### Recent Submissions

• #### Effect of Fe oxidation state (+2 versus +3) in precursor on the structure of Fe oxides/carbonates-based composites examined by XPS, FTIR and EXAFS

(Solid State Sciences, Elsevier BV, 2021-10) [Article]
• #### Combining X-ray Diffraction and X-ray Absorption Spectroscopy to Unveil Zn Local Environment in Zn-Doped ZrO2 Catalysts

(The Journal of Physical Chemistry C, American Chemical Society (ACS), 2021-09-29) [Article]
• #### Untargeted metabolic profiling of extracellular vesicles of sars-cov-2-infected patients shows presence of potent anti-inflammatory metabolites

(International Journal of Molecular Sciences, MDPI AG, 2021-09-28) [Article]
Extracellular vesicles (EVs) carry important biomolecules, including metabolites, and contribute to the spread and pathogenesis of some viruses. However, to date, limited data are available on EV metabolite content that might play a crucial role during infection with the SARS-CoV-2 virus. Therefore, this study aimed to perform untargeted metabolomics to identify key metabolites and associated pathways that are present in EVs, isolated from the serum of COVID-19 patients. The results showed the presence of antivirals and antibiotics such as Foscarnet, Indinavir, and lymecycline in EVs from patients treated with these drugs. Moreover, increased levels of anti-inflammatory metabolites such as LysoPS, 7-α,25-Dihydroxycholesterol, and 15-d-PGJ2 were detected in EVs from COVID-19 patients when compared with controls. Further, we found decreased levels of metabolites associated with coagulation, such as thromboxane and elaidic acid, in EVs from COVID-19 patients. These findings suggest that EVs not only carry active drug molecules but also anti-inflammatory metabolites, clearly suggesting that exosomes might play a crucial role in negotiating with heightened inflammation during COVID-19 infection. These preliminary results could also pave the way for the identification of novel metabolites that might act as critical regulators of inflammatory pathways during viral infections.
• #### Seismic Inversion by Hybrid Machine Learning

(Journal of Geophysical Research: Solid Earth, American Geophysical Union (AGU), 2021-09-23) [Article]
We present a hybrid machine learning (HML) inversion method, which uses the latent space (LS) features of a convolutional autoencoder (CAE) to estimate the subsurface velocity model. The LS features are the effective low-dimensional representation of the high-dimensional seismic data. However, no equations exist to describe the relationship between the perturbation of an LS feature and the velocity perturbation. To address this problem, we use automatic differentiation (AD) to connect the two terms. Following this step, we use the wave-equation inversion to invert the LS features for the subsurface velocity model. The HML misfit function measures the LS feature differences between the observed and predicted seismic data in a low-dimensional space, which is less affected by the cycle-skipping problem compared to the waveform mismatch in a high-dimensional space. A low dimensional LS feature mainly contains the kinematic information of seismic data, while a large dimensional LS feature can also preserve the dynamic information of seismic data. Therefore, the HML inversion can recover the subsurface velocity model in a multiscale approach by inverting the LS features with different dimensions. Based on the different ways of utilizing AD to compute the velocity gradient, we propose full- and semi-automatic approaches to solve this problem. These two approaches are mathematically equivalent; the former is easier to implement, while the latter is computationally more efficient. Numerical tests show that the HML inversion method can effectively recover both the low- and high-wavenumber velocity information by inverting the LS features with different dimensions.
• #### A semi-Lagrangian scheme for Hamilton-Jacobi-Bellman equations with oblique boundary conditions

(arXiv, 2021-09-21) [Preprint]
We investigate in this work a fully-discrete semi-Lagrangian approximation of second order possibly degenerate Hamilton-Jacobi-Bellman (HJB) equations on a bounded domain with oblique boundary conditions. These equations appear naturally in the study of optimal control of diffusion processes with oblique reflection at the boundary of the domain. The proposed scheme is shown to satisfy a consistency type property, it is monotone and stable. Our main result is the convergence of the numerical solution towards the unique viscosity solution of the HJB equation. The convergence result holds under the same asymptotic relation between the time and space discretization steps as in the classical setting for semi-Lagrangian schemes. We present some numerical results that confirm the numerical convergence of the scheme.
• #### High carrier mobility in single-crystal PtSe2 grown by molecular beam epitaxy on ZnO(0001)

