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Liver organoids represent emerging human-relevant in vitro liver models that have a wide range of biomedical applications in basic medical studies and preclinical drug discovery. However, the generation of liver organoids currently relies on the conventional Matrigel dome method, which lacks precise microenvironmental control over organoid growth and results in significant heterogeneity of the formed liver organoids. Here, we demonstrate a novel high-throughput culture method to generate uniform liver organoids from human pluripotent stem cell-derived foregut stem cells in micropatterned agarose scaffold. By using this approach, more than 8000 uniformly-sized liver organoids containing liver parenchyma cells, non-parenchymal cells, and a unique stem cell niche could be efficiently and reproducibly generated in a 48-well plate with a size coefficient of variation significance smaller than that in the Matrigel dome. Additionally, the liver organoids highly expressed liver-specific markers, including albumin (ALB), hepatocyte nuclear factor 4 alpha (HNF4α), and alpha-fetoprotein (AFP), and displayed liver functions, such as lipid accumulation, glycogen synthesis, ALB secretion, and urea synthesis. As a proof of concept, we evaluated the acute hepatotoxicity of acetaminophen (APAP) in these organoids and observed APAP-induced liver fibrosis. Overall, we expect that the liver organoids will facilitate wide biomedical applications in hepatotoxicity analysis and liver disease modeling.

This paper presents a technology to fabricate ultra-small (100 × 100 μm) thermopile infrared (IR) sensing devices with a novel front-side micromachining process for integration of dense IR detection array. The thermopile IR detection unit is constructed into double-level suspending films, where the IR absorber and n/p-polysilicon thermopile are located at the central-supported upper level and the surrounding-supported lower level, respectively. The separated design for IR absorber and thermopile temperature-detection helps to achieve a high thermocouple fill-factor of 0.9 in terms of the structural area. A single-step wet etching method is proposed to simultaneously release the umbrella-shaped upper-level absorber and the thermal insulation cavity beneath the lower-level thermopile layer. By pre-depositing a polysilicon sacrificial layer, the etching method breakthroughs the restriction of anisotropic over-etch to the single crystalline silicon substrate, thereby maximizing the thermocouple number in the lower-layer film and minimizing the opened area of the etching holes. Finite-element-method simulation results show that the detector with 20 pairs of thermocouples exhibits maximized responsivity than those with more or less pairs of thermocouples. Benefit from the optimized heat dissipation design, the thermopile detection unit in atmospheric environment achieves a high normalized detectivity of 2.1 × 107 cm Hz1/2 W−1, a high responsivity of 52 V W−1 and sound respond time of 1.4 ms. Thanks to the ultra-small unit area, many unites can be densely integrated into IR detection arrays of various scales. As an example, an integrated 4 × 4 IR sensing array is herein fabricated. The 16 integrated pixels demonstrate good consistency in responsivity, with the deviation being less than ±7%. Featuring the tiny-size pixels for dense-array integration and the single-sided micromachining process that is compatible with integrated circuit fabrication, the proposed thermopile IR sensing devices are promising in IR detection applications including consumer-electronic level IR focal plane arrays.

Computational modeling is a crucial approach in material-related research for discovering new materials with superior properties. However, the high design flexibility in materials, especially in the realm of metamaterials where the sub-wavelength structure provides an additional degree of freedom in design, poses a formidable computational cost in various real-world applications. With the advent of big data, deep learning (DL) brings revolutionary breakthroughs in many conventional machine learning and pattern recognition tasks such as image classification. The accompanied data-driven modeling paradigm also provides transformative methodology shift in materials science, from trial-and-error routine to intelligent material discovery and analysis. This review systematically summarize the application of DL in material science, based on a model selection perspective for both natural materials and metamaterials. The review aims to uncover the logic behind data-model relation with emphasis on suitable data structures for different scenarios in the material study and the corresponding problem-solving DL model architectures.

