Aaenmeldgaard5667

A green synthetic strategy to design biomass-derived porous carbon electrode materials with precisely tailored structure and morphology has always been a challenging goal because these materials can fulfill the demands of next-generation supercapacitors and other electrochemical devices. Potassium hydroxide (KOH) is extensively utilized as an activator since it can produce porous carbon with high specific surface area and well-developed porous channels. click here The exploitation of sodium hydroxide (NaOH) as an activating agent is less referenced in the literature, although it offers some advantages over KOH in terms of low cost, less corrosiveness, and simple handling procedure, all of which are appealing particularly from an industrial viewpoint. The motivation for this present study is to fabricate porous carbon spheres in a sustainable manner via a spray drying approach followed by a carbonization process, using Kraft lignin as the carbon precursor and NaOH as an alternative activation agent instead of the high-cost and high-corrosive KOH for the first time. The structure of carbon particles can be accurately transitioned from a compact to hollow structure, and the surface textural properties can be easily tuned by altering the NaOH concentration. The obtained porous carbon spheres were applied as highly packed thin film electrode materials for supercapacitor devices. The specific capacitance value of porous carbon spheres with a highly compact structure (high packing density) is 66.5 F g-1, which is higher than that of commercial activated carbon and other biomass-derived carbon. This work provides a green processing for producing low-cost and environment-friendly porous carbon spheres from abundant Kraft lignin and important insight for selecting NaOH as an activator to tailor the morphology and structure, which represents an economical and sustainable approach for energy storage devices.Particle size disparities suppress crystallization. However, soft deformable nanogels can change the size of the larger particles in suspension and crystallize even at a high initial size-polydispersity. Using neutron scattering with contrast variation, the response of individual nanogels in crowded environments was probed, and an increase of the parameter describing size-polydispersity was found, which is often interpreted as deformation. Here, computer simulations are used to generate deformed nanogels and the corresponding form factor. The data are fitted with the spherical model used to analyze scattering data. The fits show the same qualitative increase of the parameter related to the size-polydispersity with increasing particle deformation. Starting from the simulated deformed spheres, we also reproduce experimental scattering data. A further analysis of the particle shows that the size disparities between nanogels do not increase significantly. In contrast, their shapes strongly vary from one nanogel to the other.Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.3,4-Dihydroxyphenylalanine (Dopa) is a versatile molecule that enables marine mussels to achieve successful underwater adhesion. However, due to its complicated redox chemistry and vulnerability to oxidation, controlling surface adhesion and cohesion has been a challenging issue to overcome. Foot protein type 6 (fp-6), a thiol-rich interfacial mussel adhesive protein, has been reported as a proteinaceous antioxidant for mussels that helps Dopa maintain surface adhesion ability. In this study, we focused on the role of fp-6 in oxidized Dopa. The effect on the tautomer equilibrium of oxidized Dopa was investigated using recombinant fp-6 (rfp-6) and Dopa-incorporated foot protein type 3 fast variant (drfp-3F), which were produced in bacterial cells. The redox chemistry of Dopa in drfp-3F and the role of rfp-6 were observed using a UV-vis spectrophotometer and a surface forces apparatus (SFA). We discovered that rfp-6 shifts the tautomer equilibrium to ΔDopa as a preferred tautomer for oxidized Dopa in drfp-3F and makes drfp-3F better on underwater surface adhesion.Intrusion (wetting)/extrusion (drying) of liquids in/from lyophobic nanoporous systems is key in many fields, including chromatography, nanofluidics, biology, and energy materials. Here we demonstrate that secondary topological features decorating main channels of porous systems dramatically affect the intrusion/extrusion cycle. These secondary features, allowing an unexpected bridging with liquid in the surrounding domains, stabilize the water stream intruding a micropore. This reduces the intrusion/extrusion barrier and the corresponding pressures without altering other properties of the system. Tuning the intrusion/extrusion pressures via subnanometric topological features represents a yet unexplored strategy for designing hydrophobic micropores. Though energy is not the only field of application, here we show that the proposed tuning approach may bring 20-75 MPa of intrusion/extrusion pressure increase, expanding the applicability of hydrophobic microporous materials.Tuning