(arXiv, 2021-09-16) [Preprint]
PtSe2 is attracting considerable attention as a high mobility two-dimensional material with envisionned applications in microelectronics, photodetection and spintronics. The growth of high quality PtSe2 on insulating substrates with wafer-scale uniformity is a prerequisite for electronic transport investigations and practical use in devices. Here, we report the growth of highly oriented few-layers PtSe2 on ZnO(0001) by molecular beam epitaxy. The crystalline structure of the films is characterized with electron and X-ray diffraction, atomic force microscopy and transmission electron microscopy. The comparison with PtSe2 layers grown on graphene, sapphire, mica, SiO2 and Pt(111) shows that among insulating substrates, ZnO(0001) yields films of superior structural quality. Hall measurements performed on epitaxial ZnO/PtSe2 with 5 monolayers of PtSe2 show a clear semiconducting behaviour and a high mobility in excess of 200 cm2V 1s-1 at room temperature and up to 447 cm2V-1s-1 at low temperature.
• #### The Air–Water Interface of Condensed Water Microdroplets does not Produce H2O2

(arXiv, 2021-09-07) [Preprint]
Recent reports on the production of hydrogen peroxide (H2O2) on the surface of condensed water microdroplets without the addition of catalysts or additives have sparked significant interest. The underlying mechanism isspeculated to be ultrahigh electric fields at the air-water interface;smaller droplets present higher interfacial area and produce higher (detectable) H2O2 yields. Herein, we present an alternative explanation for these experimental observations. We compare H2O2 production in water microdroplets condensed from vapor produced via (i) heating water to 50–70℃ and (ii) ultrasonic humidification (as exploited in the original report). Water microdroplets condensed after heating do not show any enhancement in the H2O2 level in comparison to the bulk water, regardless of droplet size or the substrate wettability. In contrast, those condensed after ultrasonic humidification produce significantly higher H2O2 quantities. We conclude that the ultrasonication of water contributes to the H2O2 production, not droplet interfacial effects.
• #### A threefold approach including quantum chemical, molecular docking and molecular dynamic studies to explore the natural compounds from Centaurea jacea as the potential inhibitors for COVID-19

(Brazilian Journal of Biology, FapUNIFESP (SciELO), 2021-09-03) [Article]
Abstract In the current report, we studied the possible inhibitors of COVID-19 from bioactive constituents of Centaurea jacea using a threefold approach consisting of quantum chemical, molecular docking and molecular dynamic techniques. Centaurea jacea is a perennial herb often used in folk medicines of dermatological complaints and fever. Moreover, anticancer, antioxidant, antibacterial and antiviral properties of its bioactive compounds are also reported. The Mpro (Main proteases) was docked with different compounds of Centaurea jacea through molecular docking. All the studied compounds including apigenin, axillarin, Centaureidin, Cirsiliol, Eupatorin and Isokaempferide, show suitable binding affinities to the binding site of SARS-CoV-2 main protease with their binding energies -6.7 kcal/mol, -7.4 kcal/mol, -7.0 kcal/mol, -5.8 kcal/mol, -6.2 kcal/mol and -6.8 kcal/mol, respectively. Among all studied compounds, axillarin was found to have maximum inhibitor efficiency followed by Centaureidin, Isokaempferide, Apigenin, Eupatorin and Cirsiliol. Our results suggested that axillarin binds with the most crucial catalytic residues CYS145 and HIS41 of the Mpro, moreover axillarin shows 5 hydrogen bond interactions and 5 hydrophobic interactions with various residues of Mpro. Furthermore, the molecular dynamic calculations over 60 ns (6×106 femtosecond) time scale also shown significant insights into the binding effects of axillarin with Mpro of SARS-CoV-2 by imitating protein like aqueous environment. From molecular dynamic calculations, the RMSD and RMSF computations indicate the stability and dynamics of the best docked complex in aqueous environment. The ADME properties and toxicity prediction analysis of axillarin also recommended it as safe drug candidate. Further, in vivo and in vitro investigations are essential to ensure the anti SARS-CoV-2 activity of all bioactive compounds particularly axillarin to encourage preventive use of Centaurea jacea against COVID-19 infections.
• #### Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning

