Rearrangement of three types of fused polycyclic hydrocarbons with carbon atoms 12–14 was conducted using chloroaluminate ionic liquid (IL) as acid catalyst. The hydrocarbons undergo quick configurational isomerization and then skeletal rearrangement toward alkyl-adamantanes. Although having different molecular structure, these hydrocarbons all lead to adamantanes substituted with 2–3 methyl and/or ethyl groups. The alkyl-adamantanes show high density, low freezing point and viscosity, and are very attractive as high-density fuels. Computation using density functional theory confirms that alkyl-adamantanes are thermodynamically favored and explains the experimental product distribution very well. On this basis, the reaction pathway of each hydrocarbon was illustrated. The reaction conditions including acidity (AlCl3 fraction), temperature, and IL dosage show significant effect on the rearrangement. Moreover, the distribution of alkyl-adamantanes can be tuned by adjusting the reaction conditions, allowing the fuels' properties to be fine-tuned. This work provides a versatile route to synthesizing alkyl-diamondoid fuels using simple chemical feedstocks in an effective way.
Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions, stakeholders often require insights into how to alter these decisions. Counterfactual explanations (CFEs) have emerged as a solution, offering interpretations of opaque ML models and providing a pathway to transition from one decision to another. However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none can handle multivariate time-series without training datasets. These limitations can be formidable in many scenarios. In this paper, we present CFWoT, a novel reinforcement-learning-based CFE method that generates CFEs when training datasets are unavailable. CFWoT is model-agnostic and suitable for both static and multivariate time-series datasets with continuous and discrete features. Users have the flexibility to specify non-actionable, immutable, and preferred features, as well as causal constraints which CFWoT guarantees will be respected. We demonstrate the performance of CFWoT against four baselines on several datasets and find that, despite not having access to a training dataset, CFWoT finds CFEs that make significantly fewer and significantly smaller changes to the input time-series. These properties make CFEs more actionable, as the magnitude of change required to alter an outcome is vastly reduced.
Wine is a high-value alcoholic beverage welcomed by consumers because of its flavor and nutritional value. The key information on wine bottle label is the basis of consumers' choice, which also becomes a target for manufacturers to adulterate, including geographical origin, grape variety and vintage. With the improvement of wine adulteration technology, modern technological means are needed to solve the above mentioned problems. The chemical basis of wine determines the type of technique used. Detection technology can be subdivided into four groups: mass spectrometry techniques, spectroscopic techniques, chromatography techniques, and other techniques. Multivariate statistical analysis of the data was performed by means of chemometrics methods. This paper outlines a series of procedures for wine classification and identification, and classified the analytical techniques and data processing methods used in recent years with listing their principles, advantages and disadvantages to help wine researchers choose appropriate methods to meet the challenge and ensure wine traceability and authenticity.
Cholangiocarcinoma (CCA) is a malignancy arising from multiple locations along the biliary tree, which is still lacking effective diagnostic biomarkers. The present study aimed to provide a comprehensive differential gene expression profile for the disease. The differentially expressed genes (DEGs) for CCA were explored in‑depth using a Gene Expression Omnibus (GEO) dataset, an internal cohort of clinical participants as well as <em>in vitro</em> experiments with the HUCCT1 cell line. Based on the GEO dataset, potential biomarker genes were proposed and the enriched biological processes as well as signaling pathways were further investigated. A protein‑protein interaction network of target genes was established. In the clinical specimens, the functions of the primary candidate, FBJ murine osteosarcoma viral oncogene homolog B (FOSB), were evaluated by reverse transcription‑quantitative (RT‑q)PCR and western blot analysis. A Cell Counting Kit‑8 (CCK‑8) assay was used for a functional study on FOSB. The results indicated that, compared with non‑tumor bile duct tissues, primary CCA samples had 676 differentially expressed genes, including 277 downregulated and 399 upregulated ones. Among these, HBD, FOSB, HBB, ITIH2, FCGBP, MT1JP, PIJR, SLC38A1, COL10A1 and MMP19 were determined to be the most significant DEGs. At the same time, upregulated genes were enriched in 'vasculature development' and 'IL‑17 signaling pathways'. Downregulated genes were enriched in 'extracellular matrix progress' and 'glucuronate signaling pathway'. The patients with CCA displayed decreased levels of hemoglobin. Compared with paracancerous tissues, CCA cancerous tissues exhibited increased RNA and protein expression levels of FOSB according to RT‑qPCR and western blot analysis, respectively. Furthermore, FOSB expression influenced the proliferation/viability of the CCA cell line HUCCT1. In conclusion, the present study suggested that the FOSB gene may serve as a primary biomarker candidate for CCA, providing a valuable reference for its clinical implementation.
