High Throughput Research Articles

Transcriptomic characteristics analysis of bone from chronic osteomyelitis

To explore the molecular mechanism of chronic osteomyelitis and to clarify the role of MAPK signal pathway in the pathogenesis of chronic osteomyelitis, by collecting and analyzing the transcriptional information of bone tissue in patients with chronic osteomyelitis. Four cases of traumatic osteomyelitis in limbs from June 2019 to June 2020 were selected, and the samples of necrotic osteonecrosis from chronic osteomyelitis (necrotic group), and normal bone tissue (control group) were collected. Transcriptome information was collected by Illumina Hiseq Xten high throughput sequencing platform, and the gene expression in bone tissue was calculated by FPKM. The differentially expressed genes were screened by comparing the transcripts of the Necrotic group and control group. Genes were enriched by GO and KEGG. MAP3K7 and NFATC1 were selected as differential targets in the verification experiments, by using rat osteomyelitis animal model and immunohistochemical analysis. A total of 5548 differentially expressed genes were obtained by high throughput sequencing by comparing the necrotic group and control group, including 2701 up-regulated and 2847 down-regulated genes. The genes enriched in MAPK pathway and osteoclast differentiation pathway were screened, the common genes expressed in both MAPK and osteoclast differentiation pathway were (inhibitor of nuclear factor κ subunit Beta, IκBKβ), (mitogen-activated protein kinase 7, MAP3K7), (nuclear factor of activated t cells 1, NFATC1) and (nuclear factor Kappa B subunit 2, NFκB2). In rat osteomyelitis model, MAP3K7 and NFATC1 were highly expressed in bone marrow and injured bone tissue. Based on the transcriptome analysis, the MAPK signaling and osteoclast differentiation pathways were closely related to chronic osteomyelitis, and the key genes IκBKβ, MAP3K7, NFATC1, NFκB2 might be new targets for clinical diagnosis and therapy of chronic osteomyelitis.

High throughput screening of promoted bimetallic catalysts supported on alumina for production of light olefins via Fischer-Tropsch synthesis with artificial neural network and response surface methodology

The production of light olefins via Fischer-Tropsch Synthesis (FTS) was investigated by both experimental and modeling studies. Experimental studies were conducted by employing promoted FeCo/α-Al2O3 (FeCo) bimetallic catalysts. The Response Surface Methodology (RSM) and Artificial Neural Network (ANN) methods were used in modeling. Specifically, effects of the types and concentration of promoters on the catalytic performances of α-Al2O3 supported iron-cobalt bimetallic catalysts are investigated by using Ce, Ni, La, Ho, Ga Cu, Mn, and Zn used as promoters. A total of 49 catalysts were prepared by co-impregnation method and tested at 310 °C and 1 bar in a high-speed catalyst performance test system (HT-CPA). The FTS performances of catalysts showed that both the types and amounts of the promoters can affect light olefin production. The light olefin (C2=–C3=) production of unpromoted FeCo catalyst was about 3.87 × 10−3 (mol C/g active metal. h). The best performances were obtained from FeCo catalysts promoted with Ho, Cu, and Zn. Presence of these promoters increased olefin production up to ∼ 84 %, 76 % and 70 %, respectively.Performance parameters (MSE and R2) of RSM and ANN models revealed that both methods can be used to support experimental studies and provide a procedure for optimizing catalyst formulations yielding the desired product selectivity in FTS processes. The RSM outcomes aligned with experimental findings, suggest that that Ho, Cu, and Zn can serve as suitable promoters for the light olefin production in FTS. ANN model attained higher performance parameters (R2 = 0.95 and MSE = 2E−7) than RSM model (R2 = 0.85 and MSE = 3.4E−7). This indicates that ANN model, which designed as one hidden layer including 7 neurons, can provide better predictions and a fitting capacity. Therefore, it may be used in further optimization and sensitivity analysis approaches. Alignment degree between the experimental and modeling results demonstrates that the proposed methodology can provide a reliable and efficient way to guide experiments to optimize catalyst formulations through identification of potential promoters and their amounts to be used.

In vitro assessment of thyroid peroxidase inhibition by chemical exposure: comparison of cell models and detection methods.