Objective Repair and regeneration of peripheral nerve defect by engineered conduits have greatly advanced in the past decades while still facing great challenges. Approach In this work, we fabricated a new highly oriented poly(L-lactic acid) (PLLA)/soy protein isolate (SPI) nanofibrous conduit (HO-PSNC) for nerve regeneration. Main results Firstly, we observed that SPI could efficiently modify PLLA for the electrospinning of PLLA/SPI nanofibers with enhanced physical and biological properties. Incorporation of SPI decreased the fiber diameter and ductility of PLLA/SPI nanofibrous films (PSNFs), improved the tensile strength and surface wettability of PSNFs and increased the in vivo degradability of the PSNFs. When the hybrid ratio of SPI was 20 and 40%, PSNFs could efficiently promote neural cell extension and differentiation in vitro. Based on these data, 20% SPI (PSNF-20) was chosen for further investigation. Next, PSNF-20 with different fiber orientations (random/low orientation, medium, and high orientation, respectively) were developed and used for evaluating neural cell behaviors on the materials. Results revealed that the PSNF-20 with highly oriented nanofibers (HO-PSNF-20) or mediumly oriented nanofibers (MO-PSNF-20) showed a better performance in directing cell extension and enhancing neurite outgrowth. Finally, the highly oriented nanofibers conduits (HO-PSNC-20) were used to bridge sciatic nerve defect in rats with highly oriented PLLA and autografts as controls. HO-PSNC-20 exhibited a significant promotion in nerve regeneration and functional reconstruction comparing to highly oriented PLLA as proven by the evaluations of walking track, electrophysiology, toluidine blue nerve staining, transmission electron microscopy, neural factors staining and qPCR, and gastrocnemius histology. Significance In conclusion, nerve conduit fabricated from aligned electrospinning of SPI-modified PLLA nanofibers is promising for peripheral nerve regeneration.

Early and differential diagnosis of Alzheimer's disease (AD) is a problem that puzzled many doctors. Reliable markers in easy-assembling samples are of considerable clinical diagnostic value. In this work, laser Raman spectroscopy (LRS) was developed a new method that potentially allows early and differential diagnosis of AD from the platelet sample. Raman spectra of platelets isolated from different ages of AD transgenic mice and non-transgenic controls were collected and analyzed. Multilayer perceptron networks (MLP) classification method was used to classify spectra and establish the diagnostic models. For differential diagnosis, spectra of platelets isolated from AD, Parkinson's disease (PD) and vascular dementia (VD) mice were also discriminated. Two notable spectral differences at 740 and 1654 cm-1 were revealed in the mean spectrum of platelets isolated from AD transgenic mice and the controls. MLP displayed a powerful ability in the classifying of early, advanced AD and the control group, and in differential diagnosis of PD and advanced AD, as well as VD and advanced AD. The results suggest that platelet detecting by LRS coupled with MLP analysis appears to be an easy and accurate method for early and differential diagnosis of AD. This technique could be rapidly promoted from laboratory to the hospital.

In this paper, we discuss an approach to fabricating a carbon nanotubes (CNTs) gate electrode structure for cold cathode emitter applications. The field emission behavior of the structure was studied by using single vertically aligned free-standing CNTs of various lengths and gate electrodes of various sizes to determine the optimal emission structure. A single CNT electrode source with a gate electrode was fabricated by combining optical lithography, electron-beam lithography (EBL), and inductively coupled plasma chemical vapor deposition (ICP-CVD). A single mask was adopted to define the gated structure and nano sized catalyst for CNT growth. A vertically aligned CNT was then grown within the gate hole by ICP-CVD. The CNT height inside the gate could be lengthened by multiple growth processes without affecting the field emission capability. The best turn-on fields, defined as the field required to generate an emission current of 1 nA, were approximately 1.66 and 1.38 V/µm, respectively. The turn-on field emission current saturated when the CNT length was about the same as the gate height.

This paper demonstrates the fabrication of a micro-cantilever equipped with a high aspect ratio carbon nanotube (CNT) tip to perform surface characterization with high spatial resolution. A single vertical CNT, synthesized on a freestanding tip-lessmicro cantilever, is intended to be used for scanning probe microscopy (SPM). E-beam lithography and lift-off technique was adopted to define a nano-sized catalyst particle. An individual CNT was then synthesized by using an inductively coupled plasma chemical vapor deposition (ICP-CVD) system. The freestanding individual aligned CNT probe was then used to characterize a silicon nano pillar structure. The surface morphology was scanned in a contact mode and 0.1 nN set-point force. The ultra fine resolution can be characterized without wearing the probe and sample. The CNT probe shows a great potential as a high sensitive sensor operating in an optimally adjusted atomic force microscopy (AFM) system.

The three-dimensional premixed H2-O2 detonation propagation in rectangular ducts is simulated using an in-house parallel detonation code based on the second-order space–time conservation element and solution element (CE/SE) scheme. The simulation reproduces three typical cellular structures by setting appropriate cross-sectional size and initial perturbation in square tubes. As the cross-sectional size decreases, critical cellular structures transforming the rectangular or diagonal mode into the spinning mode are obtained and discussed in the perspective of phase variation as well as decreasing of triple point lines. Furthermore, multiple cellular structures are observed through examples with typical aspect ratios. Utilizing the visualization of detailed three-dimensional structures, their formation mechanism is further analyzed.

