carriers (LOHCs) can be derived. We developed a catalytic system comprised of Ru-ReOx /SiO2 +HZSM-5 for direct HDO of polycarbonate (PC), polyethylene terephthalate (PET), polybutylene terephthalate (PBT), polyphenylene oxide (PPO), and their mixtures, to cycloalkanes as LOHCs, with high yields up to 99 %, under mild reaction conditions. The theoretical hydrogen storage capacity reaches ca. 5.74 wt%. The reaction pathway involves depolymerization of PC into C15 aromatics and C15 monophenols by direct hydrogenolysis of the C-O bond between the benzene ring and ester group, and subsequent parallel hydrogenation of C15 aromatics and HDO of C15 monophenols. HDO of cyclic alcohol is the rate-determining step. The active site is Ru metallic nanoparticles with partially covered ReOx species. The excellent performance is attributed to the synergetic effect of oxophilic ReOx species and Ru metallic sites for C-O hydrogenolysis and hydrogenation, and the promotion effect of HZSM-5 for dehydration of cyclic alcohol. The highly efficient and stable dehydrogenation of cycloalkanes over Pt/γ-Al2 O3 confirms that HDO products can act as LOHCs.

Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA.

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

Crystal Facet Structure Dependence and Promising Pd-Pt Catalytic Materials for Perhydroacenaphthene Dehydrogenation.

Designing an effective Pd-Pt catalytic material with excellent catalytic performance for perhydroacenaphthene (PHAN) dehydrogenation is a great challenge. In this work, in order to explore the crystal facet structure over the bimetallic Pd-Pt catalyst on the dehydrogenation performance of PHAN, the surface compositions of two kinds of Pd (Pt) atoms with different coverage on Pd modulated Pt (PdPt) and Pt modulated Pd (PtPd) catalysts were designed and studied by means of density functional theory (DFT). Through the investigation of the reaction path of PHAN dehydrogenation on PdMLPt(111) and PtMLPd(111) surfaces, it was found that PdMLPt(111) was advantageous to PHAN dehydrogenation (Ea = 2.317 eV). This was attributed to a lower energy barrier, more stable dehydrogenation products, and the fact that Pd doping brought Pt(111) close to the Fermi level. Apparently, Pd modulated Pt catalyst has a broad application prospect in the dehydrogenation of PHAN. In the process of optimizing the PdPt morphology, a method for selecting the minimum active unit of PdPt catalysts with different ratios was proposed, that is, the most stable active unit: rhombus structure was determined according to the surface formation energy. Moreover, we correlated the relationship among the number of H atoms removed, adsorption energy, surface charge, activation energy, reaction energy, and surface coverage, and obtained the important parameters to predict the performance of PdPt catalyst in PHAN dehydrogenation system: surface charge and d-band center. Finally, the essential correlativity among Pd-Pt surface characteristics, catalytic PHAN activity, and adsorption energy was constructed; that is, the relationship model among d-band center, H atom, and product C12H8 adsorption energy was established. This work opens a new simultaneous path to improve the catalytic performance of Pd-Pt-based catalytic materials for PHAN dehydrogenation, which can be achieved by regulating the rhombic active units of Pt modulated by Pd.

A preliminary study of the salivary microbiota of young male subjects before, during, and after acute high-altitude exposure.