Over three-fifths of the world's known crude oil cannot be recovered using state-of-the-art techniques, but microbial conversion of petroleum hydrocarbons trapped in oil reservoirs to methane is one promising path to increase the recovery of fossil fuels. The process requires cooperation between syntrophic bacteria and methanogenic archaea, which can be affected by sulfate-reducing prokaryotes (SRPs). However, the effects of sulfate on hydrocarbon degradation and methane production remain elusive, and the microbial communities involved are not well understood. In this study, a methanogenic hexadecane-degrading enrichment culture was treated with six different concentrations of sulfate ranging from 0.5 to 25 mM. Methane production and maximum specific methane production rate gradually decreased to 44 and 56% with sulfate concentrations up to 25 mM, respectively. There was a significant positive linear correlation between the sulfate reduction/methane production ratio and initial sulfate concentration, which remained constant during the methane production phase. The apparent methanogenesis fractionation factor (α app) gradually increased during the methane production phase in each treatment, the α app for the treatments with lower sulfate (0.5–4 mM) eventually plateaued at ~1.047, but that for the treatment with 10–25 mM sulfate only reached ~1.029. The relative abundance levels of Smithella and Methanoculleus increased almost in parallel with the increasing sulfate concentrations. Furthermore, the predominant sulfate reducer communities shifted from Desulfobacteraceae in the low-sulfate cultures to Desulfomonile in the high-sulfate cultures. The distribution of hexadecane carbon between methane-producing and sulfate-reducing populations is dependent on the initial sulfate added, and not affected during the methane production period. There was a relative increase in hydrogenotrophic methanogenesis activity over time for all sulfate treatments, whereas the total activity was inhibited by sulfate addition. Both Smithella and Methanoculleus, the key alkane degraders and methane producers, can adapt to sulfate stress. Specifically, different SRP populations were stimulated at various sulfate concentrations. These results could help to evaluate interactions between sulfate-reducing and methanogenic populations during anaerobic hydrocarbon degradation in oil reservoirs.
Composite photocatalysts are crucial for photocatalytic hydrogen evolution. In this work, ZrO2/CdSe-diethylenetriamine (ZrO2/CdSe-DETA) heterojunction nanocomposites are synthesized, and efficiently and stably catalyzed hydrogen evolution under visible light. X-ray photoelectron spectroscopy (XPS) and high resolution transmission electron microscope (HRTEM) confirm the formation of heterojunctions between ZrO2 (ZO) and CdSe-DETA (CS). Ultraviolet–visible spectroscopy diffuse reflectance spectra (UV-vis DRS), Mott–Schottky, and theoretical calculations confirm that the mechanism at the heterojunction of the ZrO2/CdSe-DETA (ZO/CS) nanocomposites is Type-I. Among the ZO/CS nanocomposites (ZO/CS-0.4, ZO/CS-0.6, and ZO/CS-0.8; in the nanocomposites, the mass ratio of ZO to CS is 0.1:0.0765, 0.1:0.1148, and 0.1:0.1531, respectively). ZO/CS-0.6 nanocomposite has the best photocatalytic hydrogen evolution activity (4.27 mmol g−1 h−1), which is significantly higher than ZO (trace) and CS (1.75 mmol g−1 h−1). Within four cycles, the ZO/CS-0.6 nanocomposite maintains an efficient catalytic hydrogen evolution rate. Due to the existence of the heterojunction of the composites, the photogenerated electron-hole pairs can be effectively separated, which accelerates the photocatalytic hydrogen evolution reaction and reduces the progress of photocorrosion. This work reveals the feasibility of ZO/CS nanocomposite photocatalysts for hydrogen evolution.
The access of the distributed power supply completely changes the structure and operating state of the distribution network, transforming the distribution network from a single-ended power supply structure and unidirectional power flow mode to multi-terminal power supply structure and a frequently changing bidirectional power flow mode. The high proportion of renewable distributed power distribution is distributed to the distribution network, and its output randomness superimposes the load volatility, which brings great difficulties to the optimization control and the operation situation prediction analysis, so that the potential of multi-type reactive power equipment can be fully explored, from Source-network-load-storage multi-dimensional broadening of reactive power optimization control "feasible domain" becomes necessary.
