Phosphotungstic Acid Clusters Decorated Znln2S4 Nanoflowers as Molecular-Scale S-Scheme Heterojunctions for Simultaneous H2 Evolution and Benzyl Alcohol Upgrading.

Simultaneous utilization of photogenerated electrons and holes to achieve overall redox reactions is attractive but still far from practical application. The emerging step (S)-scheme mechanism has proven to be an ideal approach to inhibit charge recombination and supply photoinduced charges with highest redox potentials. Herein, a hierarchical phosphotungstic acid (H3PW12O40, HPW)@Znln2S4 (ZISW) heterojunction was prepared through one-pot hydrothermal method for simultaneous hydrogen (H2) evolution and benzyl alcohol upgrading. The fabricated HPW-based heterojunctions indicated much enhanced visible-light absorption, promoted photogenerated charge transfer and inhibited charge recombination, owing to hierarchical architecture based on visible-light responsive Znln2S4 microspheres, and S-scheme charge transfer pathway. The S-scheme mechanism was further verified by free-radical trapping electron spin resonance (ESR) spectra. Moreover, the wettability of composite heterojunction was improved by the modification of hydrophilic HPW, contributing to gaining active hydrogen (H+) from water sustainably. The optimal ZISW-30 heterojunction photocatalyst indicated an enhanced hydrogen evolution rate of 27.59 mmol g-1 h-1 in benzyl alcohol (10 vol. %) solution under full-spectrum irradiation, along with highest benzaldehyde production rate is 8.32 mmol g-1 h-1. This work provides a promising guideline for incorporating HPW into S-scheme heterojunctions to achieve efficient overall redox reactions.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [18F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [18F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing. Two radiologists segmented and labelled foci as suspicious or non-suspicious for malignancy. A DL-based segmentation was developed with two independent CNNs. An anatomical prior guidance was applied to make the DL framework focus on PSMA-avid lesions. Segmentation performance was evaluated by Dice, IoU, precision, and recall. Classification model was constructed with multi-modal decision fusion framework evaluated by accuracy, AUC, F1 score, precision, and recall. Automatic segmentation of suspicious lesions was improved under prior guidance, with mean Dice, IoU, precision, and recall of 0.700, 0.566, 0.809, and 0.660 on the internal test set and 0.680, 0.548, 0.749, and 0.740 on the external test set. Our multi-modal decision fusion framework outperformed single-modal and multi-modal CNNs with accuracy, AUC, F1 score, precision, and recall of 0.764, 0.863, 0.844, 0.841, and 0.847 in distinguishing suspicious and non-suspicious foci on the internal test set and 0.796, 0.851, 0.865, 0.814, and 0.923 on the external test set. DL-based lesion segmentation on PSMA PET is facilitated through our anatomical prior guidance strategy. Our classification framework differentiates suspicious foci from those not suspicious for cancer with good accuracy.

Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.

Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the most informative images using real-world clinical datasets for brain tumor segmentation and proposing a framework that minimizes the data needed while maintaining performance. Then, 638 multi-institutional brain tumor magnetic resonance imaging scans were used to train three-dimensional U-net models and compare active learning strategies. Uncertainty estimation techniques including Bayesian estimation with dropout, bootstrapping, and margins sampling were compared to random query. Strategies to avoid annotating similar images were also considered. We determined the minimum data necessary to achieve performance equivalent to the model trained on the full dataset (α = 0.05). Bayesian approximation with dropout at training and testing showed results equivalent to that of the full data model (target) with around 30% of the training data needed by random query to achieve target performance (p = 0.018). Annotation redundancy restriction techniques can reduce the training data needed by random query to achieve target performance by 20%. We investigated various active learning strategies to minimize the annotation burden for three-dimensional brain tumor segmentation. Dropout uncertainty estimation achieved target performance with the least annotated data.

Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images.

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4-1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Catalytic hydrolysis of carbonyl sulfide in blast furnace gas over Sm-Ce-Ox@ZrO2 catalyst.

Carbonyl sulfur (COS) is a prominent organic sulfur pollutant commonly found in the by-product gas generated by the steel industry. A series of Sm-doped CeOx@ZrO2 catalysts were prepared for the hydrolysis catalytic removal of COS. The results showed that the addition of Sm resulted in the most significant enhancement of hydrolysis catalytic activity. The 3% Sm2O3-Ce-Ox@ZrO2 catalyst exhibited the highest activity, achieving a hydrolysis catalytic efficiency of 100% and H2S selectivity of 100% within the temperature range of 90-180 °C. The inclusion of Sm had the effect of reducing the acidity of the catalyst while increasing weak basic sites, which facilitated the adsorption and activation of COS molecules at low temperatures. Appropriate doping of Sm proved beneficial in converting active surface chemisorbed oxygen into lattice oxygen, thereby decreasing the oxidation of intermediate products and maintaining the stability of the hydrolysis reaction.