(Journal of the American Chemical Society, American Chemical Society (ACS), 2021-09-01) [Article]
Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.
• #### Selective Hydrocracking Polyaromatics into Light Aromatics: the Separation of Hydrogenation Center and Cracking Center

(ACS Sustainable Chemistry & Engineering, American Chemical Society (ACS), 2021-09-01) [Article]
Serial combined catalysts with separated hydrogenation centers and cracking centers, which were composed of CoMo/Al2O3 and Ni/B, were synthesized and compared with the normal bifunctional catalysts of NiMo/Al2O3–Beta and CoMo/Al2O3–Beta. The properties of different catalysts were characterized by various methods. H2-TPR results demonstrated the existence of hydrogen spillover between a Co or Ni promoter, and Mo metals could facilitate the reducibility of oxide Mo species and hydrogenation of aromatics. The naphthalene hydrocracking performances were also evaluated and compared with different catalysts. The combined catalysts showed relatively high yields of light aromatics with high-octane values (<C10 aromatics) and low yields of cyclanes. The reason should be correlated to the synergistic effect of CoMo/Al2O3 (hydrogenation center) with a high selectivity of converting naphthalene into C10 aromatics and the Ni/B (cracking center) with high activity of cracking C10 aromatics into light aromatics. The kinetic and thermodynamic analyses confirmed that the naphthalene hydrocracking activity of the CoMo/Al2O3–Beta catalyst was the highest, and the combined catalysts showed higher selectivity of converting naphthalene into light aromatics than NiMo/Al2O3–Beta and CoMo/Al2O3–Beta catalysts.
• #### Growth of highly conductive Al-rich AlGaN:Si with low group-III vacancy concentration

(AIP Advances, AIP Publishing, 2021-09-01) [Article]
• #### Specificity and Synergy at the Oil–Brine Interface: New Insights from Experiments and Molecular Dynamics Simulations

(Energy & Fuels, American Chemical Society (ACS), 2021-08-30) [Article]
The interfacial tension (IFT) between oil and brine is a key parameter affecting the enhanced oil recovery process. Despite the several theoretical and experimental investigations on the oil–brine system, the salinity effect on the IFT of oil–brine is still not fully understood. There is a contradiction in the literature rather than consistency. In the present study, we combine molecular dynamics (MD) simulations with the pendant drop method to investigate the molecular interactions at the oil–brine interface to better understand the salinity–IFT relationship. Herein, we are taking into account the complex composition of both crude oil and brine and the pH and total acid number. Different salinity conditions have been considered ranging from deionized water to connate (formation) water. We also consider the effects of individual brines of the main alkali salts (i.e., NaCl, MgCl2, and CaCl2) that are common in carbonate reservoirs. The specificity and synergy of the molecular interactions are observed via the confrontation of the results of the mixed brines (seawater and formation water) with those of the individual brines. We observed a significant impact of the divalent cations on the oil–brine interfacial tension. Due to the specificity of the organic acid–Ca2+ type of interaction and the synergy between the different ions, complete encapsulation of the Ca2+ ions has been observed within the formation water brine. This induces the depletion of the organic acids at the interface and thus increases the IFT. Such ionic encapsulation has not been observed in the individual brines because the cation–anion (Cl–) and the cation–water interactions are strong enough to prevent the cation–acid encapsulation. The interplay between the electrostatic interactions and the cations’ dehydration-free energies is the main parameter that controls their specificity and synergy, affecting the oil–brine interfacial properties. This work provides important details on the ionic interactions influencing the interfacial properties between crude hydrocarbons and brine.
• #### A Comparison of Turbulence Generated by 3DS Sparse Grids with Different Blockage Ratios and Different Co-frame Arrangements

