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Zeolites have unique pore structures of molecular dimensions and tunable compositions, making them ideal for shape selective catalysis and separation. However, targeted synthesis of zeolites with new pore structures and compositions remains a key challenge. Here, we propose an approach based on a unique 3D-3D topotactic transformation, which takes advantage of weak bonding in zeolites. This is inspired by the structure transformation of PST-5, a new aluminophosphate molecular sieve, to PST-6 by calcination. The structure of nano-sized PST-5 crystals is determined by 3D electron diffraction. We find that the 3D-3D topotactic transformation involves two types of building units where penta- or hexa-coordinated Al is present. We apply this approach to several other zeolite systems and predict a series of new zeolite structures that would be synthetically feasible. This method provides a concept for the synthesis of targeted zeolites, especially those which may not be feasible by conventional methods. Zeolites have pore structures that are attractive for shape-selective catalysis and separation, but targeted synthesis is challenging. Here, the authors propose using a 3D-3D topotactic transformation to synthesize targeted zeolites, including some that may be not feasible with conventional methods.

Methane, a greenhouse gas with 21 times the global warming potential of carbon dioxide, is increasingly subject to stringent emission regulations, driving the demand for high-performance methane sensors. This study proposes a novel IR spectroscopy technique based on a CuBDC-integrated microcantilever (CuBDC-microcantilever IR spectroscopy) for CH4 sensing, offering exceptional sensitivity and selectivity. The metal-organic framework (MOF) CuBDC was synthesized on the microcantilever using a drop-and-dry method facilitated by an intense pulsed light technique. Characterization via scanning electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy confirmed the successful formation of CuBDC on the microcantilever. The CuBDC-microcantilever IR spectroscopy demonstrated a significantly enhanced sensitivity, with a differential amplitude at the CH4 characteristic peak approximately 13 times higher than that of a conventional Si microcantilever. Moreover, the limit of detection was determined to be as low as 14.05 ppm. The clear separation of the CH4 characteristic peak from the water and acetone vapor peaks also emphasized the sensor’s high selectivity. These findings highlight the superior sensitivity and selectivity of the proposed sensor, positioning it as a promising platform for CH4 detection in industrial and environmental applications.

The preparation of zeolites has long been viewed as an empirical practice in which the impact of numerous synthesis parameters on complex pathways of crystallization remains unresolved. Efforts to achieve predictive control in zeolite crystal engineering are often motivated by the benefits of producing materials with nanosized dimensions for improved mass transport properties. In the past decade there has been substantial progress in the synthesis of zeolites and zeotypes with nanosized and hierarchical structures that have been shown to outperform conventional analogues in various applications. The ability to synthesize state-of-the-art nanoporous materials has socioeconomic advantages in processes that are critical to addressing twenty-first-century problems. Here we summarize synthetic methods used to prepare different classes of zeolitic materials and we highlight the diversity of nucleation and growth mechanisms, approaches to control these pathways through experimental design, and the advantages of infusing computational and big data analyses to transition zeolite synthesis away from trial-and-error methodologies.Crystal engineering of nanosized and hierarchical zeolites may improve the mass transport properties of materials at the nanoscale in various applications. In this Review, synthetic methods used to prepare different classes of zeolitic materials are summarized, with a focus on nucleation and growth mechanisms. Experimental and computational advances, as well as future challenges in the field, are discussed.

Mesenchymal stromal cell (MSC)-derived extracellular vesicles (EVs) are known to have a therapeutic effect on nephrotoxicity. As animal models require significant time and resources to evaluate drug effects, there is a need for a new experimental technique that can accurately predict drug effects in humans. We evaluated the therapeutic effect of MSC-derived EVs in cisplatin nephrotoxicity using a three-dimensional, gravity-driven, two-layer tubule-on-a-chip (3D-MOTIVE chip). In the 3D-MOTIVE chip, 10 μM cisplatin decreased the number of attached cells compared to the vehicle. Conversely, annexin V and reactive oxygen species (ROS) were increased. Cell viability was increased 2.8-fold and 2.5-fold after treatment with EVs at 4 and 8 µg/mL, respectively, compared to the cisplatin-induced nephrotoxicity group. Cell attachment was increased 2.25-fold by treatment with 4 µg/mL EVs and 2.02-fold by 8 µg/mL EVs. Annexin V and ROS levels were decreased compared to those in the cisplatin-induced nephrotoxicity group. There were no significant differences in annexin V and ROS levels according to EV concentration. In sum, we created a cisplatin-induced nephrotoxicity model on a 3D-MOTIVE chip and found that MSC-derived EVs could restore cell viability. Thus, MSC-derived EVs may have the potential to ameliorate cisplatin-induced nephrotoxicity.

