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Resistance to algae contamination is an important characteristic of insulators used in overhead power distribution in coastal environments. It is therefore important to understand the parameters governing algae adhesion onto polymer insulator materials such as silicone. Flow cell-based shear experiments were conducted in order to characterize the adhesion strength of algae onto polydimethylsiloxane surfaces, comparing fresh polymer substrates with those that have been soaked in water and saline solutions for 1 month. Both freshwater algae and seawater species could withstand considerably less drag force and were therefore more easily removed when the polymer was soaked in salt water. The polymer surface was found to be unaltered in terms of its roughness, contact angle, and lack of water uptake; no macroscopic surface characterization was therefore able to account for the differences in cell adhesion strength resulting from the soaking treatment. Surface-specific nonlinear vibrational spectroscopy, however, revealed subtle differences in the orientation of surface methyl groups that resulted from the water and saline exposure.The self-assembly of colloids at fluid interfaces is a well-studied research field both for gaining fundamental insights and for material fabrication. The fluid interface allows the confinement of particles in two dimensions and may act as a template for guiding their organization into soft and reconfigurable structures. Additives (e.g., surfactants, salts, and polymers) in the colloidal suspension are routinely used as a practical and effective tool to drive particle adsorption and tune their interfacial organization. However, some phenomena lying at the heart of the accumulation and self-assembly of particles at fluid interfaces remain poorly understood. This Feature Article aims to critically analyze the mechanisms involved in the adsorption and self-organization of micro- and nanoparticles at various fluid interfaces. In particular, we address the role of additives in both promoting the adsorption of particles from the bulk suspension to the fluid interface and in mediating the interactions between interfacial particles. We emphasize how different types of additives play a crucial role in controlling the interactions between suspended particles and the fluid interface as well as the interactions between adsorbed particles, thus dictating the final self-assembled structure. We also critically summarize the main experimental protocols developed for the complete adsorption of particles initially suspended in the bulk. Furthermore, we highlight some special properties (e.g., reconfigurability upon external stimulation and dissipative self-assembly) and the application potential of structures formed by colloid self-organization at fluid interfaces mediated/promoted by additives. We believe our contribution serves both as a practical roadmap to scientists coming from other fields and as a valuable information resource for all researchers interested in this exciting research field.A method for representing and comparing distributions of N-linked glycans located at specific sites on proteins is presented. The representation takes the form of a simple mass spectrum for a given peptide sequence, with each peak corresponding to a different glycopeptide. The mass (in place of m/z) of each peak is that of the glycan mass, and its abundance corresponds to its relative abundance in the electrospray MS1 spectrum. This provides a facile means of representing all identifiable glycopeptides arising from a single protein "sequon" on a specific sequence, thereby enabling the comparison and searching of these distributions as routinely done for mass spectra. Likewise, these reference glycopeptide abundance distribution spectra (GADS) can be stored in searchable libraries. A set of such libraries created from available data is provided along with an adapted version of the widely used NIST-MS library-search software. Since GADS contain only MS1 abundances and identifications, they are equally suitable for expressing collision-induced fragmentation and electron-transfer dissociation determinations of glycopeptide identity. Comparisons of GADS for N-glycosylated sites on several proteins, especially the SARS-CoV-2 spike protein, demonstrate the potential reproducibility of GADS and their utility for comparing site-specific distributions.A solar evaporator is regarded as a prospective approach to solve the problem of water shortage. Here, we report an aerogel-based solar evaporator with self-propulsion and self-healing behavior to achieve efficient desalination and enhanced heavy-metal removal. The aerogel solar evaporator is prepared from a Schiff-base hydrogel with an asymmetric Au deposition layer via a simple freeze-drying method. The hydrogel is composed of chitosan and dialdehyde starch, and the Au layer generates a thermal gradient to drive the self-propulsion of the aerogel solar evaporator. Also, the dynamic linkages involved in the Schiff-base hydrogel endow the aerogel solar evaporator with self-healing ability upon external damage. Meanwhile, the Schiff-base framework is used as the interaction site between the aerogel evaporator and water molecules to lower the water evaporation enthalpy. Moreover, the aerogel evaporators are designed into small elliptical spheres and a porous