Algicidal bacteria-derived membrane vesicles as shuttles mediating cross-kingdom interactions between bacteria and algae.

Bacterial membrane vesicles (BMVs) are crucial biological vehicles for facilitating interspecies and interkingdom interactions. However, the extent and mechanisms of BMV involvement in bacterial-algal communication remain elusive. This study provides evidence of BMVs delivering cargos to targeted microalgae. Membrane vesicles (MVs) from Chitinimonas prasina LY03 demonstrated an algicidal profile similar to strain LY03. Further investigation revealed Tambjamine LY2, an effective algicidal compound, selectively packaged into LY03-MVs. Microscopic imaging demonstrated efficient delivery of Tambjamine LY2 to microalgae Heterosigma akashiwo and Thalassiosira pseudonana through membrane fusion. In addition, the study demonstrated the versatile cargo delivery capabilities of BMVs to algae, including the transfer of MV-carried nucleic acids into algal cells and the revival of growth in iron-depleted microalgae by MVs. Collectively, our findings reveal a previously unknown mechanism by which algicidal bacteria store hydrophobic algicidal compounds in MVs to trigger target microalgae death and highlight BMV potency in understanding and engineering bacterial-algae cross-talk.

Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions.

Ionic liquids (ILs), known for their distinct and tunable properties, offer a broad spectrum of potential applications across various fields, including chemistry, materials science, and energy storage. However, practical applications of ILs are often limited by their unfavorable physicochemical properties. Experimental screening becomes impractical due to the vast number of potential IL combinations. Therefore, the development of a robust and efficient model for predicting the IL properties is imperative. As the defining feature, it is of practice significance to establish an accurate yet efficient model to predict the normal melting point of IL (T m), which may facilitate the discovery and design of novel ILs for specific applications. In this study, we presented a pseudo-Siamese convolution neural network (pSCNN) inspired by SCNN and focused on the T m. Utilizing a data set of 3098 ILs, we systematically assess various deep learning models (ANN, pSCNN, and Transformer-CNF), along with molecular descriptors (ECFP fingerprint and Mordred properties), for their performance in predicting the T m of ILs. Remarkably, among the investigated modeling schemes, the pSCNN, coupled with filtered Mordred descriptors, demonstrates superior performance, yielding mean absolute error (MAE) and root-mean-square error (RMSE) values of 24.36 and 31.56 °C, respectively. Feature analysis further highlights the effectiveness of the pSCNN model. Moreover, the pSCNN method, with a pair of inputs, can be extended beyond ionic liquid melting point prediction.

Accurate Structural Elucidation of Samoquasine A and an Unknown Homologue Using a Computation-Based Machine Learning Protocol.

The structure of samoquasine A has long been a subject of controversy, which was resolved only upon its successful total synthesis. We examined the structures of the associated compounds using the state-of-the-art SVM-M protocol. The method accurately discriminated all putative structures historically attributed to samoquasine A from a pool of 48 isomeric structures, confirming that samoquasine A is indeed identical to perlolidine. Furthermore, by applying the SVM-M protocol to an additional pool of 67 isomeric structures, we successfully assigned a yet unknown natural product, initially misidentified as perlolidine, as a novel oxime, (E)-3H-cyclopenta[c]quinolin-3-one oxime, representing the first reported cyclone oxime-quinoline natural product.

Sodium Carbazolide and Derivatives as Solid-State Electrolytes for Sodium-Ion Batteries.