(Springer International Publishing, 2021-08-30) [Conference Paper]
A new type of grid turbulence generator, the 3D sparse grid (3DS), is a co-planar arrangement of co-frames each containing a different length scale of grid elements [Malik, N. A. US Patent No. US 9,599,269 B2 (2017)] and possessing a much bigger parameter space than the flat 2D fractal square grid (2DF). Using DNS we compare the characteristics of the turbulence (mean flow, turbulence intensity, energy spectrum) generated by different types of 3DS grids. The peak intensities generated by 3DS can exceed the peaks generated by the 2DF by 80%; we observe that a 3DS with blockage ratio 24% produces turbulence similar to the 2DF with blockage ratio 32% implying lower energy input for the same turbulence.
• #### Environmental vulnerability of the global ocean epipelagic plankton community interactome.

(Science advances, American Association for the Advancement of Science (AAAS), 2021-08-28) [Article]
Marine plankton form complex communities of interacting organisms at the base of the food web, which sustain oceanic biogeochemical cycles and help regulate climate. Although global surveys are starting to reveal ecological drivers underlying planktonic community structure and predicted climate change responses, it is unclear how community-scale species interactions will be affected by climate change. Here, we leveraged $\textit{Tara}$ Oceans sampling to infer a global ocean cross-domain plankton co-occurrence network-the community interactome-and used niche modeling to assess its vulnerabilities to environmental change. Globally, this revealed a plankton interactome self-organized latitudinally into marine biomes (Trades, Westerlies, Polar) and more connected poleward. Integrated niche modeling revealed biome-specific community interactome responses to environmental change and forecasted the most affected lineages for each community. These results provide baseline approaches to assess community structure and organismal interactions under climate scenarios while identifying plausible plankton bioindicators for ocean monitoring of climate change.
• #### Diel pCO2 fluctuations alter the molecular response of coral reef fishes to ocean acidification conditions

(Molecular Ecology, Wiley, 2021-08-28) [Article]
Environmental partial pressure of CO2 (pCO2) variation can modify the responses of marine organisms to ocean acidification, yet the underlying mechanisms for this effect remain unclear. On coral reefs, environmental pCO2 fluctuates on a regular day–night cycle. Effects of future ocean acidification on coral reef fishes might therefore depend on their response to this diel cycle of pCO2. To evaluate the effects on the brain molecular response, we exposed two common reef fishes (Acanthochromis polyacanthus and Amphiprion percula) to two projected future pCO2 levels (750 and 1,000 µatm) under both stable and diel fluctuating conditions. We found a common signature to stable elevated pCO2 for both species, which included the downregulation of immediate early genes, indicating lower brain activity. The transcriptional programme was more strongly affected by higher average pCO2 in a stable treatment than for fluctuating treatments, but the largest difference in molecular response was between stable and fluctuating pCO2 treatments. This indicates that a response to a change in environmental pCO2 conditions is different for organisms living in a fluctuating than in stable environments. This differential regulation was related to steroid hormones and circadian rhythm (CR). Both species exhibited a marked difference in the expression of CR genes among pCO2 treatments, possibly accommodating a more flexible adaptive approach in the response to environmental changes. Our results suggest that environmental pCO2 fluctuations might enable reef fishes to phase-shift their clocks and anticipate pCO2 changes, thereby avoiding impairments and more successfully adjust to ocean acidification conditions.
• #### An Efficient ADER-DG Local Time Stepping Scheme for 3D HPC Simulation of Seismic Waves in Poroelastic Media

