As the base material of the printed circuit boards (PCBs), copper clad laminates (CCLs) with high thermal conductivity, low dielectric loss and high peel strength have been greatly desired especially ...for the upcoming 5G age. In this study, hexagonal boron nitride (hBN) was firstly introduced into the polymer insulating filler (Polyphenylene ether resin, PPER) of copper clad laminates for high frequency applications. The as-obtained CCLs exhibited a high thermal conductivity up to 1W/m·K and a low dielectric loss of ~4×10−3. Meanwhile, the interface tailoring of hBN was also tried by SiO2 coating, which enhanced the peel strength to a maximum value of 1.1N/mm, but changed the thermal conductivity behavior of the CCLs from the Agari's linear model to the modified quadratic model due to the interfacial thermal resistance effect of SiO2 coating on hBN. Moreover, the flexural strength and water absorption were also tested. The results revealed that, comparing with the related composite materials reported in literature and commercial products, the CCLs obtained in this paper have outstanding advantages in thermal and dielectric properties, validating the high potential of the present CCLs for use in high frequency PCBs.
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•Polyphenylene ether resin filled with hBN or hBN@SiO2 were used as an insulation layer of copper clad laminates.•The as-obtained copper clad laminates exhibited a high advantage in thermal and dielectric properties.•A modified model for the thermal conductivity was proposed taking into consideration of the SiO2 effect of on hBN.
Herein a novel series of histone deacetylases (HDACs) and epidermal growth factor receptor (EGFR) dual inhibitors were designed and synthesized based on the structure of the approved EGFR inhibitor ...osimertinib (AZD9291). Among them, four compounds
,
,
and
exhibited more potent total HDAC inhibition than the approved HDAC inhibitor SAHA. However, these compounds only showed moderate to low inhibitory potency towards EGFR with compounds
and
possessing IC
values against EGFR
and EGFRT
in the micromolar range. 3-4,5-dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay revealed the potent antiproliferative activities of compounds
,
,
and
, among which
was even more potent against HeLa, MDA-MB-231, MDA-MB-468, HT-29 and KG-1 cell lines than SAHA and AZD9291. Further selectivity profile of
showed that this compound was not active against other 13 cancer-related kinases and two epigenetic targets lysine specific demethylase 1 (LSD1) and bromodomain-containing protein 4 (BRD4). These results support further structural modification of
to improve its EGFR inhibitory activity, which will lead to more potent and balanced HDAC and EGFR dual inhibitors as anticancer agents.
With five histone deacetylase (HDAC) inhibitors approved for cancer treatment, proteolysis-targeting chimeras (PROTACs) for degradation of HDAC are emerging as an alternative strategy for ...HDAC-targeted therapeutic intervention. Herein, three bestatin-based hydroxamic acids (
,
and
) were designed, synthesized and biologically evaluated to see if they could work as HDAC degrader by recruiting cellular inhibitor of apoptosis protein 1 (cIAP1) E3 ubiquitin ligase. Among the three compounds, the bestatin-SAHA hybrid
exhibited comparable even more potent inhibitory activity against HDAC1, HDAC6 and HDAC8 relative to the approved HDAC inhibitor SAHA. It is worth noting that although
could not lead to intracellular HDAC degradation after 6 h of treatment, it could dramatically decrease the intracellular levels of HDAC1, HDAC6 and HDAC8 after 24 h of treatment. Intriguingly, the similar phenomenon was also observed in the HDAC inhibitor SAHA. Cotreatment with proteasome inhibitor bortezomib could not reverse the HDAC decreasing effects of
and SAHA, confirming that their HDAC decreasing effects were not due to protein degradation. Moreover, all three bestatin-based hydroxamic acids
,
and
exhibited more potent aminopeptidase N (APN, CD13) inhibitory activities than the approved APN inhibitor bestatin, which translated to their superior anti-angiogenic activities. Taken together, a novel bestatin-SAHA hybrid was developed, which worked as a potent APN and HDAC dual inhibitor instead of a PROTAC.