Optimizing oxygen vacancies through grain boundary engineering to enhance electrocatalytic nitrogen reduction.

Electrocatalytic nitrogen reduction is a challenging process that requires achieving high ammonia yield rate and reasonable faradaic efficiency. To address this issue, this study developed a catalyst by in situ anchoring interfacial intergrown ultrafine MoO2 nanograins on N-doped carbon fibers. By optimizing the thermal treatment conditions, an abundant number of grain boundaries were generated between MoO2 nanograins, which led to an increased fraction of oxygen vacancies. This, in turn, improved the transfer of electrons, resulting in the creation of highly active reactive sites and efficient nitrogen trapping. The resulting optimal catalyst, MoO2/C700, outperformed commercial MoO2 and state-of-the-art N2 reduction catalysts, with NH3 yield and Faradic efficiency of 173.7 µg h-1 mg-1cat and 27.6%, respectively, under - 0.7 V vs. RHE in 1 M KOH electrolyte. In situ X-ray photoelectron spectroscopy characterization and density functional theory calculation validated the electronic structure effect and advantage of N2 adsorption over oxygen vacancy, revealing the dominant interplay of N2 and oxygen vacancy and generating electronic transfer between nitrogen and Mo(IV). The study also unveiled the origin of improved activity by correlating with the interfacial effect, demonstrating the big potential for practical N2 reduction applications as the obtained optimal catalyst exhibited appreciable catalytic stability during 60 h of continuous electrolysis. This work demonstrates the feasibility of enhancing electrocatalytic nitrogen reduction by engineering grain boundaries to promote oxygen vacancies, offering a promising avenue for efficient and sustainable ammonia production.

DE-JANet: A unified network based on dual encoder and joint attention for Alzheimer's disease classification using multi-modal data.

Structural magnetic resonance imaging (sMRI), which can reflect cerebral atrophy, plays an important role in the early detection of Alzheimer's disease (AD). However, the information provided by analyzing only the morphological changes in sMRI is relatively limited, and the assessment of the atrophy degree is subjective. Therefore, it is meaningful to combine sMRI with other clinical information to acquire complementary diagnosis information and achieve a more accurate classification of AD. Nevertheless, how to fuse these multi-modal data effectively is still challenging. In this paper, we propose DE-JANet, a unified AD classification network that integrates image data sMRI with non-image clinical data, such as age and Mini-Mental State Examination (MMSE) score, for more effective multi-modal analysis. DE-JANet consists of three key components: (1) a dual encoder module for extracting low-level features from the image and non-image data according to specific encoding regularity, (2) a joint attention module for fusing multi-modal features, and (3) a token classification module for performing AD-related classification according to the fused multi-modal features. Our DE-JANet is evaluated on the ADNI dataset, with a mean accuracy of 0.9722 and 0.9538 for AD classification and mild cognition impairment (MCI) classification, respectively, which is superior to existing methods and indicates advanced performance on AD-related diagnosis tasks.

Oxygen vacancy modulation in interfacial engineering Fe3O4 over carbon nanofiber boosting ambient electrocatalytic N2 reduction.

The oxygen vacancy modulation of interface-engineered Fe3O4 nanograins over carbon nanofiber (Fe@CNF) was achieved to improve electrocatalytic nitrogen reduction reaction (NRR) activity and stability via facile electrospinning and tuning thermal procedure. The optimal catalyst calcined at 800 ℃ (Fe@CNF-800) was endowed with abundant nanograin boundaries and optimized oxygen vacancy (Vo) concentration of iron oxides, thereby affording 37.1 µg h-1 mgcat.-1 (-0.2 V vs. reversible hydrogen electrode (RHE)) NH3 yield and rational Faraday efficiency (10.2%), with 13.6 times atomic activity enhancement compared to of that commercial Fe3O4. The interfacial effect of assembled nanograins in particles correlated with the formation of Vo and more intrinsic active sites, which is conducive to the trapping and activation of nitrogen (N2). The in-situ X-ray photoelectron spectroscopy (XPS) measurement revealed the real consumption of adsorbed oxygen when introducing N2 by the trapping effect of Vo. Density-Functional-Theory (DFT) calculation validates the promotive hydrogenation effect and elimination of hydrogen intermediate (H*) interacted with N2 transferring toward oxygen of the support. The optimal catalyst shows a lasting NRR activity at least 90 h, outperforming most reported Fe-based NRR catalysts.