Disruption of the thyroid hormone (TH) system is connected with diverse adverse health outcomes in wildlife and humans. It is crucial to develop and validate suitable in vitro assays capable of measuring the disruption of the thyroid hormone (TH) system. These assays are also essential to comply with the 3R principles, aiming to replace the ex vivo tests often utilised in the chemical assessment. We compared the two commonly used assays applicable for high throughput screening [Luminol and Amplex UltraRed (AUR)] for the assessment of inhibition of thyroid peroxidase (TPO, a crucial enzyme in TH synthesis) using several cell lines and 21 compounds from different use categories. As the investigated cell lines derived from human and rat thyroid showed low or undetectable TPO expression, we developed a series of novel cell lines overexpressing human TPO protein. The HEK-TPOA7 model was prioritised for further research based on the high and stable TPO gene and protein expression. Notably, the Luminol assay detected significant peroxidase activity and signal inhibition even in Nthy-ori 3-1 and HEK293T cell lines without TPO expression, revealing its lack of specificity. Conversely, the AUR assay was specific to TPO activity. Nevertheless, despite the different specificity, both assays identified similar peroxidation inhibitors. Over half of the tested chemicals with diverse structures and from different use groups caused TPO inhibition, including some widespread environmental contaminants suggesting a potential impact of environmental chemicals on TH synthesis. Furthermore, in silico SeqAPASS analysis confirmed the high similarity of human TPO across mammals and other vertebrate classes, suggesting the applicability of HEK-TPOA7 model findings to other vertebrates.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Amanitin determination in bile samples by UHPLC-MS: LR-MS and HR-MS analytical performance

Consumption of misidentified foraged mushrooms containing bicyclic amanitin octapeptides is a worldwide public health and veterinary problem, being considered one of the deadliest accidental human and canine food ingestion due to acute liver failure (ALF). Reversal of advanced ALF and complete clinical recovery can be achieved following definitive removal of accumulated amatoxin laden bile from the gallbladder. An accurate means of quantifying amanitin content in aspirated bile is, therefore, urgently needed. Building on our prior work validating a method to detect and quantify amanitin in hepatic autopsy tissue, the development of an accurate method of measuring α- and β-amanitin in aspirated gallbladder bile was performed to evaluate the efficiency of this emergency procedure applied as a clinical treatment for intoxicated patients. A solid-phase extraction (SPE) procedure was optimized followed by detection based on ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). Low resolution mass spectrometry (LRMS) was compared with high resolution (HRMS) by the validation of UHPLC-MS/MS (triple quadrupole MS) and UHPLC-ToF-MS (time-of-flight MS). Both methods were able to detect amatoxins in bile with limits of detection and quantification ranging from 2.71 to 3.46 µg.kg-1, and 8.36–9.03 µg.kg-1 for α-amanitin and, 0.32–1.69 µg.kg-1 and 0.55–5.62 µg.kg-1 for β-amanitin, respectively. Validation was completed with the evaluation of linearity, specificity, robustness, recovery, and precision following the ICH guidelines and CIR 808/2021. The validated methods were finally applied to bile samples obtained 48–96 hours + post-ingestion from 4 amatoxin poisoning patients who underwent gallbladder drainage procedures in Vietnam, Canada, and California. Gallbladder bile from patients with amatoxin mushroom poisoning contained significant amanitin content, even when aspirated several days post-ingestion, thus confirming the important role of enterohepatic circulation in amatoxin hepatotoxicity. This work represents a high and unique analytical throughput in amanitin poisoning allowing to efficiently respond to this fatal health problem.

Highly sensitive extraction-free saliva-based molecular assay for rapid diagnosis of SARS-CoV-2.

Three years after the SARS-CoV-2 outbreak, there are no molecular tests that combine low-cost and straightforward sample preparation, effective sample handling, minimal reagent and disposable requirements, high sensitivity, and high throughput required for mass screening. Existing rapid molecular techniques typically sacrifice certain requirements to meet others. Yet, localized outbreaks of novel viral diseases happen daily in different parts of the world. In this context, respiratory diseases are of specific importance, as they are frequently airborne and highly contagious, with the potential for a rapid global spread. The widely accepted opinion is that another pandemic is just a question of time. To ensure that the containment efforts for the upcoming "disease X" are successful, introducing rapid, high-throughput, and highly sensitive diagnostic methods for detecting and identifying pathogens is critical. A few months into the pandemic, saliva was suggested as a diagnostic matrix for SARS-CoV-2 detection. The collection of saliva does not require swabs and is minimally invasive. In particular, extraction-free saliva-based assays require fewer reagents and disposables, and therefore are faster and cheaper, offering an appealing alternative for low-income countries. Unfortunately, current extraction-free saliva-based detection methods, such as direct RT-qPCR or isothermal amplification, have either low sensitivity or low throughput. Therefore, we believe that the presented highly sensitive ht-OMBi platform and the extraction-free saliva-based molecular assay can become an essential tool in the infectious disease monitoring toolbox.