Background T lymphocytes, integral to the adaptive immune system, wield pivotal influence in bolstering anti-tumor responses, and are strictly regulated by ubiquitination modification. The objective ...of this investigation was to devise a novel prognostic and immunotherapeutic efficacy predictor for hepatocellular carcinoma patients utilizing T cell-related ubiquitination genes (TCRUG). Method The single-cell RNA sequencing (scRNA-seq) data and bulk RNA data of HCC patients are derived from the GEO database and TCGA database. Based on the processing of scRNA-seq, T cell marker genes are obtained and TCRUG is obtained. Further combined with WGCNA, differential analysis, univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis to filter and screen TCRUG. Finally construct a riskscore for predicting the prognosis of HCC patients, the predictive effect of which is validated in the GEO dataset. In addition, we also studied the correlation between riskscore and immunotherapy efficacy. Finally, the oncogenic role of UBE2E1 in HCC was explored through various in vitro experiments. Result Based on patients’ scRNA-seq data, we finally obtained 3050 T cell marker genes. Combined with bulk RNA data and clinical data from the TCGA database, we constructed a riskscore that accurately predicts the prognosis of HCC patients. This riskscore is an independent prognostic factor for HCC and is used to construct a convenient column chart. In addition, we found that the high-risk group is more suitable for immunotherapy. Finally, the proliferation, migration, and invasion abilities of HCC cells significantly decreased after UBE2E1 expression reduction. Conclusion This study developed a riskscore based on TCRUG that can accurately and stably predict the prognosis of HCC patients. This riskscore is also effective in predicting the immune therapy response of HCC patients.
The lithium-containing solution is also rich in lithium after preparation of lithium carbonate. With the depletion of primary lithium resource, it is necessary to recovery lithium from a low ...concentrated lithium-containing solution which can solve the shortage of lithium resources and avoid the waste of lithium. In this article, the lithium phosphate is recovered from lithium-containing solution with a concentration of 2 g/L after preparation of lithium carbonate. The results show that by the application of ultrasound, the lithium recovery rate can be increased. The concentration of lithium is less than 0.3 g/L after preparation of lithium phosphate. For lithium carbonate recovery by ultrasound, please refer to the full length article entitled “Lithium carbonate recovery from lithium-containing solution by ultrasound assisted precipitation”, https://doi.org/10.1016/j.ultsonch.2018.12.025 (Chunlong Zhao et al., 2019) 1.
Municipal solid waste incineration fly ash is classified as the hazardous waste because of its high levels of heavy metals alkali chlorides, and polychlorinated dibenzo-p-dioxins. Thermal treatment ...is widely used for fly ash treatment because of its advantages of reduction and harmless. The transformation behaviors of chlorine and metal ions during the thermal treatment of fly ash has a significant impact on the harmless and resource of fly ash. At present, the migration behaviors of chlorine and metal ions during thermal treatment of fly ash is not clearly demonstrated. In this manuscript, the phase compositions, transformation behaviors, the variation of mass and content of chlorine and various metal ions were analyzed through diverse characterization methods under different sintering temperatures to understand the migration behaviors of chlorine and metal ions during thermal treatment. Roasting experiments showed that the migration behaviors of heavy metals and chlorides were consistent. The chlorine, sodium, potassium and heavy metal ions can be removed sharply while the calcium, aluminum, magnesium and iron were decreased slightly when the roasting temperature was above 750 °C. The findings also suggested that removed chlorides were soluble chlorides and unstable crystals in municipal solid waste incineration fly ash were inclined to formed steady structure under high temperature. The structure of roasted fly ash became denser and generated ceramic-like particle due to thermal agglomeration and chemical reactions.
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•Soluble chlorides will be transformed into insoluble chlorides under high temperature.•Migration behaviors of chlorides and heavy metal ions were consistent.•Unstable crystals in MSWI FA were inclined to formed steady structure under high temperature.
Carbon nanotubes (CNTs)-dispersed ceramics were usually obtained by simple mixing, or firstly dispersing the metal catalysts inside ceramic powders, and then growing CNTs from the thermal ...decomposition of hydrocarbon gas. In the present study, a novel route for the fabrication of Al2O3–Ni–CNTs nanocomposites was proposed by co-precipitation of CNTs and Ni nanoparticle on Al2O3 powder using nickelocene as a precursor in a rotary CVD reactor. Fine Ni nanoparticles (10–50 nm in diameter) and CNTs (20–50 nm in diameter and as long as 1 µm in length) were uniformly dispersed on agitated Al2O3 powders. After spark plasma sintering at 1923 K for 0.6 ks, the Al2O3–Ni–CNTs nanocomposites showed uniform microstructure and enhanced mechanical properties. Carbon incorporated in nickel changed from amorphous to crystalline phase state after the high temperature treatment. No other impurities were identified, and the incorporation of CNTs and Ni was also found to enhance the relative density and mechanical properties of Al2O3. Thus the present method is promising for fabrication of high performance CNTs-ceramic composites.