Ternary dual S-scheme In2O3/SnIn4S8/CdS heterojunctions for boosted light-to-hydrogen conversion.

Developing artificial S-scheme systems with highly active catalysts is significant to long-term solar-to-hydrogen conversion. Herein, CdS nanodots-modified hierarchical In2O3/SnIn4S8 hollow nanotubes were synthesized by an oil bath method for water splitting. Benefiting from the synergy among the hollow structure, tiny size effect, matched energy level positions, and abundant coupling heterointerfaces, the optimized nanohybrid attains an impressive photocatalytic hydrogen evolution rate of 110.4 µmol/h, and the corresponding apparent quantum yield reaches 9.7% at 420 nm. On In2O3/SnIn4S8/CdS interfaces, the migration of photoinduced electrons from both CdS and In2O3 to SnIn4S8via intense electronic interactions contributes to the ternary dual S-scheme modes, which are beneficial to promote faster spatial charge separation, deliver better visible light-harvesting ability, and provide more reaction active sites with high potentials. This work reveals protocols for rational design of on-demand S-scheme heterojunctions for sustainably converting solar energy into hydrogen in the absence of precious metals.

Improvement of sleep quality in isolated metastatic patients with spinal cord compression after surgery.

BACKGROUND: This study aimed to assess changes in quality of sleep (QoS) in isolated metastatic patients with spinal cord compression following two different surgical treatments and identify potential contributing factors associated with QoS improvement. METHODS: We reviewed 49 patients with isolated spinal metastasis at our spinal tumor center between December 2017 and May 2021. Total en bloc spondylectomy (TES) and palliative surgery with postoperative stereotactic radiosurgery (PSRS) were performed on 26 and 23 patients, respectively. We employed univariate and multivariate analyses to identify the potential prognostic factors affecting QoS. RESULTS: The total Pittsburgh Sleep Quality Index (PSQI) score improved significantly 6 months after surgery. Univariate analysis indicated that age, pain worsening at night, decrease in visual analog scale (VAS), increase in Eastern Cooperative Oncology Group performance score (ECOG-PS), artificial implant in focus, and decrease in epidural spinal cord compression (ESCC) scale values were potential contributing factors for QoS. Multivariate analysis indicated that the ESCC scale score decreased as an independent prognostic factor. CONCLUSIONS: Patients with spinal cord compression caused by the metastatic disease had significantly improved QoS after TES and PSRS treatment. Moreover, a decrease in ESCC scale value of > 1 was identified as a favorable contributing factor associated with PSQI improvement. In addition, TES and PSRS can prevent recurrence by achieving efficient local tumor control to improve indirect sleep. Accordingly, timely and effective surgical decompression and recurrence control are critical for improving sleep quality.

Wastewater preinteraction accelerates the photoaging of disposable box-derived polystyrene microplastics in water.

Before being discharged into natural environment, almost all of microplastics (MPs) interact with wastewater constituents in wastewater treatment plants (WWTPs). This study investigated the photoaging of disposable box-derived polystyrene (PS) mediated by real wastewater by simulating the case flowing from WWTPs to natural water. Results showed that wastewater influent pretreatment significantly enhanced the photoaging of PSMPs through the sorption of wastewater constituents, e.g., 2.02 times of increase in photooxidation after 30 d of ultraviolet irradiation. Fulvic acid was identified as the leading contributor for the enhancing effect of wastewater relative to other wastewater constituents such as Cl, CO32-, NO3- and clay particles. In-depth mechanism analysis showed that the observed enhancement was critically controlled by the photosensitization effect of wastewater itself and the enhanced utilization of PSMPs for ultraviolet energy. Specifically, various sorbed wastewater constituents can not only generate higher concentrations of •OH and O2⋅- than clean MPs without constituents, but also reinforce the utilization of PSMPs for light energy due to the increased dispersion in solution by increasing hydrophilicity and surface charges. Also, the light-shielding effect was induced by wastewater, but was less important. This study bridges wastewater source and MP aging and fate and suggests the shortened lifetime of (micro)plastic samples via WWTP input to deepen our understanding of MP pollution in the environment.

Retinal Vessel Segmentation Based on B-COSFIRE Filters in Fundus Images.