Human cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

Characterisation of high throughput screening outputs for small molecule degrader discovery

Targeted protein degradation is an important mechanism carried out by the cellular machinery, one that is gaining momentum as an exploitable strategy for the development of drug-like compounds. Molecules which are able to induce proximity between elusive therapeutic targets of interest and E3 ligases which subsequently leads to proteasomal degradation of the target are beginning to decrease the percentage of the human proteome described as undruggable. Therefore, having the ability to screen for, and understand the mechanism of, such molecules is becoming an increasingly attractive scientific focus. We have established a number of cascade experiments including cell-based assays and orthogonal triage steps to provide annotation to the selectivity and mechanism of action for compounds identified as putative degraders from a primary high throughput screen against a high value oncology target. We will describe our current position, using PROTACs as proof-of-concept, on the analysis of these novel outputs and highlight challenges encountered.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

A Wettability Contrast SERS Droplet Assay for Multiplexed Analyte Detection.

Droplet assay platforms have emerged as a significant methodology, providing distinct advantages such as sample compartmentalization, high throughput, and minimal analyte consumption. However, inherent complexities, especially in multiplexed detection, remain a challenge. We demonstrate a novel strategy to fabricate a plasmonic droplet assay platform (PDAP) for multiplexed analyte detection, enabling surface-enhanced Raman spectroscopy (SERS). PDAP efficiently splits a microliter droplet into submicroliter to nanoliter droplets under gravity-driven flow by wettability contrast between two distinct regions. The desired hydrophobicity and adhesive contrast between the silicone oil-grafted nonadhesive hydrophilic zone with gold nanoparticles is attained through (3-aminopropyl) triethoxysilane (APTES) functionalization of gold nanoparticles (AuNPs) using a scotch-tape mask. The wettability contrast surface facilitates the splitting of aqueous droplets with various surface tensions (ranging from 39.08 to 72 mN/m) into ultralow volumes of nanoliters. The developed PDAP was used for the multiplexed detection of Rhodamine 6G (Rh6G) and Crystal Violet (CV) dyes. The limit of detection for 120 nL droplet using PDAP was found to be 134 pM and 10.1 nM for Rh6G and CV, respectively. These results align with those from previously reported platforms, highlighting the comparable sensitivity of the developed PDAP. We have also demonstrated the competence of PDAP by testing adulterant spiked milk and obtained very good sensitivity. Thus, PDAP has the potential to be used for the multiplexed screening of food adulterants.

Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species

BackgroundThe sweet potato whitefly (Bemisia tabaci) is a globally important insect pest that damages crops through direct feeding and by transmitting viruses. Current B. tabaci management revolves around the use of insecticides, which are economically and environmentally costly. Host plant resistance is a sustainable option to reduce the impact of whiteflies, but progress in deploying resistance in crops has been slow. A major obstacle is the high cost and low throughput of screening plants for B. tabaci resistance. Oviposition rate is a popular metric for host plant resistance to B. tabaci because it does not require tracking insect development through the entire life cycle, but accurate quantification is still limited by difficulties in observing B. tabaci eggs, which are microscopic and translucent. The goal of our study was to improve quantification of B. tabaci eggs on several important crop species: cassava, cowpea, melon, sweet potato and tomato.ResultsWe tested a selective staining process originally developed for leafhopper eggs: submerging the leaves in McBryde’s stain (acetic acid, ethanol, 0.2% aqueous acid Fuchsin, water; 20:19:2:1) for three days, followed by clearing under heat and pressure for 15 min in clearing solution (LGW; lactic acid, glycerol, water; 17:20:23). With a less experienced individual counting the eggs, B. tabaci egg counts increased after staining across all five crops. With a more experienced counter, egg counts increased after staining on melons, tomatoes, and cowpeas. For all five crops, there was significantly greater agreement on egg counts across the two counting individuals after the staining process. The staining method worked particularly well on melon, where egg counts universally increased after staining for both counting individuals.ConclusionsSelective staining aids visualization of B. tabaci eggs across multiple crop plants, particularly species where leaf morphological features obscure eggs, such as melons and tomatoes. This method is broadly applicable to research questions requiring accurate quantification of B. tabaci eggs, including phenotyping for B. tabaci resistance.

