Natural liquid organic hydrogen carrier with low dehydrogenation energy: A first principles study

Liquid organic hydrogen carriers (LOHCs) represent a promising approach for hydrogen storage due to their favorable properties including stability and compatibility with the existing infrastructure. However, fossil-based LOHC molecules are not green or sustainable. Here we examined the possibility of using norbelladine and trisphaeridine, two typical structures of Amaryllidaceae alkaloids, as the LOHCs from the sustainable and renewable sources of natural products. Our first principles thermodynamics calculations reveal low reversibility for the reaction of norbelladine to/from perhydro-norbelladine because of the existence of stabler isomers of perhydro-norbelladine. On the other hand, trisphaeridine is found promising due to its high hydrogen storage capacity ($\sim$5.9 wt\%) and favorable energetics. Dehydrogenation of perhydro-trisphaeridine has an average standard enthalpy change of $\sim$54 KJ/mol-H$_2$, similar to that of perhydro-\textit{N}-ethylcarbazole, a typical LOHC known for its low dehydrogenation enthalpy. This work is a first exploration of Amaryllidaceae alkaloids for hydrogen storage and the results demonstrate, more generally, the potential of bio-based molecules as a new sustainable resource for future large-scale hydrogen storage

Critical dehydrogenation steps of perhydro-N-ethylcarbazole on Ru(0001) surface

Understanding of the critical atomistic steps during the dehydrogenation process of liquid organic hydrogen carriers (LOHCs) is important to the design of cost-efficient, high-performance LOHC catalysts. Based on the density functional theory (DFT) we studied the thermodynamics and kinetics of the complete dehydrogenation path of perhydro-N-ethylcarbazole (12H-NEC) on Ru(0001) surface, involving the adsorption of 12H-NEC, the discharge of H ions onto Ru surface, and the desorption of H2 and hydrogen-lean NEC. It was found that the bonding of nH-NEC is significantly strengthened for n $\le$ 4 because of the flat aromatic ring. Although the whole dehydrogenation process is endothermic, the release of H from nH-NEC, with H adsorbed onto the Ru surface, was found to be exothermic. The desorption of flat, hydrogen-lean NEC, which costs ~255 kJ/mol, was identified as the most energy demanding step. In addition, the effect of surface morphology on adsorption was studied based on an amorphous surface model. Overall, the results imply more efficient dehydrogenation could be achieved from relatively weak bonding of NEC to catalysts, either through engineering catalyst surface (such as surface defects or smaller catalyst particles) or different catalyst materials. Our calculations also revealed possible dealkylation at elevated temperatures

Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis

We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification system for NPC, superior to the current clinical staging system. Between January 2010 and January 2013, 792 patients with recent diagnosis of NPC, who had MR image data, were enrolled in the study. The AutoML algorithm was used and all statistical analyses were based on the 10-fold test. Primary endpoints included the probabilities of overall survival (OS), distant metastasis-free survival (DMFS), and local-region relapse-free survival (LRFS), and their sum was recorded as the final voting score, representative of progression-free survival (PFS) for each patient. The area under the receiver operating characteristic (ROC) curve generated from the MR image data-based model compared with the tumor, node, and metastasis (TNM) system-based model was 0.796 (P=0.008) for OS, 0.752 (P=0.053) for DMFS, and 0.721 (P=0.025) for LRFS. The Kaplan-Meier (KM) test values for II/I, III/II, IV/III groups in our new machine learning-based scoring system were 0.011, 0.010, and <0.001, respectively, whereas those for II/I, III/II, IV/III groups in the TNM/American Joint Committee on Cancer (AJCC) system were 0.118, 0.121, and <0.001, respectively. Significant differences were observed in the new machine learning-based scoring system analysis of each curve (P<0.05), whereas the P values of curves obtained from the TNM/AJCC system, between II/I and III/II, were 0.118 and 0.121, respectively, without a significant difference. In conclusion, the AutoML algorithm demonstrated better prognostic performance than the TNM/AJCC system for NPC. The algorithm showed a good potential for clinical application and may aid in improving counseling and facilitate the personalized management of patients with NPC. The clinical application of our new scoring and staging system may significantly improve precision medicine