Background: The microbial community structure in saliva differs at different altitudes. However, the impact of acute high-altitude exposure on the oral microbiota is unclear. This study explored the impact of acute high-altitude exposure on the salivary microbiome to establish a foundation for the future prevention of oral diseases. Methods. Unstimulated whole saliva samples were collected from 12 male subjects at the following three time points: one day before entering high altitude (an altitude of 350 m, pre-altitude group), seven days after arrival at high altitude (an altitude of 4,500 m, altitude group) and seven days after returning to low altitude (an altitude of 350 m, post-altitude group). Thus, a total of 36 saliva samples were obtained. 16S rRNA V3-V4 region amplicon sequencing was used to analyze the diversity and structure of the salivary microbial communities, and a network analysis was employed to investigate the relationships among salivary microorganisms. The function of these microorganisms was predicted with a Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Results: In total, there were 756 operational taxonomic units (OTUs) identified, with 541, 613, and 615 OTUs identified in the pre-altitude, altitude, and post-altitude groups, respectively. Acute high-altitude exposure decreased the diversity of the salivary microbiome. Prior to acute high-altitude exposure, the microbiome mainly consisted of Proteobacteria, Firmicutes, Bacteroidetes, Fusobacteria, and Actinobacteria. After altitude exposure, the relative abundance of Streptococcus and Veillonella increased, and the relative abundance of Prevotella, Porphyromonas, and Alloprevotella decreased. The relationship among the salivary microorganisms was also affected by acute high-altitude exposure. The relative abundance of carbohydrate metabolism gene functions was upregulated, while the relative abundance of coenzyme and vitamin metabolism gene functions was downregulated. Conclusion: Rapid high-altitude exposure decreased the biodiversity of the salivary microbiome, changing the community structure, symbiotic relationships among species, and abundance of functional genes. This suggests that the stress of acute high-altitude exposure influenced the stability of the salivary microbiome.

HGR-Net: Hierarchical Graph Reasoning Network for Arbitrary Shape Scene Text Detection.

As a prerequisite step of scene text reading, scene text detection is known as a challenging task due to natural scene text diversity and variability. Most existing methods either adopt bottom-up sub-text component extraction or focus on top-down text contour regression. From a hybrid perspective, we explore hierarchical text instance-level and component-level representation for arbitrarily-shaped scene text detection. In this work, we propose a novel Hierarchical Graph Reasoning Network (HGR-Net), which consists of a Text Feature Extraction Network (TFEN) and a Text Relation Learner Network (TRLN). TFEN adaptively learns multi-grained text candidates based on shared convolutional feature maps, including instance-level text contours and component-level quadrangles. In TRLN, an inter-text graph is constructed to explore global contextual information with position-awareness between text instances, and an intra-text graph is designed to estimate geometric attributes for establishing component-level linkages. Next, we bridge the cross-feed interaction between instance-level and component-level, and it further achieves hierarchical relational reasoning by learning complementary graph embeddings across levels. Experiments conducted on three publicly available benchmarks SCUT-CTW1500, Total-Text, and ICDAR15 have demonstrated that HGR-Net achieves state-of-the-art performance on arbitrary orientation and arbitrary shape scene text detection.

DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the models' intrinsic mechanisms and prediction results are difficult to explain. There is still room for improvement in prediction performance. We present DeepSTF, a unique deep-learning architecture for predicting TFBSs by integrating DNA sequence and shape profiles. We use the improved transformer encoder structure for the first time in the TFBSs prediction approach. DeepSTF extracts DNA higher-order sequence features using stacked convolutional neural networks (CNNs), whereas rich DNA shape profiles are extracted by combining improved transformer encoder structure and bidirectional long short-term memory (Bi-LSTM), and, finally, the derived higher-order sequence features and representative shape profiles are integrated into the channel dimension to achieve accurate TFBSs prediction. Experiments on 165 ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) datasets show that DeepSTF considerably outperforms several state-of-the-art algorithms in predicting TFBSs, and we explain the usefulness of the transformer encoder structure and the combined strategy using sequence features and shape profiles in capturing multiple dependencies and learning essential features. In addition, this paper examines the significance of DNA shape features predicting TFBSs. The source code of DeepSTF is available at https://github.com/YuBinLab-QUST/DeepSTF/.

Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have shown huge potential in determining the specificity of TFs-DNA interactions. Convolutional network-based and self-attention network-based methods have been proposed for transcription factor binding sites (TFBSs) prediction. Convolutional operations are efficient to extract local features but easy to ignore global information, while self-attention mechanisms are expert in capturing long-distance dependencies but difficult to pay attention to local feature details. To discover comprehensive features for a given sequence as far as possible, we propose a Dual-branch model combining Self-Attention and Convolution, dubbed as DSAC, which fuses local features and global representations in an interactive way. In terms of features, convolution and self-attention contribute to feature extraction collaboratively, enhancing the representation learning. In terms of structure, a lightweight but efficient architecture of network is designed for the prediction, in particular, the dual-branch structure makes the convolution and the self-attention mechanism can be fully utilized to improve the predictive ability of our model. The experiment results on 165 ChIP-seq datasets show that DSAC obviously outperforms other five deep learning based methods and demonstrate that our model can effectively predict TFBSs based on sequence feature alone. The source code of DSAC is available at https://github.com/YuBinLab-QUST/DSAC/.

