Surfactant-free electrochemical synthesis of fluoridated hydroxyapatite nanorods for biomedical applications

Fluoridated hydroxyapatite (FHA) [Ca₁₀(PO₄)₆F_x(OH)_2−x, x = 0–2] is believed to be a promising calcium phosphate (CaP) to replace pure hydroxyapatite (HA) for next-generation implants, owing to its better biocompatibility, higher antibacterial activity, and lower solubility. Notably, the shape and size of the CaP crystals play key roles in their performance and can influence their applications. One-dimensional (1D) FHA nanorods are important CaP materials which have been widely used in regenerative medicine applications such as restorative dentistry. Unfortunately, the traditional synthesis methods for FHA nanorods either employ surfactants or take a relatively long time. In this study, we aimed to propose a facile synthesis route to fabricate FHA nanorods without any surfactants using an electrochemical deposition method for the first time. This study focused on preparing FHA nanorods without the assistance of any surfactant, unlike the traditional synthesis methods, to avoid chemical impurities. FHA nanorods with lengths of 124–2606 nm, diameters of 28–211 nm, and aspect ratios of 4.4–21.8 were synthesized using the electrochemical method, followed by a heat treatment. For the as-synthesized FHA nanorods, the Ca/P ratio was 1.60 and the atomic concentration of F was 2.06 at.%. An ultrastructure examination revealed that each FHA nanorod possessed long-range order, good crystallinity, and a defect-free lattice with a certain crystallographic plane orientation along the whole rod. In short, we propose a novel, surfactant-free, cost-saving, and more efficient route to synthesize FHA nanorods which can be widely applied in multiple biomedical applications, including drug delivery, bone repair, and restorative dentistry.     

Novel nanorod Ti0·7Ir0·3O2 prepared by facile hydrothermal process: A promising non-carbon support for Pt in PEMFCs

Late transition metal doped TiO₂ has been exploited for generating efficient catalyst support by enhancing electrical conductivity and modifying properties of TiO₂. The Ti_0·7Ir_0·3O₂ nanorod (NRs), a novel catalyst support for Pt nanoparticles, was prepared for the first time via single-step hydrothermal process at low temperature using IrCl₃·3H₂O and TiCl₄ as starting materials. We found that the Ti_0·7Ir_0·3O₂ NRs with 70–80 nm in length and 25–30 nm in width is successful prepared at 210 °C for 12 h without utilizing surfactants or stabilizers. In addition, the Ti_0·7Ir_0·3O₂ NRs was presented principally as a single-phase solid with the TiO₂ is in the rutile form with high crystallinity without using further treatment after synthesis. More importantly, we found that the Ti_0·7Ir_0·3O₂ NRs possesses high electrical conductivity (0.028 S cm⁻¹) dealing the intrinsically non-conducted drawback of TiO₂. The Pt nanoparticles were then deposited on the support of Ti_0·7Ir_0·3O₂ NRs via chemical reduction method. The properties of 20 wt % Pt/Ti_0·7Ir_0·3O₂ NRs electrocatalyst were characterized by X-ray diffraction (XRD), Transmission electron microscopy (TEM), the cyclic voltammetry (CV). The uniformly distributed small Pt nanoparticles (3–4 nm diameter) were well adhered to the Ti_0·7Ir_0·3O₂ NRs. The electrochemically active surface area (ECSA) of 20 wt % Pt/Ti_0·7Ir_0·3O₂ NRs was higher than that of the commercial 20 wt % Pt/C (E-TEK) due to the small size and good dispersion of Pt nanoparticles on the surface of Ti_0·7Ir_0·3O₂ NRs. Moreover, the ECSA value of the Pt/Ti_0·7Ir_0·3O₂ NRs retained up to 88% after 2000 cycles of cyclic voltammetry, suggesting the high stability of catalyst resulted from strong metal support interaction (SMSI) of Titania-based materials with the noble metals. More importantly, the onset potential of Pt/Ti_0·7Ir_0·3O₂ NRs catalyst towards oxygen reduction reaction is more positive (∼80 mV) compared to commercial Pt/C, indicating the high catalytic activity of the Pt/Ti_0·7Ir_0·3O₂ NRs catalyst. The results of this research suggested that novel Ti_0·7Ir_0·3O₂ NRs could be applied as promising robust non-carbon support for Pt. This research also creates a preliminary step for investigating systematically promising Iridium doped Titania materials.     

Two-level intelligent modeling method for the rate of penetration in complex geological drilling process

The rate of penetration (ROP) model is of great importance in achieving a high efficiency in the complex geological drilling process. In this paper, a novel two-level intelligent modeling method is proposed for the ROP considering the drilling characteristics of data incompleteness, couplings, and strong nonlinearities. Firstly, a piecewise cubic Hermite interpolation method is introduced to complete the lost drilling data. Then, a formation drillability (FD) fusion submodel is established by using Nadaboost extreme learning machine (Nadaboost-ELM) algorithm, and the mutual information method is used to obtain the parameters, strongly correlated with the ROP. Finally, a ROP submodel is established by a neural network with radial basis function optimized by the improved particle swarm optimization (RBFNN-IPSO). This two-level ROP model is applied to a real drilling process and the proposed method shows the best performance in ROP prediction as compared with conventional methods. The proposed ROP model provides the basis for intelligent optimization and control in the complex geological drilling process.     