Regenerative medicine and tissue engineering have seen unprecedented growth in the past decade, driving the field of artificial tissue models towards a revolution in future medicine. Major progress has been achieved through the development of innovative biomanufacturing strategies to pattern and assemble cells and extracellular matrix (ECM) in three-dimensions (3D) to create functional tissue constructs. Bioprinting has emerged as a promising 3D biomanufacturing technology, enabling precise control over spatial and temporal distribution of cells and ECM. Bioprinting technology can be used to engineer artificial tissues and organs by producing scaffolds with controlled spatial heterogeneity of physical properties, cellular composition, and ECM organization. This innovative approach is increasingly utilized in biomedicine, and has potential to create artificial functional constructs for drug screening and toxicology research, as well as tissue and organ transplantation. Herein, we review the recent advances in bioprinting technologies and discuss current markets, approaches, and biomedical applications. We also present current challenges and provide future directions for bioprinting research.

Alzheimer's disease (AD) is a common form of dementia. Early and differential diagnosis of AD has always been an arduous task for the medical expert due to the unapparent early symptoms and the currently imperfect imaging examination methods. Therefore, obtaining reliable markers with clinical diagnostic value in easily assembled samples is worthy and significant. Our previous work with laser Raman spectroscopy (LRS), in which we detected platelet samples of different ages of AD transgenic mice and non-transgenic controls, showed great effect in the diagnosis of AD. In addition, a multilayer perception network (MLP) classification method was adopted to discriminate the spectral data. However, there were disturbances, which were induced by noise from the machines and so on, in the data set; thus the MLP method had to be trained with large-scale data. In this paper, we aim to re-establish the classification models of early and advanced AD and the control group with fewer features, and apply some mechanism of noise reduction to improve the accuracy of models. An adaptive classification method based on the Gaussian process (GP) featured, with predictive probabilities, is proposed, which could tell when a data set is related to some kind of disease. Compared with MLP on the same feature set, GP showed much better performance in the experimental results. What is more, since the spectra of platelets are isolated from AD, GP has good expansibility and can be applied in diagnosis of many other similar diseases, such as Parkinson's disease (PD). Spectral data of 4 month and 12 month AD platelets, as well as control data, were collected. With predictive probabilities, the proposed GP classification method improved the diagnostic sensitivity to nearly 100%. Samples were also collected from PD platelets as classification and comparison to the 12 month AD. The presented approach and our experiments indicate that utilization of GP with predictive probabilities in platelet LRS detection analysis turns out to be more accurate for early and differential diagnosis of AD and has a wide application prospect.

Zinc oxide (ZnO) films with two-dimensional (2D) vertically aligned nanowalls, denoted by nanowalls-films, are successfully prepared on glass substrates at a low growth temperature of 450 °C without using metal catalysts. The morphology and optical properties of the nanowalls-film are characterized by scanning electron microscopy, X-ray diffraction analysis, transmission electron microscopy, energy dispersive X-ray spectroscopy, and photoluminescence measurement. The ZnO nanowalls-film show a strong UV emission and a preferred c-axis orientation with a hexagonal structure. The UV sensor measurement of the ZnO nanowalls-film shows a high sensitivity to UV light, rapid rise and decay times, and a good UV-to-visible rejection ratio.

Early and differential diagnosis of Alzheimer's disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson's disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.

We describe a novel technique capable of controlling the oxygen content of YBa2Cu3O x (YBCO) films in a precise and reversible manner. The temperature dependence of resistivity and the distinct two-plateau behavior in critical temperature T co versus oxygen content plot of these films are consistent with those observed in the bulk and single crystals of YBCO. The O 1s and Cu 2p absorption spectra of these films were measured by polarization-dependent X-ray absorption spectroscopy (XAS). The intensity variations of the pre-edge peaks as a function of oxygen content are discussed. We also used these films to systematically study the electron-phonon coupling strength and the position of Fermi level by using a femtosecond pump-probe technique. A clear sign-reversal of the transient reflectivity, which was consistently explained by the thermomodulation model, was observed. Both of these optical measurements support the idea that the electronic structure of YBCO cuprates is based on the charge transfer model with hybridization between the Cu and O sites.