Municipal solid waste (MSW) amount has direct influence on MSW management, policy-decision making, and MSW treatment methods. Machine learning has great potential for prediction, but few studies ...apply the approaches of deep learning to forecast the quantity of MSW. Therefore, the aim of this study is to evaluate the feasibility and practicability of employing the methods of supervised learning, including Attention, one-dimension Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) to predict the MSW Amount in Shanghai. Integrated 1D-CNN and LSTM with Attention model, the new structure model (1D-CNN-LSTM-Attention, 1D-CLA), is designed to forecast MSW amount. In addition, the influence of socioeconomic factors on MSW amount, the structure and layers distribution of Attention, 1D-CNN, LSTM and 1D-CLA are also discussed. The results indicate that the correlation coefficients of Attention, one-dimension CNN, LSTM, and proposed 1D-CLA model to predict the MSW in Shanghai are 78%, 86.6%, 90%, and 95.3%, respectively, suggesting the feasible and practicable. The values of 24, 0.01, 50 and 25 for the number of neurons, dropout, the value of epoch number and Batch size best fit 1D-CLA to predict the amount of MSW in Shanghai. Furthermore, the performance of 1D-CLA is better than any single model or two model's combination (R2 is 95.3%) and the mechanism of 1D-CLA is contributed by three former models following the order: LSTM>CNN>Attention.
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•The influence of socioeconomic factors on MSW amount were considered.•The approaches of Deep learning to forecast the MSW quantity is feasible.•The new structure model was designed to predict the amount of MSW.•1D-CLA performance is better than any single model or two model's combination.
Increasing generation of municipal solid waste, heterogeneity of waste composition, and complex processes of waste management and recovery have limited the performance of traditional treatment ...approaches. It is urgent to innovate waste management toward smarter and more efficient modes and break up the bottlenecks of the current system. Recently, deep learning has emerged as a powerful method for revealing hidden patterns or deducing correlations for which traditional treatment approaches face limitations or challenges. However, deep learning concepts and practices have not been widely utilized by researches in municipal solid waste management (MSWM). Herein, this research provides a critical review for deep learning and its application in MSWM. The framework and algorithms of a variety of deep learning methods have been compared and assessed. A body of deep learning applications have been reviewed according to their engagement in waste collection, transportation, and final disposal. Application of deep learning in MSWM stays in its infancy and requires great efforts for further development. The challenges and futures opportunities in the application of deep learning in the MSWM have been discussed to highlight the potential of deep learning in this field.
Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM ...pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (<138 °C), de-volatilization (138–570 °C), and decomposition stage (≥570 °C above). The de-volatilization can further be divided into stage 2 and stage 3, with values of activation energy estimated by Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods as 123.35 and 172.95 kJ/mol, respectively. The gas products like H2O, CO2, CH4, and CO, as well as functional groups like phenols and carbonyl (CO), were all detected during the process of FM pyrolysis by thermogravimetric-fourier transform infrared spectrometry at a heating rate of 10 °C/min. The main species detected by pyrolysis-gas chromatography-mass spectrometry analyzer included acid (41.98%) and aliphatic hydrocarbon (22.44%). Finally, the 1D–CNN–LSTM algorithm demonstrated an outstanding generalization capability to predict the relationship between FM composition and temperature, with R2 reaching 93.91%. In sum, this study provided a reference for the treatment of FM from MSW classification as well as the feasibility and practicability of deep learning applied in pyrolysis.
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•Pyrolysis is considered to dispose of FM from MSW classification.•Deep learning was firstly applied to predict TG data.•Pyrolytic performance, kinetics, and gaseous products of FM are discussed.•1D–CNN–LSTM shows an outstanding performance to predict TG data (R2 = 93.91%).
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