Ricerosendal0769

Controllable underwater gas bubble (UGB) transport on a surface is realized by geography-/stimuli-induced wettability gradient force (Fwet-grad). Unfortunately, the high-speed maneuvering of UGBs along free routes on planar surfaces remains challenging. Herein, a regime of magnetism-actuated robot (MAR) mounting on biomimetic laser-ablated lubricant-impregnated slippery surfaces (LA-LISS) is reported. Leveraging on LA-LISS, MAR-entrained UGBs can move along arbitrary directions through the loading of a tracing magnetic trigger. The underlying hydrodynamics is that MAR-entrained UGBs would be actuated slipping upon a giant magnetic-induced towing force (FM//). Once the magnetism stimuli is discharged, FM// vanishes immediately to immobilize the UGBs on LA-LISS. Thanks to the MAR's robust bubble affinity, a typical UGB (20 μL) on the optimized LA-LISS can be accelerated at 500 mm/s2 and gain an ultrafast velocity of over 205 mm/s that far exceeds previously reported figures. Moreover, fundamental physics renders MAR antibuoyancy, steering locomotive UGBs on the inclined LA-LISS. Significantly, an MAR propelling UGBs to configure desirable patterns, realize on-demand coalescence, remedy the cutoff switch, as well as facilitate a programmable light-control-light optical shutter is successfully deployed. Compared with previous smart surfaces, the current multifunctional regime is more competent for harnessing UGBs featuring an unparalleled transport velocity independent of the feeble Fwet-grad.Metallic Zn as a promising anode material of aqueous batteries suffers from severe parasitic reactions and notorious dendrite growth. To address these issues, the desolvation and nucleation processes need to be carefully regulated. Herein, Zn foils coated by ZnF2-Ag nanoparticles (ZnF2-Ag@Zn) are used as a model to modulate the desolvation and nucleation processes by hybrid surfaces, where Ag has a strong affinity to Zn adatoms and ZnF2 shows an intense adsorption to H2O. This selective adsorption of different species on ZnF2 and Ag reduces the mutual interference between two species. Therefore, ZnF2-Ag@Zn exhibits the electrochemical performance much better than ZnF2@Zn or Ag@Zn. Even at -40 °C, the full cells using ZnF2-Ag@Zn demonstrate an ultralong lifespan of 5000 cycles with a capacity retention of almost 100%. This work provides new insights to improve the performance of Zn metal batteries, especially at low temperatures.We studied self-assembly and colloidal properties of poly(ethylene glycol) (pEG) conjugated sucrose soyate polyols (PSSP). These molecular platforms were synthesized by covalently connecting PEGs of different molecular weights (Mn) (12 and 16 ethylene oxide units) to epoxidized sucrose soyate (ESS). The synthesized PSSP products showed amphiphilicity, reduced water surface tension, and exhibited critical Aggregation Concentration (CAC) within the range of 0.3-0.4 mg/mL. We observed that PSSP self-assembles in water in the form of nanoparticles without the need of any cosolvents. These nanoparticles exhibited number-average hydrodynamic diameter of 120 ± 8 nm with a polydispersity index (PDI) of less then 0.3, and negatively charged surfaces. We also found out that PSSP nanoparticles can encapsulate and homogeneously distribute a hydrophobic model compound, such as a phthalocyanine dye, Solvent Blue-70 (BL-70), on a metal surface. Collectively, our studies explored and demonstrated the possibility of molecular diversification of biobased starting materials to form amphiphilic nanoparticles with industrially relevant colloidal and surface properties.Developing efficient strategies for synthesizing novel diazocine compounds is valuable because their use has been limited by their synthetic accessibility. This work describes the catalytic (4+3) cycloaddition reaction of carbonyl ylides with azoalkenes generated in situ. The rhodium-catalyzed cascade reaction features good atom and step economy, providing the first access to oxo-bridged diazocines. The product could be synthesized on a gram scale and converted into diversely substituted dihydroisobenzofurans.Worldwide use of hydrofluorocarbons (HFCs) is currently being regulated and phased out because of high global warming potentials (GWPs). Separation techniques for recycling refrigerants are needed so that HFCs can be dealt with responsibly. Many HFCs currently in use are azeotropic or near-azeotropic refrigerant blends and must be separated so that the components can be recycled and repurposed effectively. One such refrigerant is R-410A, which is a near-azeotropic 50/50 wt % mixture of pentafluoroethane (HFC-125) and difluoromethane (HFC-32). This study examined the use of the LTA zeolites for separating HFC-32 from HFC-125. Pure gas isotherms were measured using a XEMIS gravimetric microbalance with zeolites 3A, 4A, and 5A. Reversible sorption was observed for HFC-32 with zeolites 4A and 5A, whereas irreversible sorption was observed for HFC-125 with zeolite 5A. Negligible sorption was observed for HFC-125 with zeolites 3A and 4A, and although sorption of HFC-32 with zeolite 3A was observed, the process was slow, making the sorbent not commercially viable. The enthalpy of adsorption was predicted using the vapor adsorption equilibrium (VAE) analogue of the Clausius-Clapeyron equation and measured using a calorimeter for HFC-125 and HFC-32 with zeolite 5A and for HFC-32 with zeolite 4A. Molecular-level interactions between the LTA zeolites and HFCs were discussed and used to interpret pure gas isotherms and enthalpy of adsorption results. Overall, zeolites 4A and 5A were found to be good candidates for kinetically and thermodynamically separating R-410A, respectively.Ultrahigh-resolution NMR has recently attracted considerable attention in the field of complex