Density Functional Theory Study on the Role of Polyacetylene as a Promoter in Selective Hydrogenation of Styrene on a Pd Catalyst

Understanding mechanisms of catalyst–substrate interactions is of essential importance for the design and development of novel catalysts with superior performances. In the present density functional theory study, selective hydrogenation of styrene on a polyacetylene (PA)-supported Pd₄ catalyst (Pd₄/PA) was employed as a model system to address how catalyst–substrate interactions affect the charge state of Pd, which subsequently influences catalytic activity. It was found that the Pd cluster can be anchored strongly on the CC bond of the polymer substrate through the π–d interaction, which further leads to charge rearrangement on the Pd₄ cluster with the top two Pd atoms being more negatively charged. By comparing the calculated minimum energy profiles of styrene hydrogenation on surfaces of both pure Pd₄ and Pd₄/PA, the mechanism that dictates the catalytic process on Pd₄/PA was identified. Charge analysis reveals that the enhanced catalytic activity of Pd₄/PA is largely attributed to the negative charges on the two topmost Pd atoms, which facilitates both hydrogenation of styrene and desorption of the product. Nevertheless, PA hydrogenation to produce polyethylene (PE) was also found to be a potentially viable process with a moderate activation barrier of 0.43 eV, which may consequently lead to the formation of a PE-supported Pd₄ catalytic system. As a consequence, the absence of π orbitals of the PE substrate may significantly reduce the electronic interaction between Pd₄ and PE, which ultimately leads to the catalytic performance similar to the activity on the pure Pd₄ cluster

Mechanisms of autogenous shrinkage for Ultra-High Performance Concrete (UHPC) prepared with pre-wet porous fine aggregate (PFA)

Here, the intrinsic driving mechanisms of autogenous shrinkage for Ultra-High Performance Concrete (UHPC) incorporating porous internal curing (IC) medium are detailly investigated, including characterization and monitoring of UHPC hydration kinetics, internal relative humidity (IRH) and internal temperature (IT) fields. The results show that the autogenous shrinkage evolution of UHPC prepared with pre-wet porous fine aggregate (PFA) undergoes multiple stages. The potential expansion drivers of UHPC with IC at super early hardening period are identified as the extra liquid volume compensation and the effect of thermal expansion. Furthermore, the rapid shrinkage growth stage of the UHPC matrix is governed by the hydration dynamic, while the sustained shrinkage growth stage is managed by cold shrinkage, chemical shrinkage and capillary pressure. Finally, the feasibility of the Powers model in the design of UHPC incorporating wet PFA is carefully revealed, and the key parameters for obtaining UHPC with advanced volumetric stability are elucidated. The strategy in terms of the autogenous shrinkage for the UHPC designed with PFA is revealed

Per-Nucleus Crossover Covariation and Implications for Evolution

International audienceCrossing over is a nearly universal feature of sexual reproduction. Here, analysis of crossover numbers on a per-chromosome and per-nucleus basis reveals a fundamental, evolutionarily conserved feature of meiosis: within individual nuclei, crossover frequencies covary across different chromosomes. This effect results from per-nucleus covariation of chromosome axis lengths. Crossovers can promote evolutionary adaptation. However, the benefit of creating favorable new allelic combinations must outweigh the cost of disrupting existing favorable combinations. Covariation concomitantly increases the frequencies of gametes with especially high, or especially low, numbers of crossovers, and thus might concomitantly enhance the benefits of crossing over while reducing its costs. A four-locus population genetic model suggests that such an effect can pertain in situations where the environment fluctuates: hyper-crossover gametes are advantageous when the environment changes while hypo-crossover gametes are advantageous in periods of environmental stasis. These findings reveal a new feature of the basic meiotic program and suggest a possible adaptive advantage