Polysaccharide-based intelligent responsive colloidal carriers for food bioactives: Structures, stimuli-triggered release mechanism and applications.

As an important branch of structure chemistry, isomer structural chemistry has developed tremendously. However, the isomerization of periodic framework materials especially isomeric covalent organic frameworks (Iso-COFs) remains a great challenge due to their intrinsic structural complexity and lack of a systematic regulation strategy. In this work, we provided a triple synergic regulation strategy including geometric, positional, and linkage isomerism. Under the guidance of this multiple-regulation strategy, we successfully obtained recorded Iso-COFs with 15 kinds with the same structural framework and identical chemical formula (C48H36N6O3). With functional design, 6 model Iso-COFs synthesized through three isomerism strategies were chosen to reveal the influence of isomeric structural differences on their photochemical properties in H2O2 photosynthesis. Therefore, compared with other control samples, TDA-BTD COF, which possessed excellent optical properties and the lowest oxygen adsorption energy, exhibited satisfactory photochemical H2O2 production efficiency (2016.26 μmol g-1h-1) and an apparent quantum yield (AQY) of 3.67% at 420 nm. Density functional theory (DFT) calculations and in situ characterization results collectively demonstrated that positional, linkage, and geometric isomerisms regulate the substrate adsorption behavior, charge separation, and electronic structure of the catalyst. For the first time, this work put forward the multidimensional regulation strategy for designing periodic framework-based isomers, which realized the recorded number of Iso-COFs with identical chemical formula to date, also providing a pioneering structural illustration for in-depth exploration of the structure-performance relationship of Iso-COF from multiple dimensions.

One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products. For molecules with up to 36 non-hydrogen atoms, the number of possible structures has been estimated to range from 1020-1060. The task of determining the structure (formula and connectivity) of a molecule of this size using only its one-dimensional 1H and/or 13C NMR spectrum, i.e., de novo structure generation, thus appears completely intractable. Here, we show how it is possible to achieve this task for systems with up to 40 non-hydrogen atoms across the full elemental coverage typically encountered in organic chemistry (C, N, O, H, P, S, Si, B, and the halogens) using a deep learning framework, thus covering a vast portion of the drug-like chemical space. Leveraging insights from natural language processing, we show that our transformer-based architecture predicts the correct molecule with 60.4% accuracy within the first 15 predictions using only the 1H and 13C NMR spectra, thus overcoming the combinatorial growth of the chemical space while also being extensible to experimental data via fine-tuning.

Single-molecule nanorotors based on boron clusters often suffer from a trade-off between dynamic fluxionality and chemical stability. Herein, we extend our design strategy beyond the previously reported "ballet rotor" to its heavier group 7 congeners, technetium and rhenium. Through a comprehensive computational exploration, we identify the global minimum (GM) structures of MB10O10- (M = Tc, Re) as pyramidal clusters with the structural formula [(OB)3-M©B7O7]-, featuring a (BO)3 cap on one side of a B7O7 ring, resembling a three-bladed molecular propeller. Molecular dynamics simulations confirm the ultrafast, nearly barrierless rotation of the (BO)3 unit around the molecular axis. Remarkably, these fluxional clusters exhibit exceptional stability, with large HOMO-LUMO gaps (6.09-6.10 eV) and high vertical detachment energies (VDEs) (5.36-5.37 eV). Chemical bonding and nucleus-independent chemical shift (NICS) analyses reveal that the stability originates from a σ + π + δ triple aromaticity within the MB10 core, while the delocalized bonding also acts as a "lubricant" enabling free rotation. These triply aromatic molecular propellers represent a new, robust class of nanoscale rotors with promising potential for molecular machinery.

Structural analysis of a soluble polysaccharide from the root of Atractylodis Macrocephala Rhizoma and its mechanism in alleviating ulcerative colitis in mice.

