Transition metal oxides/noble metal (TMOs/NM) nanocomposites are one kind of important material for semiconductor gas sensors. The controllable construction of a highly connected mesoporous structure ...and easily accessible active sites is essential for gas sensing performance but remains a great challenge. Herein, a soluble mercapto phenolic resin polymer mediated co‐assembly approach is proposed for the construction of ordered dual mesoporous structure and the simultaneous loading of highly dispersed noble metal nanoparticles. The home‐made soluble mercapto phenolic resin polymer enabled the co‐assembly of transition metal precursors, noble metal precursors, and poly(ethylene oxide)‐block‐polystyrene (PEO‐b‐PS) micelles, resulting in a straightforward synthesis of ordered dual‐mesoporous TMOs/NM nanocomposites (e.g., WO3/Au, TiO2/Au, NbOx/AuPd). As proof of the concept, the synthesized dual‐mesoporous WO3/Au materials are applied for sensing of 3‐hydroxy‐2‐butanone, a biomarker of food‐borne pathogenic bacteria Listeria monocytogenes. The sensors exhibit high sensitivity (Ra/Rg = 18.8 to 2.5 ppm) and high selectivity based on their noble metal sensitization and superior mesopore connectivity for gas diffusion. Furthermore, the synthesized gas sensors are integrated into a wireless sensing module connected to a smartphone, providing a rapid and convenient real‐time detection of 3‐hydroxy‐2‐butanone.
A general one‐pot synthesis of ordered dual‐mesoporous transition metal oxides/noble metal nanocomposites is developed using a mercapto phenolic resin polymer as a multifunctional bridging molecule, which can realize multi‐component co‐assembly, stabilize the noble metal nanoparticles, and be a template for a secondary channel. The ordered dual‐mesoporous WO3/Au realizes a rapid and convenient detection of 3‐hydroxy‐2‐butanone, a biomarker of Listeria monocytogenes.
Bifunctional core-shell magnetic fluorescent microspheres have been synthesized via a facile interface Pechini-type sol-gel method. Benefited from the uniform core-shell structure, large ...magnetization, and strong fluorescence emission, the Fe3O4@YVO4:Eu3+ microspheres were successfully applied as effective and sensitive markers to recognize latent fingerprint on a variety of substrates with fine lines, low background interference, high contrast, and clear detail features of 3 levels, indicating promising application in personal identification.
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Latent fingerprint recognition technique has received increasing attention because it helps to precisely identify human information for many applications. In this study, bifunctional core-shell magnetic fluorescent microspheres have been synthesized via a facile interface Pechini-type sol-gel method using citric acid and polyethylene glycol as chelating agent and cross-linking agent, respectively. The obtained Fe3O4@YVO4:Eu3+ microspheres possess a typical core-shell structure, large magnetization, and strong fluorescence emission. The surface morphology and roughness of the microspheres can be flexibly tuned by controlling the multistep interface deposition process and subsequent calcination temperatures. Due to their well-integrated bifunctionalities, these magnetic fluorescent microspheres show outstanding performance in the visualization of latent fingerprints on various substrates with high definition and excellent anti-interference, and therefore they have great potential for application in identity recognition.
Semiconducting metal oxides‐based gas sensors with the capability to detect trace gases at low operating temperatures are highly desired in applications such as wearable devices, trace pollutant ...detection, and exhaled breath analysis, but it still remains a great challenge to realize this goal. Herein, a multi‐component co‐assembly method in combination with pore engineering strategy is proposed. By using bi‐functional (3‐mercaptopropyl) trimethoxysilane (MPTMS) that can co‐hydrolyze with transition metal salt and meanwhile coordinate with gold precursor during their co‐assembly with PEO‐b‐PS copolymers, ordered mesoporous SiO2–WO3 composites with highly dispersed Au nanoparticles of 5 nm (mesoporous SiO2–WO3/Au) are straightforward synthesized. This multi‐component co‐assembly process avoids the aggregation of Au nanoparticles and pore blocking in conventional post‐loading method. Furthermore, through controlled etching treatment, a small portion of silica can be removed from the pore wall, resulting in mesoporous SiO2–WO3/Au with increased specific surface area (129 m2 g−1), significantly improved pore connectivity, and enlarged pore window (>4.3 nm). Thanks to the presence of well‐confined Au nanoparticles and ε‐WO3, the mesoporous SiO2–WO3/Au based gas sensors exhibit excellent sensing performance toward ethanol with high sensitivity (Ra/Rg = 2–14 to 50–250 ppb) at low operating temperature (150 °C).
Highly dispersed Au nanoparticles decorated ordered mesoporous SiO2–WO3 hybrid materials with improved pore connectivity and increased specific surface area is synthesized via a solvent evaporation induced multi‐component co‐assembly method combined with pore engineering strategy. The hybrid materials show excellent ultratrace (ppb‐level) ethanol sensing with a high sensitivity at low operating temperature.