This study presents a urothelium-on-a-chip platform, an advanced microfluidic system designed to replicate the physiological environment of the bladder’s urothelium. This platform facilitates the co-culture of multiple cell types, specifically human urothelial (SV-HUC) and fibroblast (Hs27) cells, effectively simulating the urothelial layer of the bladder. The urothelium-on-a-chip system consists of three insert modules for cultivating SV-HUC and Hs27 cells interlinked through microfluidic channels. Following fabrication, the functionality of the microfluidic channels and the biocompatibility of the chip were evaluated using fluorescence diffusion assays and live/dead assays under dynamic conditions. Cells were cultured under dynamic flow conditions to enhance the interactions between substances across the insert modules. The fluorescence diffusion assay confirmed that the microfluidic channels connecting the culture inserts function properly. The live/dead assay demonstrated high cell viability during co-culture, with 98.27% viability for SV-HUC cells and 99.65% for Hs27 cells. These outcomes further validate the platform’s suitability for long-term culture under dynamic conditions. These findings indicate that the urothelium-on-a-chip platform holds significant potential for effectively mimicking urothelial conditions and serves as a valuable model for studying urothelial diseases. Future advancements, such as incorporating additional cell types and biomechanical forces, could further enhance its applicability for simulating bladder urothelium.

This study presents a urothelium-on-a-chip platform, an innovative microfluidic system designed to replicate the physiological environment of the bladder. This platform effectively models the bladder mucosa by facilitating the co-culture of multiple cell types, including human urothelial (SV-HUC) and fibroblast (Hs27) cells. The system was employed to investigate urothelial injury, inflammation, and recovery, with a particular emphasis on the anti-inflammatory effects of curcumin in the context of lipopolysaccharide (LPS)-induced inflammation. The urothelium-on-a-chip system consists of three insert modules for culturing SV-HUC and Hs27 cells, interconnected via microfluidic channels. Single-cell (SV-HUC only) monocultures and multi-cell (SV-HUC and Hs27) co-cultures were established on the urothelium-on-a-chip platform. In both groups, inflammation was induced using LPS (1 µg/mL) for 24 h, followed by treatment with curcumin (10 µM) for an additional 24 h to evaluate its anti-inflammatory effects. Supernatants from the two groups were collected, and the levels of inflammatory cytokines IL-1β, IL-6, and TNF-α were quantified using ELISA. LPS treatment significantly increased IL-1β and IL-6 levels while slightly decreasing TNF-α. The co-culture systems exhibited notably higher levels of all three cytokines than the monoculture, indicating that fibroblast-urothelial interactions enhance the inflammatory response. Curcumin treatment following LPS exposure notably reduced cytokine levels under certain conditions. In the monoculture, curcumin completely suppressed IL-1β but did not induce a significant change in TNF-α and IL-6 levels. However, curcumin notably reduced all three cytokine levels in the co-culture system, highlighting its potential to modulate inflammation in a multi-cellular context. The ability to simulate inflammatory processes and assess treatments like curcumin provides a novel approach to researching bladder disease and screening potential therapies.