(arXiv, 2021-08-24) [Preprint]
Many applications from geosciences require simulations of seismic waves in porous media. Biot's theory of poroelasticity describes the coupling between solid and fluid phases and introduces a stiff source term, thereby increasing computational cost and motivating efficient methods utilising High-Performance Computing. We present a novel realisation of the discontinuous Galerkin scheme with Arbitrary DERivative time stepping (ADER-DG) that copes with stiff source terms. To integrate this source term with a reasonable time step size, we use an element-local space-time predictor, which needs to solve medium-sized linear systems - with 1000 to 10000 unknowns - in each element update (i.e., billions of times). We present a novel block-wise back-substitution algorithm for solving these systems efficiently. In comparison to LU decomposition, we reduce the number of floating-point operations by a factor of up to 25. The block-wise back-substitution is mapped to a sequence of small matrix-matrix multiplications, for which code generators are available to generate highly optimised code. We verify the new solver thoroughly in problems of increasing complexity. We demonstrate high-order convergence for 3D problems. We verify the correct treatment of point sources, material interfaces and traction-free boundary conditions. In addition, we compare against a finite difference code for a newly defined layer over half-space problem. We find that extremely high accuracy is required to resolve the slow P-wave at a free surface, while solid particle velocities are not affected by coarser resolutions. By using a clustered local time stepping scheme, we reduce time to solution by a factor of 6 to 10 compared to global time stepping. We conclude our study with a scaling and performance analysis, demonstrating our implementation's efficiency and its potential for extreme-scale simulations.
• #### Deep Context-Encoding Network For Retinal Image Captioning

(IEEE, 2021-08-23) [Conference Paper]
Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, we propose a new context-driven encoding network to automatically generate medical reports for retinal images. The proposed model is mainly composed of a multi-modal input encoder and a fused-feature decoder. Our experimental results show that our proposed method is capable of effectively leveraging the interactive information between the input image and context, i.e., keywords in our case. The proposed method creates more accurate and meaningful reports for retinal images than baseline models and achieves state-of-the-art performance. This performance is shown in several commonly used metrics for the medical report generation task: BLEUavg (+16%), CIDEr (+10.2%), and ROUGE (+8.6%).
• #### Contextualized keyword representations for multi-modal retinal image captioning

(ACM, 2021-08-21) [Conference Paper]
Medical image captioning automatically generates a medical description to describe the content of a given medical image. Traditional medical image captioning models create a medical description based on a single medical image input only. Hence, an abstract medical description or concept is hard to be generated based on the traditional approach. Such a method limits the effectiveness of medical image captioning. Multi-modal medical image captioning is one of the approaches utilized to address this problem. In multi-modal medical image captioning, textual input, e.g., expert-defined keywords, is considered as one of the main drivers of medical description generation. Thus, encoding the textual input and the medical image effectively are both important for the task of multi-modal medical image captioning. In this work, a new end-to-end deep multi-modal medical image captioning model is proposed. Contextualized keyword representations, textual feature reinforcement, and masked self-attention are used to develop the proposed approach. Based on the evaluation of an existing multi-modal medical image captioning dataset, experimental results show that the proposed model is effective with an increase of +53.2% in BLEU-avg and +18.6% in CIDEr, compared with the state-of-the-art method. https://github.com/Jhhuangkay/Contextualized-Keyword-Representations-for-Multi-modal-Retinal-Image-Captioning
• #### Multi-window SRS imaging using a rapid widely tunable fiber laser

(arXiv, 2021-08-19) [Preprint]
Spectroscopic stimulated Raman scattering (SRS) imaging has become a useful tool finding a broad range of applications. Yet, wider adoption is hindered by the bulky and environmentally-sensitive solid-state optical parametric oscillator (OPO) in current SRS microscope. Moreover, chemically-informative multi-window SRS imaging across C-H, C-D and fingerprint Raman regions is challenging due to the slow wavelength tuning speed of the solid-state OPO. In this work, we present a multi-window SRS imaging system based on a compact and robust fiber laser with rapid and widely tuning capability. To address the relative intensity noise intrinsic to fiber laser, we implemented auto-balanced detection which enhances the signal-to-noise ratio of stimulated Raman loss imaging by 23 times. We demonstrate high-quality SRS metabolic imaging of fungi, cancer cells, and Caenorhabditis elegans across the C-H, C-D and fingerprint Raman windows. Our re-sults showcase the potential of the compact multi-window SRS system for a broad range of applications.
• #### Antiscaling Evaluation and Quantum Chemical Studies of Nitrogen-Free Organophosphorus Compounds for Oilfield Scale Management

(Industrial & Engineering Chemistry Research, American Chemical Society (ACS), 2021-08-17) [Article]