Selective H2O2 Electrosynthesis over Defective Carbon from Electrochemical Etching of Molybdenum Carbide.

The controllable synthesis of specific defective carbon catalysts is crucial for two-electron oxygen reduction reaction (2e- ORR) to generate H2O2 due to the great potential applications. Herein, the defective carbon catalysts (Mo-CDC-ns) were prepared by an electrochemical activation (ECA) method with Mo2C/C as a parent. Electrochemical cyclic voltammetry curves, X-ray photoelectron spectroscopy, inductively coupled plasma-mass spectrometry, scanning electron microscopy, and high-resolution transmission electron microscopy confirm the evolution process of a defective carbon structure from the Mo2C phase in which Mo species are first oxidized to Mo6+ species and then the latter are dissolved into the solution and defective carbon is simultaneously formed. Raman and electron paramagnetic resonance spectra reveal that the defect types in Mo-CDC-ns are the edge defect and vacancy defect sites. Compared with the parent Mo2C/C, Mo-CDC-ns exhibit gradually increased kinetic current density and selectivity for H2O2 generation with an extension of activation cycles from 10 (Mo-CDC-10) to 30 (Mo-CDC-30). Over Mo-CDC-30, a kinetic current density of 19.4 mA cm-2 and a selectivity close to 90% in 0.1 M KOH solution were achieved, as well as good stability for H2O2 production in an extended test up to 12 h in an H-cell. Graphene planes and Stone Wales 5757-carbon were constructed as basic models for density functional theory calculations. It revealed that the obtained defective structure after the removal of Mo atoms contains the double vacancy at the edge of graphene (Edge-DVC) and the topological defect on the plane of 5757-carbon (5757C-D), which show more moderate reaction free energy for forming *OOH and smaller energy barrier of 2e- ORR.

A non-perturbative study of the interplay between electron-phonon interaction and Coulomb interaction in undoped graphene.

In condensed-matter systems, electrons are subjected to two different interactions under certain conditions. Even if both interactions are weak, it is difficult to perform perturbative calculations due to the complexity caused by the interplay of two interactions. When one or two interactions are strong, ordinary perturbation theory may become invalid. Here we consider undoped graphene as an example and provide a non-perturbative quantum-field-theoretic analysis of the interplay of electron-phonon interaction and Coulomb interaction. We treat these two interactions on an equal footing and derive the exact Dyson-Schwinger (DS) integral equation of the full Dirac-fermion propagator. This equation depends on several complicated correlation functions and thus is difficult to handle. Fortunately, we find that these correlation functions obey a number of exact identities, which allows us to prove that the DS equation of full fermion propagator is self-closed. After solving this self-closed equation, we obtain the renormalized fermion velocity and show that its energy (momentum) dependence of renormalized fermion velocity is dominantly determined by the electron-phonon (Coulomb) interaction. In particular, the renormalized velocity exhibits a logarithmic momentum dependence and a non-monotonic energy dependence.

Inhomogeneity Effects on Reactions in Supercritical Fluids: A Computational Study on the Pyrolysis of n-Decane.

Supercritical fluids exhibit peculiar inhomogeneity, which strongly affects reaction behaviors in them. However, explanations for inhomogeneity and its effect on reactions are both ambiguous so far. Here, we provide an atomic-level understanding of inhomogeneity effects on reactions via the computational method, with the example of n-decane pyrolysis under supercritical conditions. We describe the characteristic pyrolysis behaviors through collective variable-driven hyperdynamics (CVHD) simulations and explain the inhomogeneity of supercritical n-decane as the coexistence of gas-like and liquid-like atoms by a trained machine learning classifier. Due to their specific local environment, the appearance of liquid-like atoms under supercritical conditions significantly increases the type and frequency of bimolecular reactions and eventually causes changes in product distributions. Future research with this method is expected to extend the effect of inhomogeneity on other reactions under supercritical conditions or other condensed phases.