Retinal vessel extraction plays an important role in the diagnosis of several medical pathologies, such as diabetic retinopathy and glaucoma. In this article, we propose an efficient method based on a B-COSFIRE filter to tackle two challenging problems in fundus vessel segmentation: (i) difficulties in improving segmentation performance and time efficiency together and (ii) difficulties in distinguishing the thin vessel from the vessel-like noise. In the proposed method, first, we used contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, then excerpted region of interest (ROI) by thresholding the luminosity plane of the CIELab version of the original RGB image. We employed a set of B-COSFIRE filters to detect vessels and morphological filters to remove noise. Binary thresholding was used for vessel segmentation. Finally, a post-processing method based on connected domains was used to eliminate unconnected non-vessel pixels and to obtain the final vessel image. Based on the binary vessel map obtained, we attempt to evaluate the performance of the proposed algorithm on three publicly available databases (DRIVE, STARE, and CHASEDB1) of manually labeled images. The proposed method requires little processing time (around 12 s for each image) and results in the average accuracy, sensitivity, and specificity of 0.9604, 0.7339, and 0.9847 for the DRIVE database, and 0.9558, 0.8003, and 0.9705 for the STARE database, respectively. The results demonstrate that the proposed method has potential for use in computer-aided diagnosis.

Poly(azure C)-coated CoFe Prussian blue analogue nanocubes for high-energy asymmetric supercapacitors.

Prussian blue analogues are considered as promising supercapacitor electrode materials due to their high theoretical capacitance and low cost. Yet, they suffer from poor electronic conductivity and cycling life. Here, a redox dye polymer, poly(azure C) (PAC), is in-situ grown uniformly on CoFe Prussian blue analogue (CoFePBA). As a polymer mediator, the PAC coating on each PBA not only enhances the electronic conductivity and surface area, but also improves the structural stability and specific capacitance of PBA. As a result, the optimized CoFePBA@PAC possesses ultrahigh specific capacitance (968.67 F g-1 at 1 A g-1), superior rate performance (665.78 F g-1 at 10 A g-1), and excellent long-cycling stability (92.45% capacity retention after 2000 cycles). As an application, a fabricated CoFePBA@PAC//AC asymmetric supercapacitor (AC = activated carbon) maintains 84.7% capacitance retention in 2000 cycles at 1 A g-1 and displays a superior specific energy of 29.16 W h kg-1 at the power density of 799.78 W kg-1. These results demonstrate that redox dye polymer-coated PBAs with outstanding performance have a promising prospect in the field of energy storage.

Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI.

With modern management of primary liver cancer shifting towards non-invasive diagnostics, accurate tumor classification on medical imaging is increasingly critical for disease surveillance and appropriate targeting of therapy. Recent advancements in machine learning raise the possibility of automated tools that can accelerate workflow, enhance performance, and increase the accessibility of artificial intelligence to clinical researchers. We explore the use of an automated Tree-Based Optimization Tool that leverages a genetic programming algorithm for differentiation of the two common primary liver cancers on multiphasic MRI. Manual and automated analyses were performed to select an optimal machine learning model, with an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 70-75% (95% CI 0.48-0.89), and specificity of 71-79% (95% CI 0.52-0.90) on manual optimization, and an accuracy of 73-75% (95% CI 0.59-0.85), sensitivity of 65-75% (95% CI 0.43-0.89) and specificity of 75-79% (95% CI 0.56-0.90) for automated machine learning. We found that automated machine learning performance was similar to that of manual optimization, and it could classify hepatocellular carcinoma and intrahepatic cholangiocarcinoma with an sensitivity and specificity comparable to that of radiologists. However, automated machine learning performance was poor on a subset of scans that met LI-RADS criteria for LR-M. Exploration of additional feature selection and classifier methods with automated machine learning to improve performance on LR-M cases as well as prospective validation in the clinical setting are needed prior to implementation.

S-scheme photocatalysis induced by ZnIn2S4 nanoribbons-anchored hierarchical CeO2 hollow spheres for boosted hydrogen evolution.

Developing S-scheme systems with impressive photocatalytic performance is of huge meaning in realizing the long-term conversion of solar energy into hydrogen. Herein, ZnIn2S4 nanoribbons were integrated with hierarchical CeO2 hollow spheres to construct heterostructure using an oil bath approach under mild conditions. The optimized CeO2/ZnIn2S4 presented a superior photocatalytic hydrogen production rate of 69 µmol/h, which is about 4.9 and 11.5 times greater than pristine ZnIn2S4 and CeO2, respectively. In addition, its apparent quantum yield achieved 7.6% at 420 nm. The improved photoactivity of the CeO2/ZnIn2S4 heterojunction can be referable to the cooperative effects of the aligned bandgap structures, strong visible-light-harvesting capacity, and interfacial interactions via the internal electric field. This study provides insights into the protocols for rational design of S-scheme heterojunction catalysts for high-efficiency hydrogen evolution via sustainable photocatalytic water splitting.