samples analysis. Indeed, the implementation of broadband homonuclear decoupling techniques has allowed us to greatly simplify crowded 1H spectra, yielding singlets for almost every proton site from the analyzed molecules. Pure shift methods have notably shown to be particularly suitable for deciphering mixtures of metabolites in biological samples. Here, we have successfully implemented a new pure shift pulse sequence based on the PSYCHE method, which incorporates a block for solvent suppression that is suitable for metabolomics analysis. The resulting experiment allows us to record ultrahigh-resolution 1D NOESY 1H spectra of biofluids with suppression of the water signal, which is a crucial step for highlighting metabolite mixtures in an aqueous phase. We have successfully recorded pure shift spectra on extracellular media of diffuse large B-cell lymphoma (DLBCL) cells. Despite a lower sensitivity, the resolution of pure shift data was found to be better than that of the standard approach, which provides a more detailed vision of the exo-metabolome. The statistical analyses carried out on the resulting metabolic profiles allow us to successfully highlight several metabolic pathways affected by these drugs. Notably, we show that Kidrolase plays a major role in the metabolic pathways of this DLBCL cell line.For the first time, the phase transition and criticality of methane confined in nanoporous media are measured. The measurement is performed by establishing an experimental setup utilizing a differential scanning calorimeter capable of operating under very low temperatures as well as high pressures to detect the capillary phase transition of methane inside nanopores. By performing experiments along isochoric cooling paths, both the capillary condensation and the bulk condensation of methane are detected. The pore critical point of nanoconfined methane is also determined and then used to derive the parameters of a previously developed self-consistent equation of state based on the generalized van der Waals partition function. Using these parameters, the equation of state can predict the capillary-condensation curves that agree well with the experimental data.Machine learning is increasingly applied in proteomics and metabolomics to predict molecular structure, function, and physicochemical properties, including behavior in chromatography, ion mobility, and tandem mass spectrometry. These must be described in sufficient detail to apply or evaluate the performance of trained models. Here we look at and interpret the recently published and general DOME (Data, Optimization, Model, Evaluation) recommendations for conducting and reporting on machine learning in the specific context of proteomics and metabolomics.Over the last few decades, enhanced sampling methods have been continuously improved. Here, we exploit this progress and propose a modular workflow for blind reaction discovery and determination of reaction paths. In a three-step strategy, at first we use a collective variable derived from spectral graph theory in conjunction with the explore variant of the on-the-fly probability enhanced sampling method to drive reaction discovery runs. Once different chemical products are determined, we construct an ad-hoc neural network-based collective variable to improve sampling, and finally we refine the results using the free energy perturbation theory and a more accurate Hamiltonian. We apply this strategy to both intramolecular and intermolecular reactions. Our workflow requires minimal user input and extends the power of ab initio molecular dynamics to explore and characterize the reaction space.The tight control of transcriptional coactivators is a fundamental aspect of gene expression in cells. The regulation of the CREB-binding protein (CBP) and p300 coactivators, two paralog multidomain proteins, involves an autoinhibitory loop (AIL) of the histone acetyltransferase (HAT) domain. There is experimental evidence for the AIL engaging with the HAT binding site, thus interrupting the acetylation of histone tails or other proteins. Both CBP and p300 contain a domain of about 110 residues (called the bromodomain) that recognizes histone tails with one or more acetylated lysine side chains. Here, we investigate by molecular dynamics simulations whether the AIL of CBP (residues 1556-1618) acetylated at the side chain of Lys1595 can bind to the bromodomain. The structural instability and fast unbinding kinetics of the AIL from the bromodomain pocket suggest that the AIL is not a ligand of the bromodomain on the same protein chain. This is further supported by the absence of strong and persistent contacts at the binding interface. Furthermore, the simulations of unbinding show an initial fast detachment of the acetylated lysine and a slower phase necessary for complete AIL dissociation. We provide further evidence for the instability of the AIL intramolecular binding by comparison with a natural ligand, the histone peptide H3K56ac, which shows higher stability in the pocket.Fast and accurate assessment of small-molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains challenging as the current molecular mechanics (MM) methods are limited by insufficient coverage of drug-like chemical space and accurate quantum mechanical (QM) methods are too expensive. DL-AP5 To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small-molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate compound library and leveraged massively parallel cloud computing resources for density functional theory (DFT) torsion scans of these fragments, generating a training data set of 1.2 million DFT energies. After training TorsionNet on this data set, we obtain a model that can rapidly predict the torsion energy profile of typical drug-like fragments with DFT-level accuracy.