#1457 Human renal proximal tubule-on-a-chip model to study prevention of ischemia and reperfusion injury

Abstract Background and Aims Renal ischemia/reperfusion injury (rIRI) is characterized by restriction of blood supply followed by restoration of blood flow and re-oxygenation and is a leading cause of acute kidney injury (AKI). While animal models are useful for studying systemic manifestations of AKI, their translational relevance is limited. Human in vitro models provide valuable mechanistic insights into rIRI, but often fail to incorporate crucial aspects such as reperfusion injury. To address these limitations, this study aims to develop an advanced in vitro model to study rIRI comprising a perfused three-dimensional (3D) human renal proximal tubule-on-a-chip. Method Our proximal tubule-on-a-chip model comprises a renal proximal tubule alongside a endothelial vessel, separated by extracellular matrix. A microfluidic platform, the OrganoPlate 3-Lane 40, was used to establish the model. The resulting coculture was characterized in terms of its three-dimensional structure, protein expression, and response to nephrotoxins. Subsequently, we simulated rIRI by manipulating oxygen levels, nutrient availability, and perfusion flow settings. The potential protective effect of adenosine was evaluated in the coculture model during ischemia and reperfusion. The extent of injury was assessed through morphological evaluation, caspase 3/7 activation, and cell viability measurements. Results The combination of low oxygen, reduced glucose, and interrupted flow exerted a potent disruptive effect on the proximal tubules, which was strongly amplified upon reperfusion. Endothelial vessels were less sensitive to the ischemia–reperfusion parameters. Adenosine treatment showed a protective effect on the disruption of the epithelium and on the caspase-3/7 activation. Conclusion We successfully developed a human in vitro model to study rIRI utilizing a coculture of a proximal tubule and a blood vessel on-a-chip. This model allowed us to characterize the renoprotective effect of adenosine. The robustness of the model and assays, coupled with the platform's high throughput capabilities, position it as a promising tool for advancing pathophysiological research and facilitating the development of innovative therapeutic modalities aimed at treating rIRI and improving AKI outcomes.

Hardware Efficient Integrated In-loop Filter for HEVC Encoder

The deblocking filter (DF) and the sample adaptive offset (SAO) filter, which aids in enhancing the subjective quality of the image, make up the in-loop filter of the high-efficiency video coding (HEVC) encoder and decoder. The in-loop filter significantly increases the computational load on the HEVC encoder. It is challenging to design an in-loop filter on hardware that can handle intensive computations while using the least amount of on-chip memory, taking external memory traffic and dependencies simultaneously delivering high throughput to support Ultra HD video applications. The proposed design employs the following strategies to address these issues. This work proposes an address generation technique for pipelined horizontal and vertical filtering in DF, that avoids a transpose buffer which otherwise is required. This enables easy pipelining and parallelization thus improving throughput while reducing the on-chip memory utilization. A simplified SAO filter with parallel-pipelined processing is included in the design. These features enable the design to support ultra-HD 7680 × 4320 @ 40 fps video applications. The proposed hardware architecture has a total gate count of 7.73 K LUTs and 2.8 K slice registers, and it is implemented on a 28 nm field programmable gate array (FPGA) platform.

Ion Mobility Mass Spectrometry-Based Disaccharide Analysis of Glycosaminoglycans.

Glycosaminoglycans (GAGs) are linear and acidic polysaccharides. They are ubiquitous molecules, which are involved in a wide range of biological processes. Despite being structurally simple at first glance, with a repeating backbone of alternating hexuronic acid and hexosamine dimers, GAGs display a highly complex structure, which predominantly results from their heterogeneous sulfation patterns. The commonly applied method for compositional analysis of all GAGs is "disaccharide analysis." In this process, GAGs are enzymatically depolymerized into disaccharides, derivatized with a fluorescent label, and then analysed through liquid chromatography. The limiting factor in the high throughput analysis of GAG disaccharides is the time-consuming liquid chromatography. To address this limitation, we here utilized trapped ion mobility-mass spectrometry (TIM-MS) for the separation of isomeric GAG disaccharides, which reduces the measurement time from hours to a few minutes. A full set of disaccharides comprises twelve structures, with eight possessing isomers. Most disaccharides cannot be differentiated by TIM-MS in underivatized form. Therefore, we developed chemical modifications to reduce sample complexity and enhance differentiability. Quantification is performed using stable isotope labelled standards, which are easily available due to the nature of the performed modifications.