A conventional polyurethane (PU) sponge shows less selectivity, which restricts its ability to favorably absorb oils. To address this limitation, the sponge surface was engineered using a simple, low-cost, and environmentally friendly emulsion polymerization approach, which produced a uniform coating and significantly improved the oil-absorption capacity. The structural formula, surface morphology and wettability of the modified sponges were characterized using Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and contact angle measurement techniques, respectively. The key parameters influencing the modification, including the amount of monomer, selection of the water/ethanol ratio and the concentration of the cross-linker divinylbenzene, were systematically optimized. After surface treatment, the water contact angle of the sponge increased significantly from about 85° to 154°, representing strong hydrophobicity. The modified sponges exhibited excellent selectivity, efficiently removing oils and organic liquids from oil/water and organic liquid/water systems, respectively, at saturation. They delivered high absorption capacities, ranging from 21 to 75 times their weight, while suppressing water uptake by approximately 94%. Furthermore, the materials achieved separation efficiencies exceeding 98.4% for trace oil removal and retained substantial absorption capacity even after 15 reuse cycles. These results demonstrate that a simple modification strategy can yield a low-cost, high-performance, and environmentally benign sponge, making it a highly promising candidate for oil spill remediation.

Clinical outcomes of central nervous system (CNS) infections are determined by not only pathogen coverage but also whether an antibiotic can cross the blood-brain barrier (BBB) to achieve sufficient exposure at the target site. The BBB is a prototypical example of "organ-specific vascular specialization" established by (1) the non-fenestrated nature and tight junctions of cerebral capillary endothelial cells and (2) endothelial cell regulation by pericytes and astrocytes. Therefore, CNS penetration of antibiotics should be understood as an interaction between physicochemical properties of drugs (lipophilicity, molecular weight, ionization state, and polarity) and the BBB state throughout the disease course (from the acute inflammatory phase to post-resolution). To assist readers in estimating passive BBB permeability simply by inspecting structural formulas, we provide a practical workflow-from identifying key functional groups (red-circle annotation) to a three-tier ("likely to pass," "may pass during inflammation," and "unlikely to pass under usual conditions") classification-and demonstrate workflow efficacy using four representative compounds. Reasons why meningeal gadolinium enhancement on magnetic resonance imaging cannot be taken as direct "proof" of antibiotic penetration are summarized, and treatment strategies from acute phase through recovery are discussed.

Combustion and dark fermentation are two important types of redox reactions. The former uses molecular oxygen as the oxidizing agent and the latter uses proton as the oxidizing agent. Dark fermentation is represented by Buswell’s equation for biohydrogen, which is composed of two sub-reactions: bioredox and biohydrolysis. Unlike heat of combustion, heat of dark fermentation is a concept that is rarely explored. In this research, with the help of Hess’s law, reactions of organic combustion and molecular hydrogen combustion are converted to Buswell’s equation for biohydrogen. The corresponding thermochemical equation for dark fermentation can then be identified accordingly. Based on a given chemical formula in the form of either empirical formula or structural formula, the relationship between standard heat of dark fermentation, standard heat of bioredox, and standard heat of biohydrolysis is established. Using the derived mathematical equations, standard heat of dark fermentation, standard heat of bioredox, and standard heat of biohydrolysis are determined.

Organic combustion and anaerobic digestion are two important types of redox reactions. The former uses molecular oxygen as oxidizing agent and organic carbon as reducing agent. The latter uses organic carbons as both oxidizing agent and reducing agent. Anaerobic digestion is represented by Buswell’s equation. It is composed of series of bioredox and biohydrolysis reactions. To count heat of anaerobic digestion, structural formula and heat of formation of organic matter are required. To overcome these restrictions, this research establishes a simple equation for determining heat of anaerobic digestion. Hess’s Law is used to convert organic combustion equations to Buswell’s equation. Then, the corresponding thermochemical equation for standard heat of anaerobic digestion is deduced. This research concludes that: (i) ideal standard heat of anaerobic digestion is equal to zero, (ii) heat of bioredox is dependent on Buswell’s electron, (iii) standard heat of biohydrolysis is equal to the negative value of standard heat of bioredox, and (iv) standard Gibbs free energy change is equal to the negative product of temperature times standard entropy change.