structure, which offer the aerogel evaporators excellent water evaporation behavior with an evaporation rate of 3.12 kg m-2 h-1 in natural seawater under 1-sun irradiation. The self-propulsion ability and self-healing property of such solar evaporators provide the advantages of enhanced purification efficiency, good durability, stability (maintain over 88.2% at the 10th day), and high salt resistance (maintain 80% at 200 g kg-1). More notably, heavy-metal ions in water have been removed effectively to a drinkable level after evaporation. These results prove that the self-propelled aerogel solar evaporator holds great promise for practical applications for on-site water desalination and purification.A widespread hypothesis ascribes the ability of migratory birds to navigate over large distances to an inclination compass realized by the protein cryptochrome in the birds' retinae. Cryptochromes are activated by blue light, which induces a radical pair state, the spin dynamics of which may become sensitive to earth's weak magnetic fields. The magnetic information is encoded and passed on to downstream processes by structural rearrangements of the protein, the details of which remain vague. We utilize extensive all-atom molecular dynamics simulations to probe the conformational changes of pigeon cryptochrome 4 upon light activation. The structural dynamics are analyzed based on principal component analysis and with the help of distance matrices, which reveal significant changes in selected inter-residue distances. The results are evaluated and discussed with reference to the protein structure and its putative function as a magnetoreceptor. It is suggested that the phosphate-binding loop could act as a gate controlling the access to the flavin adenine dinucleotide cofactor depending on the redox state of the protein.Identification of catalysts is a difficult matter as catalytic activities involve a vast number of complex features that each catalyst possesses. Here, catalysis gene expression profiling is proposed from unique features discovered in catalyst data collected by high-throughput experiments as an alternative way of representing the catalysts. Combining constructed catalyst gene sequences with hierarchical clustering results in catalyst gene expression profiling where natural language processing is used to identify similar catalysts based on edit distance. In addition, catalysts with similar properties are designed by modifying catalyst genes where the designed catalysts are experimentally confirmed to have catalytic activities that are associated with their catalyst gene sequences. Thus, the proposed method of catalyst gene expressions allows for a novel way of describing catalysts that allows for similarities in catalysts and catalytic activity to be easily recognized while enabling the ability to design new catalysts based on manipulating chemical elements of catalysts with similar catalyst gene sequences.The heterogeneity of histone H3 proteoforms makes histone H3 top-down analysis challenging. To enhance the detection coverage of the proteoforms, performing liquid chromatography (LC) front-end to mass spectrometry (MS) detection is recommended. Here, using optimized electron-transfer/high-energy collision dissociation (EThcD) parameters, we have conducted a proteoform-spectrum match (PrSM)-level side-by-side comparison of reversed-phase LC-MS (RPLC-MS), "dual-gradient" weak cation-exchange/hydrophilic interaction LC-MS (dual-gradient WCX/HILIC-MS), and "organic-rich" WCX/HILIC-MS on the top-down analyses of H3.1, H3.2, and H4 proteins extracted from a HeLa cell culture. While both dual-gradient WCX/HILIC and organic-rich WCX/HILIC could resolve intact H3 and H4 proteoforms by the number of acetylations, the organic-rich method could enhance the separations of different trimethyl/acetyl near-isobaric H3 proteoforms. In comparison with RPLC-MS, both of the WCX/HILIC-MS methods enhanced the qualities of the H3 PrSMs and remarkably improved the range, reproducibility, and confidence in the identifications of H3 proteoforms.Copper-related materials are used for separation of ethylene and acetylene gases in chemistry; however, the precise mechanism regarding selectivity is elusive to be fully understood. Here, we have conducted a joint experimental and theoretical study of the Cun- (n = 7-30) clusters in reacting with C2H4 and C2H2. It is found that all of the Cun- clusters readily react with C2H2, giving rise to C2H2-addition products; however, Cu18- and Cu19- do not react with C2H4. We illustrate the superatomic stability of Cu18- and advocate its availability to separate C2H4 from C2H2. Further, we demonstrate the atomically precise mechanism regarding selectivity by fully unveiling the size-dependent cluster-π interactions.The construction of (hetero)biaryls, which are ubiquitous scaffolds among medical substances, functional materials, and agrochemicals, constitutes a key application of cross-coupling methods. However, these usually require multiple synthetic steps. Herein, we report a simple photoinduced and catalyst-free C-H/C-H (hetero)arylation cross-coupling through aryl thianthrenium salts, which are formed site-selectively by direct C-H functionalization. The key to this approach is the UV-light, which can disrupt the C-S bond to form thianthrene radical cations and aryl radicals.Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model.