Low-Cost and Eco-Friendly Hydroxyapatite Nanoparticles Derived from Eggshell Waste for Cephalexin Removal

This work describes the hydroxyapatite nanoparticle (HAP) preparation from eggshell waste and their application as an adsorbent for Cephalexin (Ceph) antibiotic removal from aqueous solutions. Chemical precipitation with phosphoric acid was used to evaluate the feasibility of calcium oxide for HAP preparation. The structural properties of HAP were characterized by X-ray diffraction, which revealed the formation of the hydroxyapatite crystalline phase formation. In addition, transmitting electron spectroscopy showed an irregular shape with a variation in size. The impact of various experimental conditions on the removal efficiency such as the solution’s pH, contact time, HAP mass, solution temperature, and Ceph concentration were studied. Experimental data showed that HAP could remove most Ceph species from aqueous solutions within 1 h at pH = 7 with 70.70% adsorption efficiency utilizing 50 mg of the HAP. The removal process of Ceph species by HAP was kinetically investigated using various kinetic models, and the results showed the suitability of the pseudo-second-order kinetic model for the adsorption process description. Moreover, the removal process was thermodynamically investigated; the results showed that the removal was spontaneous endothermic and related to the randomness increase. The data confirmed that HAP had high efficiency in removing Ceph antibiotics from an aqueous solution.

A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

Study on the Algorithm of Three-Dimensional Surface Residual Material Height of Nano-ZrO2 Ceramics under Ultra-Precision Grinding

A large number of studies have shown that the height of a residual material is the key factor affecting the surface quality of ultra-precision grinding. However, the grinding process contains several random factors, such as the randomness of grinding particle size and the random distribution of grinding particles, which cause the complexity of the material removal process. In this study, taking the Nano-ZrO2 as an example, the removal process of surface materials in ultra-precision grinding of hard and brittle materials was analyzed by probability. A new calculation method for the height of surface residual materials in ultra-precision grinding of Nano-ZrO2 was proposed, and the prediction model of the three-dimensional roughness Sa and Sq were established by using this calculation method. The simulation and experimental results show that this calculation method can obtain the more accurate surface residual material height value which accords with the characteristics of three-dimensional roughness sampling, which provides a theoretical reference for the analysis of the material removal process and the surface quality evaluation of ultra-precision grinding of hard and brittle materials.