In common data environment (CDE) systems, design information is retrieved using structured query formulas, which require knowledge of data structures and query syntax. This increases user burden and limits effective utilization of accumulated design data. Recent advances in large language models (LLMs) have shown potential for generating structured search formulas from natural language input; however, their applicability in real industrial design environments remains insufficiently examined. This study investigates the feasibility and limitations of natural language–based design query generation using LLMs under a closed design data environment with restricted external network access. A system was implemented to convert natural language queries into XML-based design search formulas using a locally deployed LLM, and experiments were conducted to analyze the structural characteristics of the generated queries. The experimental results show that the LLM can generate the basic structure of XML design queries with reasonable consistency, and that expanding the training data and iterative fine-tuning improve structural stability. However, limitations were identified in handling complex query conditions, suggesting the need for additional validation and refinement mechanisms. This study offers practical insights into the realistic use of LLMs for design data retrieval in CDE environments.

Through a Vilsmeier–Haack condensation 2-chloro-3-formyl-1.8-nathyridine (I) was synthesized. When treated with aqueous hydrochloric acid converted to compound (II) was successfully obtained in high yield and then converted to Novel 1-(8-methyl) furo[2,3-b] -(1,8-naphthyridine-2-yl) ethenone (III) through Claisen –Schmidt condensation. The condensation between compound III (ketone) and benzaldehyde yield Novel (furo[2,3-b] (1,8-naphthyridine-2-yl)-2- acetyl called chalcone (4). The reaction between chalcone and bromine water yields dibromide (6), Iodo chalcone (5) produced by treatment of chalcone with one to two pieces of crystal iodine in dimethyl sulfoxide. Spectral analysis techniques such as 1H-NMR, FT-IR were employed to identify and confirm the structural formula of all products. The synthesized compounds (3-6) were evaluation for their anti- bacterial activity against both gram negative and gram-positive bacteria, demonstrating significant compared to standard drug. All these compounds showed moderate activity.

Surface-enhanced Raman scattering (SERS) is a powerful spectroscopic tool for molecular analysis, in which molecular-surface interactions, adsorption energetics, and electronic coupling govern activation, selectivity, and sensitivity. Despite this, SERS interpretation and design have been dominated by electromagnetic field enhancement, leaving chemical enhancement and interfacial understanding limited and fragmented, with surface chemistry often treated as a black box in which ligands and coatings are applied empirically with little mechanistic insight. In this perspective, we reemphasize SERS as an interfacial chemical phenomenon, viewing SERS substrates as chemically active interfaces with tunable energetic, geometric, and electronic states. We introduce a SERS interfacial chemical phase diagram that organizes substrate-analyte interactions across these dimensions, enabling mechanistic understanding, rational control, and predictive design. Furthermore, we evaluate emerging strategies for actively tuning interfacial states through chemical functionalization, porous and compartmentalized materials, adaptive architectures, and hybrid systems, and highlight opportunities to integrate computation, standardized interfacial metadata, and interpretable machine learning to advance SERS into a chemically designable and predictive platform for targeted analysis and operando studies.

This paper outlines the long-term project ‘Wortfamilien diachron’ (= Word Families in Diachrony; WoDia), funded by Deutsche Forschungsgemeinschaft (DFG) and executed at the Universities of Frankfurt/M., Hamburg, Kiel, and Trier. Its primary goal is the development of an online database-driven research environment for the historical word formation of German, and it is intended for implementation in both research and teaching. The dataset comprises lemmas from the Old High German, Middle High German, Old Saxon and Middle Low German dictionaries. WoDia integrates these lemmas into an overarching word family structure by putting each word in its position within its word family. The individual word formation elements and the word formation hierarchy are mapped in a structural formula. The four vocabularies are interconnected and linked to the online dictionary resources and to the reference corpora of historical German. This approach used in the research environment WoDia enables the user to analyse processes of change within the historical vocabularies of German, particularly transformations in word family structures and the usage of word formation elements. The methodical approach employed in constructing the database is presented in this paper, and concrete possibilities of application are illustrated by means of three exemplary case studies.