Surface roughness endows microspheres with unique and useful features and properties like improved hydrophobicity, enhanced adhesion, improved stability at the oil–water interface, and superior cell ...uptake properties, thus expanding their applications. Core–shell magnetic mesoporous microspheres combine the advantages of magnetic particles and mesoporous materials and have exhibited wide applications in adsorption, catalysis, separation, and drug delivery. In this study, virus-like rough core–shell–shell-structured magnetic mesoporous organosilica (denoted as RMMOS) microspheres with controllable surface roughness were successfully obtained through electrostatic interaction-directed interface co-assembly. The obtained RMMOS microspheres possess uniform spherical morphology with tunable surface roughness, radially aligned pore channels with a diameter of 3.0 nm in the outer organosilica shell, high specific surface area (396 m2/g), large pore volume (0.66 cm3/g), high magnetization (35.1 emu/g), and superparamagnetic property. The RMMOS microspheres serve as desirable candidates to support Au nanoparticles (2.5 nm) and show superior catalytic activity and excellent stability in hydrogenation of 4-nitrophenol. In addition, the RMMOS microspheres modified with carboxylic groups further displayed promising performance in convenient adsorption removal of dyes in polluted water.
•A new method for big social data analysis to reveal traveller’ behavior is presented.•EM clustering approach is used for customers’ segmentation.•Neural network combined with fuzzy logic is used for ...preference prediction.•The method uses HOSVD for data dimensionality reduction.•The method is evaluated on eco-friendly hotels data.
Sustainable tourism is an emerging trend around the world. Eco-friendly (green) hotels are environmentally friendly properties that are becoming more popular among green travellers. Electronic Word-of-Mouth (e-WOM) is a method of communicating with customers to share their experiences and is a powerful marketing tool for hotel marketing. This paper investigates the role of online reviews of eco-friendly hotels for preference learning using multi-criteria decision-making and machine learning techniques. We develop a new method using multi-criteria decision making, supervised and unsupervised learning techniques. The Expectation-Maximization (EM) algorithm is used as an unsupervised learning technique to cluster travellers’ online reviews. We use the Higher-Order Singular-Value Decomposition technique along with a similarity measure to find the most similar customers based on their preference. To predict travellers’ preference for eco-friendly hotels, we employ a neuro-fuzzy system, the Adaptive Neuro-Fuzzy Inference System, as a supervised learning technique. To select the most important criteria, we use the entropy-weight approach in each segment. Several experiments were performed on the collected data from the Czech Republic's eco-friendly hotels on the TripAdvisor platform. The results demonstrated that the hybrid approach is effective for customers’ segmentation, and preference learning and prediction in eco-friendly hotels.
Abstract Microgrids are small-scale energy system that supplies power to homes, businesses, and industries. Microgrids can be considered as a trending technology in energy fields due to their power ...to supply reliable and sustainable energy. Microgrids have a mode called the island, in this mode, microgrids are disconnected from the major grid and keep providing energy in the situation of an energy outage. Therefore, they help the main grid during peak energy demand times. The microgrids can be connected to the network, which is called networked microgrids. It is possible to have flexible energy resources by using their enhanced energy management systems. However, connection microgrid systems to the communication network introduces various challenges, including increased in systems complicity and noise interference. Integrating network communication into a microgrid system causes the system to be susceptible to noise, potentially disrupting the critical control signals that ensure smooth operation. Therefore, there is a need for predicting noise caused by communication network to ensure the operation stability of microgrids. In addition, there is a need for a simulation model that includes communication network and can generate noise to simulate real scenarios. This paper proposes a classifying model named Noise Classification Simulation Model (NCSM) that exploits the potential of deep learning to predict noise levels by classifying the values of signal-to-noise ratio (SNR) in real-time network traffic of microgrid system. This is accomplished by initially applying Gaussian white noise into the data that is generated by microgrid model. Then, the data has noise and data without noise is transmitted through serial communication to simulate real world scenario. At the end, a Gated Recurrent Unit (GRU) model is implemented to predict SNR values for the network traffic data. Our findings show that the proposed model produced promising results in predicting noise. In addition, the classification performance of the proposed model is compared with well-known machine learning models and according to the experimental results, our proposed model has noticeable performance, which achieved 99.96% classification accuracy.
To assess the quality and quantity of Saudi publications in oncology over a 10-year period.
A systematic PubMed search was conducted between January 2008 and December 2017 to retrieve all Saudi ...oncology publications. Data about the articles was collected. The level of evidence (LOE) was independently assessed by 2 authors. Two 5-year periods (2008-2012 and 2013-2017) were compared using the relevant parameters. Clinicaltrials.gov was also searched for all oncology trials registered in Saudi Arabia.
A total of 839 publications met our inclusion criteria. The most common type of research was case series, totaling 32% of all publications. Clinical trials formed less than 2% of the total. The LOE was I, II, III, and IV in 0.3%, 2.1%, 58.4%, and 39.3% of the included publications, respectively. The LOE was the same in the 2 periods. There were more publications in international journals (p=0.004), more international collaborations (p=0.001), and higher journal impact factors (p=0.037) in 2013-2017 than in 2008-2012. Only 76 registered clinical trials were found in the Clinicaltrials.gov registry.