Revealing the promotion of carbonyl groups on vacancy stabilized Pt4/nanocarbons for propane dehydrogenation.

Nanocarbons are promising supports for Pt clusters applied in propane dehydrogenation (PDH), owing to their large surface areas and tunable chemical properties. The vacancies and oxygen-containing groups (OCGs) in nanocarbons can enhance catalytic performance by tailoring the coordination environment of Pt clusters. Herein, 46 nanocarbons with coexisting vacancies and OCGs were designed to support Pt clusters, of which the influences on PDH were revealed by density functional theory calculations. Nanocarbons with divacancies (V2) and CO edge groups were screened out as the most appropriate support for Pt clusters in PDH. Due to the V2, tetrahedral Pt clusters were distorted into three-layered configurations, contributing to enhanced binding strength and a favorable reactive pathway starting from the methylene group in propane. This changed the rate-determining step to the first C-H bond scission with a low energy barrier. The introduction of CO edge groups coexisting with V2 further improved the stabilization of Pt clusters, resulting from the increased electron transfer from Pt atoms to C atoms. The abilities to break C-H bonds and inhibit C-C bond cracking were also enhanced as compared to the nanocarbons with only V2. Therefore, this work provides references on the regulation of vacancies and OCGs in carbon-based catalysts.

Tailoring surface carboxyl groups of mesoporous carbon boosts electrochemical H2O2 production.

Oxygen-doped porous carbon materials have been shown promising performance for electrochemical two-electron oxygen reduction reaction (2e- ORR), an efficient approach for the safe and continuous on-site generation of H2O2. The regulation and mechanism understanding of active oxygen-containing functional groups (OFGs) remain great challenges. Here, OFGs modified porous carbon were prepared by thermal oxidation (MC-12-Air), HNO3 oxidation (MC-12-HNO3) and H2O2 solution hydrothermal treatment (MC-12-H2O2), respectively. Structural characterization showed that the oxygen doping content of three catalysts reached about 20%, with the almost completely maintained specific surface area (exception of MC-12- HNO3). Spectroscopic characterization further revealed that hydroxyl groups are mainly introduced into MC-12-Air, while carboxyl groups are mainly introduced into MC-12- HNO3 and MC-12- H2O2. Compared with the pristine catalyst, three oxygen-functionalized catalysts showed enhanced activity and H2O2 selectivity in 2e- ORR. Among them, MC-12-H2O2 exhibited the highest catalytic activity and selectivity of 94 %, as well as a considerable HO2- accumulation of 46.2 mmol L-1 and excellent stability in an extended test over 36 h in a H-cell. Electrochemical characterization demonstrated the promotion of OFGs on ORR kinetics and the greater contribution of carboxyl groups to the intrinsically catalytic activity. DFT calculations confirmed that the electrons are transferred from carboxyl groups to adjacent carbon and the enhanced adsorption strength toward *OOH intermediate, leading to a lower energy barrier for forming *OOH on carboxyl terminated carbon atoms.

Decomposition Mechanism of Isoprenoid Hydrocarbon p-Menthane in the Presence of Pt@FGS Nanoparticles: A ReaxFF-MD Study.

Pt@FGS nanoparticles have shown effective enhancement in the decomposition of hydrocarbon fuels. To further explore the potential enhancing mechanisms of Pt@FGS nanoparticles, the catalytic decomposition of p-menthane, a bioderived isoprenoid "drop-in" fuel with great promise, is investigated here using the reactive force-field molecular dynamics (ReaxFF-MD) simulations. The results show that the Pt@FGS nanoparticles exhibit good catalytic reactivity with a reduction of the activation energy by nearly 62%. Possible initial reactions of enhanced p-menthane (PMT) decomposition are discussed, which suggests that the supported Pt-cluster plays a key role in the dehydrogenation of PMT, as does the oxygen-containing functional group of the functionalized graphene sheets (FGS). It is also interesting to note that the presence of Pt@FGS causes the initial reactions, which are dominated by H-abstraction, favorable in both kinetics and thermodynamics.