Metabolomics Characterization of Disease Markers in Diabetes and Its Associated Pathologies.

With the change in lifestyle of people, there has been a considerable increase in diabetes, which brings with it certain follow-up pathological conditions, which lead to a substantial medical burden. Identifying biomarkers that aid in screening, diagnosis, and prognosis of diabetes and its associated pathologies would help better patient management and facilitate a personalized treatment approach for prevention and treatment. With the advancement in techniques and technologies, metabolomics has emerged as an omics approach capable of large-scale high throughput data analysis and identifying and quantifying metabolites that provide an insight into the underlying mechanism of the disease and its progression. Diabetes and metabolomics keywords were searched in correspondence with the assigned keywords, including kidney, cardiovascular diseases and critical illness from PubMed and Scopus, from its inception to Dec 2023. The relevant studies from this search were extracted and included in the study. This review is focused on the biomarkers identified in diabetes, diabetic kidney disease, diabetes-related development of CVD, and its role in critical illness.

Analyzing high-throughput assay data to advance the rapid screening of environmental chemicals for human reproductive toxicity.

While high-throughput (HTP) assays have been proposed as platforms to rapidly assess reproductive toxicity, there is currently a lack of established assays that specifically address germline development/function and fertility. We assessed the applicability domains of yeast (S. cerevisiae) and nematode (C. elegans) HTP assays in toxicity screening of 124 environmental chemicals, determining their agreement in identifying toxicants and their concordance with reproductive toxicity in vivo. We integrated data generated in the two models and compared results using a streamlined, semi-automated benchmark dose (BMD) modeling approach. We then extracted and modeled relevant mammalian in vivo data available for the matching chemicals included in the Toxicological Reference Database (ToxRefDB). We ranked potencies of common compounds using the BMD and evaluated correlation between the datasets using Pearson and Spearman correlation coefficients. We found moderate to good correlation across the three data sets, with r = 0.48 (95% CI: 0.28-1.00, p<0.001) and rs = 0.40 (p=0.002) for the parametric and rank order correlations between the HTP BMDs; r = 0.95 (95% CI: 0.76-1.00, p=0.0005) and rs = 0.89 (p=0.006) between the yeast assay and ToxRefDB BMDs; and r = 0.81 (95% CI: 0.28-1.00, p=0.014) and rs = 0.75 (p=0.033) between the worm assay and ToxRefDB BMDs. Our findings underscore the potential of these HTP assays to identify environmental chemicals that exhibit reproductive toxicity. Integrating these HTP datasets into mammalian in vivo prediction models using machine learning methods could further enhance the predictive value of these assays in future rapid screening efforts.

Label-free active single-cell encapsulation enabled by microvalve-based on-demand droplet generation and real-time image processing

Droplet microfluidics-based single-cell encapsulation is a critical technology that enables large-scale parallel single-cell analysis by capturing and processing thousands of individual cells. As the efficiency of passive single-cell encapsulation is limited by Poisson distribution, active single-cell encapsulation has been developed to theoretically ensure that each droplet contains one cell. However, existing active single-cell encapsulation technologies still face issues related to fluorescence labeling and low throughput. Here, we present an active single-cell encapsulation technique by using microvalve-based drop-on-demand technology and real-time image processing to encapsulate single cells with high throughput in a label-free manner. Our experiments demonstrated that the single-cell encapsulation system can encapsulate individual polystyrene beads with 96.3 % efficiency and HeLa cells with 94.9 % efficiency. The flow speed of cells in this system can reach 150 mm/s, resulting in a corresponding theoretical encapsulation throughput of 150 Hz. This technology has significant potential in various biomedical applications, including single-cell omics, secretion detection, and drug screening.