TSL:predicting popularity of Facebook content based on tie strength

The rapid development of online social networks leads to an explosion of information,however,there are great differences in the popularity of different messages,and accurate prediction is always a great difficulty is the current study.Popularity prediction of online content aims to predict the popularity in the future based on its early diffusion status.Existing models for popularity prediction were mostly based on discovering network features or fitting the equation into a varying time function that the accuracy of current popularity prediction model was not high enough.Therefore,with the help of the weak ties theory in sociology,the concept of tie strength was introduced and a multilinear regression equation was constructed combined with the early popularity.A TSL model to predict the popularity of Facebook’s well-known pages was proposed.The main contribution of this article was to solve the problem and few or no work based on sociology.A high linear correlation between the proportion of faithful fans was existed in Facebook homepage with frequent shares in the early and the future popularity.Compared with other baseline models,an experimental study of Facebook (including 1.54 million shares) illustrates the effectiveness of the proposed TSL model,and the performance is better than the existing similar methods.     

Clean hydrogen production in a full biological microbial electrolysis cell

The recent interest in microbial electrolysis cell (MEC) technology has led the research platform to develop full biological MECs (bioanode-biocathode, FB-MEC). This study focused on biohydrogen production from a biologically catalyzed MEC. A bioanode and a biocathode were initially enriched in a half biological MFC (bioanode-abiocathode, HB-MFC) and a half biological MEC (abioanode-biocathode, HB-MEC), respectively. The FB-MEC was established by transferring the biocathode of the HB-MEC and the bioanode of the HB-MFC to a two-chamber MEC. The FB-MEC was operated under batch (FB-MEC-B) and recirculation batch (FB-MEC-RB) modes of operation in the anodic chamber. The FB-MEC-B reached a maximum current density of 1.5 A/m² and the FB-MEC-RB reached a maximum current density of 2.5 A/m² at a similar applied voltage while the abiotic control system showed the maximum of 0.2 A/m². Hydrogen production rate decreased in the FB-MEC compared to that of the HB-MEC. However, the cathodic hydrogen recovery increased from 42% obtained in the HB-MEC to 56% in the FB-MEC-B and 65% in the FB-MEC-RB, suggesting the efficient oxidation and reduction rates in the FB-MEC compared to the HB-MEC. The onset potential for hydrogen evolution reaction detected by linear sweep voltammetry analysis were −0.780 and −0.860 V vs Ag/AgCl for the FB-MEC-RB and the FB-MEC-B (−1.26 for the abiotic control MEC), respectively. Moreover, the results suggested that the FB-MEC worked more efficiently when the biocathode and the bioanode were enriched initially in half biological systems before transferring to the FB-MEC compared to that of the simultaneously enriched in one system.     

Medical image detection and azithromycin in the treatment of pelvic inflammation based on intelligent hospital system

The pelvic infection and people genital tract inflammation, infertility, chronic pain, and reasons for detecting ectopic pregnancy. Diagnostic and management challenges, from different signs and symptoms and a variety of microbial etiology is not fully delineated largely responsible. Because of the potential for serious things of the network, it is recommended for the diagnosis and treatment of low barriers to entry. One of pelvic inflammatory infection of pregnant woman of childbearing age is not the most testing an important public pelvic problem remains. This is the main long-term internet of things, such as tubal factor infertility, ectopic pregnancy and chronic pelvic pain. Also, acute care and its complications lead to significant pelvic care costs. These long-term preventions of the internet of things depend on the microbial cause of acute knowledge of strategies based on treatment. All aware that it is a polymicrobic acute infection. Organisms, Chlamydia trachomatis and Neisseria gonorrhoeae sexually transmit presence, in many cases, including the endogenous vaginal and cervical flora often associated with microorganisms. It contains anaerobic and facultative bacteria similar to those associated with bacterial most importantly mycoplasma has also recently been involved in acute reasons. Therefore, a therapeutic regimen for acute should provide a wide spectral range that is effective against these microorganisms.     

Effect of in-app components,medium, and screen size of electronic textbooks on reading performance,behavior, and perception

Although many app-based textbooks are available for students, reading have not been thoroughly outlined. This study aimed to understand how changes from paper to electronic textbooks have affected the academic reading task, investigate student users’ perceptions of in-app components and screen sizes, and identify issues affecting in-app components and task requirements. A mixed factorial design experiment was employed. Results showed that there were no significant changes in comprehension and time spent reading between print text and the iPad. Yet, student highlighting, notetaking, and reading behavior and perception significantly changed based on condition. In addition, students struggled to use in-app components and found them frustrating especially when accounting for sentence splitting. The findings presented can assist in understanding the changes in student reading behavior, which can be used to improve interface design of future e-textbooks.     

Optimal 5G network slicing using machine learning and deep learning concepts

Network slicing is predetermined to hold up the diversity of emerging applications with enhanced performance and flexibility requirements in the way of splitting the physical network into numerous logical networks. Consequently, a tremendous data count has been generated with an enormous number of mobile phones due to these applications. This has made remarkable challenges and has a considerable influence on the network slicing performance. This work aims to design an efficient network slicing using a hybrid learning algorithm. Thus, we proposed a model, which involves three main phases: (a) Data collection, (b) Optimal weighted feature extraction (OWFE), and (c) Slicing classification. First, we collected the 5G network slicing dataset, which involves the attributes associated with various network devices like “user device type, duration, packet loss ratio, packet delay budget, bandwidth, delay rate, speed, jitter, and modulation type.” Next, we performed the OWFE, in which a weight function is multiplied with the attribute values to have high scale variation. We optimized this weight function by the hybridization of two meta-heuristic algorithms—glowworm swarm optimization and deer hunting optimization algorithm (DHOA)—and named the proposed model glowworm swarm-based DHOA (GS-DHOA). For the given attributes, we classified the exact network slices like “eMBB, mMTC, and URLLC” for each device by a hybrid classifier using deep belief and neural networks. The weight function of both networks is optimized by the GS-DHOA. The experiment results revealed that the proposed model could influence the provision of accurate 5G network slicing.