The moderate cytotoxic of isoeleutherol reported, a naphthalene-derived compound, from Eleutherine bulbosa bulbs collected in Simalungun, North Sumatra. The isolated compound, identified as (3S)-4-hydroxy-5-methoxy-3-methyl-3H-benzo[f][2] benzofuran-1-one, was characterised through FTIR, NMR and EIMS analyses, confirming its structural integrity and molecular formula C14H12O4. Methanolic extraction of the bulb yielded the highest isoeleutherol concentration (17.35 ± 1.72 ppm), indicating that the bulb serves as the primary storage tissue. The cytotoxic evaluation revealed moderate antiproliferative activity against HeLa, A549 and MCF-7 cancer cell lines, with IC50 values of 27.63 ± 2.52, 32.68 ± 1.60 and 50.13 ± 1.49 μM, respectively, showing the highest sensitivity in HeLa cells. The observed activity is attributed to the naphthoquinone-type framework. These findings provide scientific validation of E. bulbosa’s ethnomedicinal use and establish isoeleutherol as a potential lead compound for traditional herb medicine development.

Percolation diagrams derived from first-principles investigation of chemical short-range order in binary alloys

Piper longum and Piper sarmentosum are plants of the Piperaceae family, rich in secondary metabolites, with various medicinal and food values. They are highly similar in morphology, but differ in their medicinal parts and pharmacological effects. To investigate the differences in the medicinal effects between P. longum and P.sarmentosum, it is of great practical significance to study and compare the metabolites of the two species. In the present work, non-targeted Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics was used to identify and measure metabolites in roots, stems, leaves, flowers, and three developmental stages of fruit from P. longum and P. sarmentosum. 1,073 metabolites were identified, including 729 metabolites in positive ion mode and 344 metabolites in negative ion mode. We identified differential accumulated metabolites (DAMs) in different tissues between the two species, and found that the DAMs were enriched in phenylalanine metabolism, including the biogenesis pathway of alkaloid and flavonoid. Based on the structural formula of identified substances, we proposed the biosynthesis pathway of phenylpropanoid, alkaloid and flavonoid and profiled the accumulation of each component in different tissues from the two Piper species. This study compares the differences in metabolites between the two species of the Piper genus. It describes the diverse accumulation of medicinal components, providing an informative scientific basis for the efficient utilization and targeted development of two important medicinal and food crops of the Piper genus.

The acquisition of fundamental knowledge in chemical formula language and the ability to graphically represent chemical structures are critical components of chemistry education. Various structural representational forms, such as molecular formulas, Lewis formulas, and skeletal formulas, serve different purposes in describing chemical compounds and require specialized skills to interpret and create. While experts in the field can seamlessly transition between these representational forms, students often face challenges in building the necessary mental models and understanding the design conventions. This study investigates how chemistry teachers perceive and address students' difficulties in reading and drawing chemical structural formulas. Through a survey of 116 chemistry teachers from Switzerland, Germany, Austria, and Denmark, the study explores the introduction of structural representational forms in classrooms, common student errors, and strategies employed to mitigate these errors. Teachers frequently use simpler chemical compounds, such as alkanes and alcohols, to introduce various representational forms and to diagnose typical errors. The most common errors identified include missing or excess atoms, incorrect spatial orientation, and violation of the octet rule. Findings emphasize the importance of gradually introducing complexity in chemical notations, starting with basic structures and progressively advancing to more complex spatial representational forms. Teachers highlight the value of repetition, targeted exercises, and interactive tools, such as augmented reality, to enhance students' spatial reasoning and conceptual understanding. These insights underscore the need for innovative educational resources to support individualized learning paths and improve proficiency in chemical representation.