NH2-MIL-88B(Fe)/TiO2/PAN electrostatically spun nanofiber membrane for photocatalytic degradation of tetracycline and oil–water separation

Excessive discharge of tetracycline (TC) poses a threat to the environment and human health, and finding a cost-effective treatment method to remove TC from water remains a challenge. In this study, NH2-MIL-88B(Fe)/TiO2/PAN (NMTP) electrostatically spun nanofibrous membranes (ENMs) were prepared, and the NMTP nanofibrous membranes were characterized by water contact angle test, UV–vis and other characterization methods. The results showed that the introduction of NH2-MIL-88B(Fe) expanded the light response range of ENMs, accelerated the production of active substances (•O2– and •OH) and improved the hydrophilicity of the membranes, which resulted in the excellent photocatalytic performance and oil–water separation ability of NMTP ENMs. Among them, the degradation efficiencies of NMTP-4 ENMs for tetracycline (TC), chlortetracycline (CTC), and oxytetracycline (OTC) were up to 93.0 %, 94.9 %, and 86.5 % and the removal rate of TC was still maintained at about 80 % after four photocatalytic regenerations. In addition, the NMTP ENMs exhibited excellent separation performance and high throughput for oil-in-water emulsions, with separation efficiencies of 98.15 %, 97.43 % and 84.87 % for toluene-in-water, n-heptane-in-water and n-hexane-in-water emulsions. Based on the above results, this study provides a new idea for the development of an efficient and stable simultaneous catalytic separation technology for oil-in-water emulsions.

A feasibility study on femtosecond laser texturing of sprayed nanocellulose coatings

Nanocelluloses are emerging as natural materials with favourable properties for coating industry and can be applied by state-of-the-art spraying technology. While additional functionalities are commonly introduced through chemical modification, the surface microstructuring of nanocellulose coatings with high throughput methods remains unexplored. Here, a femtosecond laser is used for texturing spray-coated coatings made of cellulose nanofibrils (CNF) or cellulose nanocrystals (CNC). For coating thickness of 1.5 to 8 μm, processing limits were determined with maximum ablation energy linearly increasing with coating thickness and minimum ablation energy decreasing or increasing depending on the apparent coating density. Within applicable processing window of pulse rate and power setting, the operational ranges were determined for creating one-dimensional and two-dimensional surface patterns, requiring a higher laser energy for CNC compared to CNF coatings and yielding thinnest possible resolved patterns of 17 μm as determined by the laser spot diameter. The laser ablation under low energy corresponds to an increase in surface roughness and intensifies surface hydrophilicity, while the line patterns are able to pin water droplets with rising water contact angles up to 90°. Present feasibility study opens future possibilities for managing surface properties of nanocellulose coatings in applications where tuning of surface hydrophilicity is required.

High throughput substrate screening for interfacial thermal management of β-Ga2O3 by deep convolutional neural network

Electronic devices get smaller and smaller in every generation. In micro-/nano-electronic devices such as high electron mobility transistors, heat dissipation has become a crucial design consideration due to the ultrahigh heat flux that has a negative effect on devices' performance and their lifetime. Therefore, thermal transport performance enhancement is required to adapt to the device size reduction. β-Ga2O3 has recently gained significant scientific interest for future power devices because of its inherent material properties such as extremely wide bandgap, outstanding Baliga's figure of merit, large critical electric field, etc. This work aims to use a machine learning approach to search promising substrates or heat sinks for cooling β-Ga2O3, in terms of high interfacial thermal conductance (ITC), from large-scale potential structures taken from existing material databases. With the ITC dataset of 1633 various substrates for β-Ga2O3 calculated by full density functional theory, we trained our recently developed convolutional neural network (CNN) model that utilizes the fused orbital field matrix (OFM) and composition descriptors. Our model proved to be superior in performance to traditional machine learning algorithms such as random forest and gradient boosting. We then deployed the CNN model to predict the ITC of 32 716 structures in contact with β-Ga2O3. The CNN model predicted the top 20 cubic and noncubic substrates with ITC on the same level as density functional theory (DFT) results on β-Ga2O3/YN and β-Ga2O3/MgO interfaces, which has the highest ITC of 1224 and 1211 MW/m2K, respectively, among the DFT-ITC datasets. Phonon density of states, group velocity, and scattering effect on high heat flux transport and consequently increased ITC are also investigated. Moderate to high phonon density of states overlap, high group velocity, and low phonon scattering are required to achieve high ITC. We also found three Magpie descriptors with strong Pearson correlation with ITC, namely, mean atomic number, mean atomic weight, and mean ground state volume per atom. Calculations of such descriptors are computationally efficient, and therefore, these descriptors provide a new route for quickly screening potential substrates from large-scale material pools for high-performance interfacial thermal management of high-electron mobility transistor devices.