Replacing widely used organic liquid electrolytes with solid-state electrolytes (SSEs) could effectively solve the safety issues in sodium-ion batteries. Efforts on seeking novel solid-state electrolytes have been continued for decades. However, issues about SSEs still exist, such as low ionic conductivity at ambient temperature, difficulty in manufacturing, low electrochemical stability, poor compatibility with electrodes, etc. Here, sodium carbazolide (Na-CZ) and its THF-coordinated derivatives are rationally fabricated as Na+ conductors, and two of their crystal structures are successfully solved. Among these materials, THF-coordinated complexes exhibit fast Na+ conductivities, i.e., 1.20×10-4  S cm-1 and 1.95×10-3  S cm-1 at 90 °C for Na-CZ-1THF and Na-CZ-2THF, respectively, which are among the top Na+ conductors under the same condition. Furthermore, stable Na plating/stripping is observed even over 400 h cycling, showing outstanding interfacial stability and compatibility against Na electrode. More advantages such as ease of synthesis, low-cost, and cold pressing for molding can be obtained. In situ NMR results revealed that the evaporation of THF may play an essential role in the Na+ migration, where the movement of THF creates defects/vacancies and facilitates the migration of Na+ .

Deforming lanthanum trihydride for superionic conduction.

With strong reducibility and high redox potential, the hydride ion (H-) is a reactive hydrogen species and an energy carrier. Materials that conduct pure H- at ambient conditions will be enablers of advanced clean energy storage and electrochemical conversion technologies1,2. However, rare earth trihydrides, known for fast H migration, also exhibit detrimental electronic conductivity3-5. Here we show that by creating nanosized grains and defects in the lattice, the electronic conductivity of LaHx can be suppressed by more than five orders of magnitude. This transforms LaHx to a superionic conductor at -40 °C with a record high H- conductivity of 1.0 × 10-2 S cm-1 and a low diffusion barrier of 0.12 eV. A room-temperature all-solid-state hydride cell is demonstrated.

Fabrication of Lithium Indolide and Derivates for Ion Conduction.

The development of ionic conductors as solid-state electrolytes to replace the widely used liquid electrolytes could effectively solve the safety issues as well as enhance the energy density of batteries. Yet no ionic conductors to date could meet all the criteria of solid-state electrolytes for practical applications. Therefore, exploration of new materials is highly demanded. Herein, a new type of metalorganic-based materials, namely, lithium indolide and its tetrahydrofuran (THF)-coordinated derivatives, are developed and employed as fast ionic conductors. Their crystal structures are also determined. Particularly, the lithium indolide ditetrahydrofuran shows ionic conductivities of 6.28 × 10-6 and 8.27 × 10-4 S cm-1 at 110 and 150 °C, respectively. A "neutral ligand-assisted" cation migration mechanism is proposed, where the migration of Li+ may be facilitated by the dynamic equilibrium of the neutral ligand and the large sized anions. The present idea of using metalorganic compounds coordinated with neutral ligands for fast ionic conductors provides vast opportunities for discovering new solid-state electrolytes in the future thanks to the rich chemistry of organic anions and ligands.

Transition Metal-Free Hydrogenolysis of Anilines to Arenes Mediated by Lithium Hydride.

Hydrodenitrogenation (HDN) of nitrogen-containing organic compounds such as aniline and its derivatives is of scientific interest and practical importance. Major efforts have been devoted to the development and understanding of transition metal-mediated chemical processes. Herein, we report a fundamentally different strategy using a transition metal-free material, that is, lithium hydride (LiH) enabling the hydrogenolysis of aniline to benzene and ammonia via a chemical looping approach. Aniline reacts with LiH to form lithium anilide, and subsequently, the hydrogenolysis of lithium anilide yields benzene and ammonia and regenerates LiH to complete the loop. This LiH-mediated chemical looping HDN process stands in sharp contrast to the transition metal-catalyzed or -mediated processes, which commonly lead to the complete hydrogenation of aromatic rings. A highly denitrogenated product formation rate of 2623 µmol·g-1·h-1 is achieved for the hydrogenolysis of lithium anilide at 300 °C and 10 bar H2, which exceeds the catalytic rate of transition metal catalysts. Computational studies reveal that the scission of C-N bonds is facilitated by a Li-mediated nucleophilic attack of hydride to the α-sp2C atom of aniline. This work not only provides a distinctive chemical looping route for HDN, but also opens up materials space for the denitrogenation of anilines.

