Artificial meniscal implants can be used to replace a severely injured meniscus after meniscectomy and restore the normal functionality of a knee joint. The aim of this paper was to design porous meniscal implants and assess their biomechanical properties. Finite element simulations were conducted on eight different cases including intact healthy knees, knee joints with solid meniscal implants, and knee joints with meniscal implants with two types of triply periodic minimal surfaces. Compression stresses, shear stresses, and characteristics of stress concentrated areas were evaluated using an axial compressive load of 1150 N and an anterior load of 350 N. Compared to the solid meniscal implant, the proposed porous meniscal implant produced lower levels of compression and shear stresses on the cartilage, which facilitated the cartilage to retain a semilunar characteristic similar to the natural meniscus. Moreover, both compression and shear stresses on the artificial cartilage were found to be sensitive to the pore properties of the meniscal implant. The meniscal implants with primitive surfaces (porosity: 41%) showed a better performance in disseminating stresses within the knee joint. The present commercial meniscal implant has the problem of equivalent biomechanical properties compared to natural menisci. The main advantage of the proposed porous structure is that it can be used to prevent excessive compression and shear stresses on the articular cartilages. This structure has advantages both in terms of mechanics and printability, which can be beneficial for future clinical applications.

Several advantages of directed energy deposition-arc (DED-arc) have garnered considerable research attention including high deposition rates and low costs. However, defects such as discontinuity and pores may occur during the manufacturing process. Defect identification is the key to monitoring and quality assessments of the additive manufacturing process. This study proposes a novel acoustic signal-based defect identification method for DED-arc via wavelet time–frequency diagrams. With the continuous wavelet transform, one-dimensional (1D) acoustic signals acquired in situ during manufacturing are converted into two-dimensional (2D) time–frequency diagrams to train, validate, and test the convolutional neural network (CNN) models. In this study, several CNN models were examined and compared, including AlexNet, ResNet-18, VGG-16, and MobileNetV3. The accuracy of the models was 96.35%, 97.92%, 97.01%, and 98.31%, respectively. The findings demonstrate that the energy distribution of normal and abnormal acoustic signals has significant differences in both the time and frequency domains. The proposed method is verified to identify defects effectively in the manufacturing process and advance the identification time.

The development of nanomaterials with special optical window is critical for clinical applications and the optoelectronic industry. In this work, eight kinds of samarium-based metal organic compound nanoparticles (Sm–Fe, Sm–Ga, Sm–Mn, Sm–Na, Sm–Nb, Sm–W, Sm–Cu, and Sm–Al) were synthesized through a solution method. They show polychromatic-photoluminescence spectra extended from the UV to near-infrared (NIR) region when excited by 280 nm, 380 nm, 480 nm, 580 nm, and 785 nm light. They emit direct white light with respect to UV excitation. Tunable white-to-green fluorescence can be achieved by variation of excitation light around 300–400 nm. When they are excited by a 785 nm light source, they show intense fluorescence around 800–1100 nm, which is promising for NIR bio-imaging. Their application in multicolor ultra-wide-range bio-tissue fluorescence imaging is demonstrated by UV (359–371 nm), blue (450–490 nm), green (540–552 nm), and NIR light (central wavelength = 785 nm) excitation with pig kidney tissue samples.

This article proposes a novel wideband three-way filtering power divider (FPD) based on one single multimode patch resonator (MPR). Firstly, by researching the unique trilateral symmetry properties of equilateral triangle patch resonator (ETPR) modes, the TM11 and TM30 resonant modes have been exploited to establish the three-way power division. Then metallic vias and slots perturbations are introduced to flexibly control the resonant mode frequencies to realize the assumed two-order filtering response. Ultimately, a resistors-grounded isolation network is developed for decent in-band isolations. For validation, a prototype with center frequency at 5.1 GHz and with 3-dB fractional bandwidth (FBW) of 21% is designed, simulated, and fabricated. The measured results show good agreements with simulated ones along with wide passband, good frequency selectivity, and in-band isolation. Finally, an extended discussion is also provided to further demonstrate the flexibility and extendibility of the proposed design method.

Temperature-adjustable optical properties of two types of one-dimensional photonic superlattices made from lithium niobate or liquid crystal are theoretically investigated. The control of band structures and density of states is achieved through the temperature with various values of refractive index, layer width, and propagation wavelength. It is presented that a null photonic gap appears at certain temperature points, which are decided by the ratio of layer width and the refractive index. It is shown that the spatial average of the wave vector ⟨ k ⟩ vanishes at a specific temperature as functions of the layer thickness and refractive index. The values of the temperature corresponding to zero-refractive-index (zero- ⟨ n ⟩ ) are demonstrated to be suppressed when the refractive index or the ratio of the layer width is enhanced. Null photonic gap and zero- ⟨ n ⟩ can appear simultaneously at certain wavelengths in the range of visible light, which can be modulated by the temperature. This implies the importance of one-dimensional photonic superlattices made from temperature-controllable-refractive-index materials for many important practical applications.