Inhibiting Coking of Lubricating Oil and Thermal Insulation of Bearing Chamber Wall by TCA-2 Nitride Ceramic Coating.

High temperature around the rear bearing chamber of supersonic aero-engines often causes the coking of the lubricating oil on the shaft end cover. To figure out the problem, a method using the TCA-2 nitride ceramic coating with the thickness of several micrometers is proposed. A simulation experiment method of lubricating oil coking for high-temperature parts is developed, and the anticoking performance of the samples with the coating is studied. The results showed that the TCA-2 coating inhibits the coke of lubricating oil on the metal surface within a certain temperature range by about 40.7% under the 500 °C attributed to the decrease in the surface activity of high-temperature metal and increase in the heat resistance. The TCA-2 coating also shows good compatibility with the lubricating oil since the acid value change of lubricating oil decreases after the thermal oxidation experiment. The TCA-2 coating can effectively reduce the surface temperature of the oil side.

Tailoring the catalytic performance of single platinum anchored on graphene by vacancy engineering for propane dehydrogenation: a theoretical study.

Propane dehydrogenation (PDH) is an effective approach to produce propylene. Downsizing the Pt species to single atom catalysts (SACs) has become a hotspot, owing to the maximum utilization and excellent catalytic behavior. However, the agglomeration of SACs is the decisive limitation for high temperature PDH. Herein, single Pt atoms were anchored on graphene with different types of vacancies, and their catalytic performances on PDH were explored based on density functional theory (DFT). As the vacancy size increased, the catalytic activity decreased. It was because the combined site of the detached H atom in propane would transfer from the Pt atom to the C atom around vacancies, thus increasing the migration distance and lowering the activity. However, with the increase of vacancy size, the selectivity to propylene was improved, owing to the enhanced repulsion between C atoms in graphene and propylene. Therefore, instead of stabilizing the single atom, vacancies in carbon materials can also tailor the catalytic performance by geometric disturbance. This fundamental work opens up the possibility for purposeful SAC design in PDH.

Template Guiding for the Encapsulation of Uniformly Subnanometric Platinum Clusters in Beta-Zeolites Enabling High Catalytic Activity and Stability.

Subnanometric metal clusters have attracted extensive attention because of their unique properties as heterogeneous catalysts. However, it is challenging to obtain uniformly distributed metal clusters under synthesis and reaction conditions. Herein, we report a template-guidance protocol to synthesize subnanometric metal clusters uniformly encapsulated in beta-zeolite, with the metal ions anchored to the internal channels of the zeolite template via electrostatic interactions. Pt metal clusters with a narrow size range of 0.89 to 1.22 nm have been obtained on the intersectional sites of beta-zeolite (Pt@beta) with a broad range of Si/Al molar ratios (15-200). The uniformly distributed Pt clusters in Pt@H-beta are subject to strong electron withdrawal by the zeolite, which promotes transfer of active hydrogen, providing excellent activity and stability in hydrodeoxygenation reactions. A general strategy is thus proposed for the encapsulation of subnanometric metal clusters in zeolites with high thermal stability.

Subsurface-Regulated PtGa Nanoparticles Confined in Silicalite-1 for Propane Dehydrogenation.

Propane dehydrogenation (PDH) is one of the most promising techniques to produce propylene. Industrial Pt-based catalysts often suffer from short-time stability under high temperature due to serious sintering and coke deposition via undesired side reactions. Detailed reaction mechanism on the surface of Pt-based nanoparticle has been well studied, while the subsurface effect remains mostly unstudied. Herein, supported PtGa nanoparticles with different surface and subsurface composition was evidenced by extended X-ray absorption fine structure (EAXFS) spectra and energy dispersive X-ray spectroscopy (EDS). Theoretical simulation demonstrated subsurface regulation would increase the electron density of surface Pt and thus weaken propylene adsorption. Propylene selectivity on the PtGa-subsurface nanoparticles was up to 98% at 600 °C while that on the Pt-subsurface nanoparticles was only 95%. Furthermore, rational designed PtGa alloy nanoparticles were encapsulated in MFI zeolite to inhibit sintering and coke deposition for enhanced catalytic stability.