Insights into the Mechanism of Metal-Catalyzed Transformation of Oxime Esters: Metal-Bound Radical Pathway vs Free Radical Pathway.

Controlling of radical reactivity by binding a radical to the metal center is an elegant strategy to overcome the challenge that radical intermediates are "too reactive to be selective". Yet, its application has seemingly been limited to a few strained-ring substrates, azide compounds, and diazo compounds. Meanwhile, first-row transition-metal-catalyzed (mainly, Fe, Ni, Cu) transformations of oxime esters have been reported recently in which the activation processes are assumed to follow free-radical mechanisms. In this work, we show by means of density functional theory calculations that the activation of oxime esters catalyzed by Fe(II) and Cu(I) catalysts more likely affords a metal-bound iminyl radical, rather than the presumed free iminyl radical, and the whole process follows a metal-bound radical mechanism. The as-formed metal-bound radical intermediates are an Fe(III)-iminyl radical (Stotal = 2, SFe = 5/2, and Siminyl = -1/2) and a Cu(II)-iminyl radical (Stotal = 0, SCu = 1/2, and Siminyl = -1/2). The discovery of such novel substrates affording metal-bound radical intermediates may facilitate the experimental design of metal-catalyzed asymmetric synthesis using oxime esters to achieve the desired enantioselectivity.

Enabling Semihydrogenation of Alkynes to Alkenes by Using a Calcium Palladium Complex Hydride.

Selective hydrogenation of alkynes to alkenes requires a catalytic site with suitable electronic properties for modulating the adsorption and conversion of alkyne, alkene as well as dihydrogen. Here, we report a complex palladium hydride, CaPdH2, featured by electron-rich [PdH2]δ- sites that are surrounded by Ca cations that interacts with C2H2 and C2H4 via σ-bonding to Pd and unusual cation-π interaction with Ca, resulting in a much weaker chemisorption than those of Pd metal catalysts. Concomitantly, the dissociation of H2 and hydrogenation of C2Hx (x = 2-4) species experience significant energy barriers over CaPdH2, which is fundamentally different from those reported Pd-based catalysts. Such a unique catalytic environment enables CaPdH2, the very first complex transition-metal hydride catalyst, to afford a high alkene selectivity for the semihydrogenation of alkynes.

Auto-classification of biomass through characterization of their pyrolysis behaviors using thermogravimetric analysis with support vector machine algorithm: case study for tobacco.

BACKGROUND: During the biomass-to-bio-oil conversion process, many studies focus on studying the association between biomass and bio-products using near-infrared spectra (NIR) and chemical analysis methods. However, the characterization of biomass pyrolysis behaviors using thermogravimetric analysis (TGA) with support vector machine (SVM) algorithm has not been reported. In this study, tobacco was chosen as the object for biomass, because the cigarette smoke (including water, tar, and gases) released by tobacco pyrolysis reactions decides the sensory quality, which is similar to biomass as a renewable resource through the pyrolysis process. RESULTS: SVM algorithm has been employed to automatically classify the planting area and growing position of tobacco leaves using thermogravimetric analysis data as the information source for the first time. Eighty-eight single-grade tobacco samples belonging to four grades and eight categories were split into the training, validation, and blind testing sets. Our model showed excellent performances in both the training and validation set as well as in the blind test, with accuracy over 91.67%. Throughout the whole dataset of 88 samples, our model not only provides precise results on the planting area of tobacco leave, but also accurately distinguishes the major grades among the upper, lower, and middle positions. The error only occurs in the classification of subgrades of the middle position. CONCLUSIONS: From the case study of tobacco, our results validated the feasibility of using TGA with SVM algorithm as an objective and fast method for auto-classification of tobacco planting area and growing position. In view of the high similarity between tobacco and other biomasses in the compositions and pyrolysis behaviors, this new protocol, which couples the TGA data with SVM algorithm, can potentially be extrapolated to the auto-classification of other biomass types.