Despite an increase in the number of Saudi publications in the field of oncology over time, the LOE did not change. There were, however, some improvements in the international collaboration and journal impact factor, as well as an increase in the number of studies published in international journals. These observations call for a national strategy to improve oncology research in Saudi Arabia.
Introduction: Herpes simplex virus (HSV) is associated with one of the lethal diseases, Herpes simplex encephalitis (HSE). Diagnosis is confirmed using MRI and CT scan imaging techniques and more ...sensitive DNA PCR from cerebrospinal fluid analysis and brain biopsy.Case presentation: However, after four days, the patient's HSE diagnosis was confirmed by the detection of herpes simplex virus type 1 (HSV1) via polymerase chain reaction (PCR) testing. This case highlights the importance of utilizing multiple diagnostic aids and not solely relying on initial test results, as infections may not appear in CSF analysis or MRI scans initially. Furthermore, this case also emphasizes the necessity of initiating empirical treatment based on clinical signs and symptoms, even in cases where diagnostic tests initially appear negative. Prompt and efficient diagnosis and treatment are crucial in managing HSE and preventing long-term neurological damage.Conclusion: This case of HSE underscores the significance of a multifaceted diagnostic approach and timely intervention in managing this potentially severe and life-threatening condition. As mentioned, sometimes the infection does not appear in CSF analysis initially, nor does its effects appear in MRI. HSV PCR remains the golden test to confirm the diagnosis of HSE.
•This study investigates the impact of service quality on customers' satisfaction during COVID-19.•The use of text mining to discover satisfaction dimensions.•A new method for online customers’ ...reviews analysis is proposed.•New findings are presented on the impact of service quality on customers' satisfaction during COVID-19.•Evaluation of method through the data in TripAdvisor.
The COVID-19 pandemic has caused major global changes both in the areas of healthcare and economics. This pandemic has led, mainly due to conditions related to confinement, to major changes in consumer habits and behaviors. Although there have been several studies on the analysis of customers’ satisfaction through survey-based and online customers’ reviews, the impact of COVID-19 on customers' satisfaction has not been investigated so far. It is important to investigate dimensions of satisfaction from the online customers’ reviews to reveal their preferences on the hotels' services during the COVID-19 outbreak. This study aims to reveal the travelers’ satisfaction in Malaysian hotels during the COVID-19 outbreak through online customers’ reviews. In addition, this study investigates whether service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. Accordingly, we develop a new method through machine learning approaches. The method is developed using text mining, clustering, and prediction learning techniques. We use Latent Dirichlet Allocation (LDA) for big data analysis to identify the voice-of-the-customer, Expectation-Maximization (EM) for clustering, and ANFIS for satisfaction level prediction. In addition, we use Higher-Order Singular Value Decomposition (HOSVD) for missing value imputation. The data was collected from TripAdvisor regarding the travelers’ concerns in the form of online reviews on the COVID-19 outbreak and numerical ratings on hotel services from different perspectives. The results from the analysis of online customers’ reviews revealed that service quality during COVID-19 has an impact on hotel performance criteria and consequently customers' satisfaction. In addition, the results showed that although the customers are always seeking hotels with better performance, they are also concerned with the quality of related services in the COVID-19 outbreak.
Two-dimensional (2D) mesoporous materials have received substantial research interest due to their highly exposed active sites and unusual nanoconfinement effect. However, controllable and efficient ...synthesis of 2D mesoporous materials and investigation of their intrinsic properties have remained quite rare. Herein, a general and effective surface-limited cooperative assembly (SLCA) method enabled by leveling precursor solutions on KCl crystals via centrifugation is employed to conveniently synthesize two-dimensional (2D) monolayer mesoporous materials with different compositions. This novel strategy is performed in a manner similar to spin coating, not only enabling generation of ultrathin mesostructured composite film on KCl particles and recycling excessive precursor solution but also providing favorable solvent annealing environment for the film to form ordered mesostructures. Taking monolayer mesoporous Ce
Zr
O
solid solutions as a sample, they display ultrathin nanosheet morphology with a thickness of ∼20 nm, highly open porous structure, and easily accessible active sites of surface superoxide species. Upon decoration of 2D mesoporous Ce
Zr
O
nanosheets with Pt nanoparticles, the obtained catalyst exhibits superior catalytic activity and stability toward CO oxidation with a low onset temperature of 30 °C and a 100% conversion temperature of 95 °C, which are 35-70 °C lower than those for their counterpart materials, namely, three-dimensional (3D) mesoporous Pt/Ce
Zr
O
. Moreover, their TOF
value is ∼11.3 times higher than that of 3D mesoporous Pt/Ce
Zr
O
. Characterizations based on various techniques indicate that such an outstanding catalytic performance is due to the ultrashort distance (20 nm) of mass diffusion, highly exposed active sites, rich surface-chemisorbed oxygen, and the synergistic effect between the Ce
Zr
O
matrix and Pt species.
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