the surface structure of the photoelectrode provides one of the most effective ways to address the critical challenges in artificial photosynthesis, such as efficiency, stability, and product selectivity, for which gallium nitride (GaN) nanowires have shown great promise. In the GaN wurtzite crystal structure, polar, semipolar, and nonpolar planes coexist and exhibit very different structural, electronic, and chemical properties. Here, through a comprehensive study of the photoelectrochemical performance of GaN photocathodes in the form of films and nanowires with controlled surface polarities we show that significant photoelectrochemical activity can be observed when the nonpolar surfaces are exposed in the electrolyte, whereas little or no activity is measured from the GaN polar c-plane surfaces. The atomic origin of this fundamental difference is further revealed through density functional theory calculations. This study provides guideline on crystal facet engineering of metal-nitride photo(electro)catalysts for a broad range of artificial photosynthesis chemical reactions.Efficient catalytic systems based on arene-Ru(II) complexes bearing bis-imidazole methane-based ligands were developed to achieve additive-free hydrogen generation from formaldehyde and paraformaldehyde in water. Our findings inferred the influential role of bis-imidazole methane ligands in the observed catalytic performance of the studied catalysts. Among the screened complexes, [(η6-p-cymene)RuCl(L)]+Cl- ([Ru]-2) (L = 4,4'-((2-methoxyphenyl)methylene)bis(2-ethyl-5-methyl-1H-imidazole) outperformed others to generate hydrogen gas from paraformaldehyde in water with an exceptionally high turnover number (TON) of >20,000. A detailed mechanistic pathway for hydrogen gas generation from formaldehyde has been proposed on the basis of identified several crucial catalytic intermediate species involved in the hydrogen production process.The pore apertures dictate the guest accessibilities of the pores, imparting diverse functions to porous materials. It is highly desired to construct crystalline porous polymers with predesignable and uniform mesopores that can allow large organic, inorganic, and biological molecules to enter. However, due to the ease of the formation of interpenetrated and/or fragile structures, the largest pore aperture reported in the metal-organic frameworks is 8.5 nm, and the value for covalent organic frameworks (COFs) is only 5.8 nm. Herein, we construct a series of COFs with record pore aperture values from 7.7 to 10.0 nm by designing building blocks with large conformational rigidness, planarity, and suitable local polarity. All of the obtained COFs possess eclipsed stacking structures, high crystallinity, permanent porosity, and high stability. As a proof of concept, we successfully employed these COFs to separate pepsin that is ∼7 nm in size from its crudes and to protect tyrosinase from heat-induced deactivation.Lakes receive and actively process terrestrial dissolved organic matter (DOM) and play an important role in the global carbon cycle. Urbanization results in elevated inputs of nonpoint-source DOM to headwater streams. Retention of water in lakes allows time for alteration and transformation of the chemical composition of DOM by microbes and UV radiation. Yet, it remains unclear how anthropogenic and natural drivers impact the composition and biolability of DOM in non-pristine lakes. We used optical spectroscopy, Fourier transform ion cyclotron mass spectrometry, stable isotopic measurements, and laboratory bioincubations to investigate the chemical composition and biolability of DOM across two large data sets of lakes associated with a large gradient of urbanization in lowland Eastern China, encompassing a total of 99 lakes. We found that increased urban land use, gross domestic products, and population density in the catchment were associated with an elevated trophic level index, higher chlorophyll-a, higher bacterial abundance, and a higher amount of organic carbon with proportionally higher contribution of aliphatic and peptide-like DOM fractions, which can be highly biolabile. Catchment areas, water depth, lake area catchment area, gross primary productivity, δ18O-H2O, and bacterial abundance, however, had comparatively little linkage with DOM composition and biolability. Urban land use is currently intensifying in many developing countries, and our results anticipate an increase in the level of biolabile aliphatic DOM from nonpoint sources and accelerated carbon cycling in lake ecosystems in such regions.Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers of literature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paper demonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks─and a carefully selected database of >10,000 literature examples─we show that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (various fingerprints, chemical descriptors, latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on the sheer frequency of certain reaction conditions reported in the literature.