Yeast cell death pathway requiring AP-3 vesicle trafficking leads to vacuole/lysosome membrane permeabilization.

Unicellular eukaryotes have been suggested as undergoing self-inflicted destruction. However, molecular details are sparse compared with the mechanisms of programmed/regulated cell death known for human cells and animal models. Here, we report a molecular cell death pathway in Saccharomyces cerevisiae leading to vacuole/lysosome membrane permeabilization. Following a transient cell death stimulus, yeast cells die slowly over several hours, consistent with an ongoing molecular dying process. A genome-wide screen for death-promoting factors identified all subunits of the AP-3 complex, a vesicle trafficking adapter known to transport and install newly synthesized proteins on the vacuole/lysosome membrane. To promote cell death, AP-3 requires its Arf1-GTPase-dependent vesicle trafficking function and the kinase Yck3, which is selectively transported to the vacuole membrane by AP-3. Video microscopy revealed a sequence of events where vacuole permeability precedes the loss of plasma membrane integrity. AP-3-dependent death appears to be conserved in the human pathogenic yeast Cryptococcus neoformans.

Highly dispersive Ru confined in porous ultrathin g-C3N4 nanosheets as an efficient peroxymonosulfate activator for removal of organic pollutants.

Ru species were loaded on a two-dimensional (2D) material of graphitic carbon nitride (2D g-C3N4) to serve as the efficient AOP catalysts. The catalytic activity was closely related to the dispersion degree of Ru, as determined by the inherent nanoarchitecture of the supporting material. Ultrathin g-C3N4 nanosheets with a unique porous structure were fabricated by further thermally oxidizing and etching bulk g-C3N4 (bCN) in air. Homogeneous dispersion of Ru species was successfully achieved on the porous few-layered g-C3N4 nanosheets (pCN) by stirring, washing, freeze drying and annealing processes to obtain Ru-pCN catalysts, whereas bCN or multilayered g-C3N4 (mCN) led to the aggregation of Ru nanoparticles in Ru-bCN and Ru-mCN materials. The conventional impregnation method also caused the resulting Ru-pCN-imp catalyst with undesirable Ru aggregation in spite of employing pCN. The optimal 4.4Ru-pCN removed 100% of 2,4,6-trichlorophenol (TCP) within only 3 min, superior to its counterpart samples, and exhibited remarkable degradation efficiencies for methyl orange, neutral red, 4-chlorophenol, tetracycline and oxytetracycline. Mechanistic studies suggested that four radicals, e.g., •OH, SO4• -, O2• - and 1O2 were generated during the peroxymonosulfate (PMS) activation, in which SO4• - and 1O2 played a major role.

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

Unsupervised Heterogeneous Coupling Learning for Categorical Representation.

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

Mesoporous poorly crystalline α-Fe2O3 with abundant oxygen vacancies and acid sites for ozone decomposition.

In this work, mesoporous poorly crystalline hematite (α-Fe2O3) was prepared using mesoporous silica (KIT-6) functionalized with 3-[(2-aminoethyl)amino]propyltrimethoxysilane as a hard template (SMPC-α-Fe2O3). The disordered atomic arrangement structure of SMPC-α-Fe2O3 promoted the formation of oxygen vacancies, which was confirmed using X-ray photoelectron spectroscopy (XPS), O2-temperature-programmed desorption (TPD), H2-temperature-programmed reduction (TPR), and in situ diffuse reflectance infrared Fourier transform (DRIFT) analyses. Density functional theory calculations (DFT) also proved that reducing the crystallinity of α-Fe2O3 decreased the formation energy of oxygen vacancies. TPD and in situ DRIFT analyses of NH3 adsorption suggested that the surface acidity of SMPC-α-Fe2O3 was considerably higher than those of mesoporous poorly crystalline α-Fe2O3 (MPC-α-Fe2O3) and highly crystalline α-Fe2O3 (HC-α-Fe2O3). The oxygen vacancies and acid sites formed on α-Fe2O3 surface are beneficial for ozone (O3) decomposition. Compared with MPC-α-Fe2O3 and HC-α-Fe2O3, SMPC-α-Fe2O3 exhibited a higher removal efficiency for 200-ppm O3 at a space velocity of 720 L g-1 h-1 at 25 ± 2 °C under dry conditions. Additionally, in situ DRIFT and XPS results suggested that the accumulation of peroxide (O22-) and the conversion of O22- to lattice oxygen over the oxygen vacancies caused catalyst deactivation. However, O22- could be desorbed completely by continuous N2 purging at approximately 350 °C. This study provides significant insights for developing highly active α-Fe2O3 catalysts for O3 decomposition.