Glomerulonephritis (GN) is characterized by podocyte injury or glomerular filtration dysfunction, which results in proteinuria and eventual loss of kidney function. Progress in studying the mechanism of GN, and developing an effective therapy, has been limited by the absence of suitable in vitro models that can closely recapitulate human physiological responses. We developed a microfluidic glomerulus-on-a-chip device that can recapitulate the physiological environment to construct a functional filtration barrier, with which we investigated biological changes in podocytes and dynamic alterations in the permeability of the glomerular filtration barrier (GFB) on a chip. We also evaluated the potential of GN-mimicking devices as a model for predicting responses to human GN. Glomerular endothelial cells and podocytes successfully formed intact monolayers on opposite sides of the membrane in our chip device. Permselectivity analysis confirmed that the chip was constituted by a functional GFB that could accurately perform differential clearance of albumin and dextran. Reduction in cell viability resulting from damage was observed in all serum-induced GN models. The expression of podocyte-specific marker WT1 was also decreased. Albumin permeability was increased in most models of serum-induced IgA nephropathy (IgAN) and membranous nephropathy (MN). However, sera from patients with minimal change disease (MCD) or lupus nephritis (LN) did not induce a loss of permeability. This glomerulus-on-a-chip system may provide a platform of glomerular cell culture for in vitro GFB in formation of a functional three-dimensional glomerular structure. Establishing a disease model of GN on a chip could accelerate our understanding of pathophysiological mechanisms of glomerulopathy.

Purpose. To distinguish between normal (SV-HUC-1) and cancerous (TCCSUP) human urothelial cell lines using microelectrical impedance spectroscopy (μEIS). Materials and Methods. Two types of μEIS devices were designed and used in combination to measure the impedance of SV-HUC-1 and TCCSUP cells flowing through the channels of the devices. The first device (μEIS-OF) was designed to determine the optimal frequency at which the impedance of two cell lines is most distinguishable. The μEIS-OF trapped the flowing cells and measured their impedance at a frequency ranging from 5 kHz to 1 MHz. The second device (μEIS-RT) was designed for real-time impedance measurement of the cells at the optimal frequency. The impedance was measured instantaneously as the cells passed the sensing electrodes of μEIS-RT. Results. The optimal frequency, which maximized the average difference of the amplitude and phase angle between the two cell lines (p<0.001), was determined to be 119 kHz. The real-time impedance of the cell lines was measured at 119 kHz; the two cell lines differed significantly in terms of amplitude and phase angle (p<0.001). Conclusion. The μEIS-RT can discriminate SV-HUC-1 and TCCSUP cells by measuring the impedance at the optimal frequency determined by the μEIS-OF.

Sequence-to-sequence (Seq2seq) models have played an important role in the recent success of various natural language processing methods, such as machine translation, text summarization, and speech recognition. However, current Seq2seq models have trouble preserving global latent information from a long sequence of words. Variational autoencoder (VAE) alleviates this problem by learning a continuous semantic space of the input sentence. However, it does not solve the problem completely. In this paper, we propose a new recurrent neural network (RNN)-based Seq2seq model, RNN semantic variational autoencoder (RNN--SVAE), to better capture the global latent information of a sequence of words. To reflect the meaning of words in a sentence properly, without regard to its position within the sentence, we construct a document information vector using the attention information between the final state of the encoder and every prior hidden state. Then, the mean and standard deviation of the continuous semantic space are learned by using this vector to take advantage of the variational method. By using the document information vector to find the semantic space of the sentence, it becomes possible to better capture the global latent feature of the sentence. Experimental results of three natural language tasks (i.e., language modeling, missing word imputation, paraphrase identification) confirm that the proposed RNN--SVAE yields higher performance than two benchmark models.

In order to maximize the applicability of sentiment analysis results, it is necessary to not only classify the overall sentiment (positive/negative) of a given document but also to identify the main words that contribute to the classification. However, most datasets for sentiment analysis only have the sentiment label for each document or sentence. In other words, there is no information about which words play an important role in sentiment classification. In this paper, we propose a method for identifying key words discriminating positive and negative sentences by using a weakly supervised learning method based on a convolutional neural network (CNN). In our model, each word is represented as a continuous-valued vector and each sentence is represented as a matrix whose rows correspond to the word vector used in the sentence. Then, the CNN model is trained using these sentence matrices as inputs and the sentiment labels as the output. Once the CNN model is trained, we implement the word attention mechanism that identifies high-contributing words to classification results with a class activation map, using the weights from the fully connected layer at the end of the learned CNN model. In order to verify the proposed methodology, we evaluated the classification accuracy and inclusion rate of polarity words using two movie review datasets. Experimental result show that the proposed model can not only correctly classify the sentence polarity but also successfully identify the corresponding words with high polarity scores.