In both preparing and using hydrogen-rich reducing gas (H2RG) in direct reduction, carbon deposition occurs if operating parameters are improperly controlled, affecting the entire process. Therefore, a universally applicable method is needed to determine carbon deposition in the CH4-H2-CO-H2O-CO2 system, especially the broader H-C-O system. This study establishes a novel method based on the H-C-O system's mass balance and chemical equilibrium diagram, alongside multi-phase/multi-reaction equilibrium principles. Critical carbon deposition point coordinates (O/C, H/C) were determined under varying conditions including temperatures typically ranging from 550 °C to 900 °C, total pressures from 0.1 to 2.0 MPa, and H2/CO ratios of approximately 2.0-6.9. Connecting points under identical parameters generated critical carbon deposition curves, forming a comprehensive "carbon deposition state diagram for H-C-O system". This diagram allows precise determination of system state and carbon deposition occurrence, providing a theoretical basis for optimizing process parameters to avoid deposition. To overcome complex diagram calculations, specialized analysis software was developed. Validation using experimental and industrial data confirmed the diagram's rationality and practicality. The diagram offers a simple, rapid, and accurate means to predict carbon deposition under specified conditions. Crucially, it guides efforts to prevent deposition while simultaneously minimizing energy consumption and costs in natural gas-based hydrogen production processes. Consequently, the "carbon deposition state diagram for H-C-O system" effectively guides actual production towards cost reduction, lower consumption, stability, and smooth operation.

Porous anodized aluminium oxide (PAA) has wide and important applications in photonic crystals, energy science, nanotemplates, life science, medicine, aerospace and other scientific research and industrial manufacturing fields. The decisive factors determining its application value and specific performance are its own structural parameters. Therefore, the accurate calculation (but not destructive measurement) of each PAA structural parameter is of great significance for the design and application of PAA structures to satisfy different practical requirements. However, there is a significant problem because multiple distinct formulas proposed by different researchers are used for calculating single independent PAA structural parameters such as the pore diameter. Furthermore, these multiple distinct formulas for determining a single PAA structural parameter frequently yield different results. Compounding this issue, these single structural parameters serve as independent variables in formulas for calculating other PAA parameters. This propagation of uncertainty leads to multiple distinct results for other subsequent parameters. Consequently, in practice, for the calculation of a PAA structural parameter, it is difficult to discern which calculations are the most accurate. Regarding the aforementioned issues, this paper systematically reviews the key structural parameters of PAA and the most commonly used distinct calculation formulas of each key structural parameter. The independent variables of almost all mentioned calculation formulas are unified to the anodization voltage. Subsequently, extensive experimental data published by other researchers are substituted into all the formulas with the unified independent variable to perform an objective competitive screening for the optimal calculation formula of each PAA structural parameter. Finally, on the basis of the competitive screening and independent variable unification, an equation set of PAA structural parameter calculations is proposed for the accurate and convenient calculation of all key PAA structural parameters. The proposal of an equation set for the PAA structural parameter calculation provides a systematic, comprehensive theoretical model and mathematical tool for the design and calculation of PAA structures according to practical requirements in scientific research and engineering applications.

Fuel consumption in heavy-duty off-road machinery depends on a wide range of interacting factors related to the operating environment, the technical characteristics and condition of the machine, and the behaviour, experience and state of the operator. Existing studies typically address only fragments of this relationship, focusing on vehicle parameters, selected environmental factors or individual aspects of driving style. The method proposed in this work provides a general and transferable framework for assessing fuel consumption in any type of machine or vehicle. The Integrated Fuel Consumption Assessment Model (IFCAM) combines environmental, vehicle and human domains into a coherent structured formula that can be used across different operational contexts. The model was developed using continuous short-term measurements and long-term operational data collected during real industrial work. Its universal structure makes it applicable not only to mining equipment, but also to construction machinery and transport vehicles, as well as conventional passenger cars, where it offers a systematic procedure for estimating fuel demand under variable operating conditions. The results demonstrate that integrating multi-domain data improves predictive accuracy and opens new possibilities for analysing operator influence and overall energy efficiency.