Rapid Mining of Fast Ion Conductors via Subgraph Isomorphism Matching.

The rapidly evolving field of inorganic solid-state electrolytes (ISSEs) has been driven in recent years by advances in data-mining techniques, which facilitates the high-throughput computational screening for candidate materials in the databases. The key to the mining process is the selection of critical features that underline the similarity of a material to an existing ISSE. Unfortunately, this selection is generally subjective and frequently under debate. Here we propose a subgraph isomorphism matching method that allows an objective evaluation of the similarity between two compounds according to the topology of the local atomic environment. The matching algorithm has been applied to discover four structure types that are highly analogous to the LiTi2(PO4)3 NASICON prototype. We demonstrate that the local atomic environments similar to LiTi2(PO4)3 endow these four structures with favorable Li diffusion tunnels and ionic conductivity on par with those of the prototype. By further taking into account the electronic structure and electrochemical stability window, 13 compounds are identified to be potential ISSEs. Our findings not only offer a promising approach toward rapid mining of fast ion conductors without limitation in the compositional range but also reveal insights into the design of ISSEs according to the topology of their framework structures.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

Nanoscale Ultrafine Zinc Metal Anodes for High Stability Aqueous Zinc Ion Batteries.

Aqueous Zn batteries (AZBs) are a promising energy storage technology, due to their high theoretical capacity, low redox potential, and safety. However, dendrite growth and parasitic reactions occurring at the surface of metallic Zn result in severe instability. Here we report a new method to achieve ultrafine Zn nanograin anodes by using ethylene glycol monomethyl ether (EGME) molecules to manipulate zinc nucleation and growth processes. It is demonstrated that EGME complexes with Zn2+ to moderately increase the driving force for nucleation, as well as adsorbs on the Zn surface to prevent H-corrosion and dendritic protuberances by refining the grains. As a result, the nanoscale anode delivers high Coulombic efficiency (ca. 99.5%), long-term cycle life (over 366 days and 8800 cycles), and outstanding compatibility with state-of-the-art cathodes (ZnVO and AC) in full cells. This work offers a new route for interfacial engineering in aqueous metal-ion batteries, with significant implications for the commercial future of AZBs.

Correlation between supine flexibility and postoperative correction in adolescent idiopathic scoliosis.

BACKGROUND: The preoperative flexibility of the scoliotic spine is a key aspect of surgical planning, as it provides information on the rigidity of the curve, the extent of structural changes, the levels to be fused and the amount of correction. The purpose of this study was to assess whether supine flexibility can be used to predict postoperative correction in patients with adolescent idiopathic scoliosis (AIS) by determining the correlation between these two characteristics. METHODS: A total of 41 AIS patients who underwent surgical treatment between 2018 and 2020 were retrospectively enrolled for analysis. Preoperative and postoperative standing radiographs and preoperative CT images of the entire spine were collected and used to measure supine flexibility and the postoperative correction rate. T tests were used to analyse the differences in supine flexibility and postoperative correction rate between groups. Pearson's product-moment correlation analysis was performed, and regression models were established to determine the correlation between supine flexibility and postoperative correction. Thoracic curves and lumbar curves were analysed independently. RESULTS: Supine flexibility was found to be significantly lower than the correction rate but showed a strong correlation with the postoperative correction rate, with r values of 0.68 for the thoracic curve group and 0.76 for the lumbar curve group. The relationship between supine flexibility and postoperative correction rate could be expressed by linear regression models. CONCLUSION: Supine flexibility can be used to predict postoperative correction in AIS patients. In clinical practice, supine radiographs may be used in place of existing flexibility test techniques.

Microscopic and Transcriptomic Comparison of Powdery Mildew Resistance in the Progenies of Brassica carinata × B. napus.

Powdery mildew is a widespread disease in rapeseed due to a lack of resistant germplasm. We compared the foliar epidermal features and transcriptomic responses between the resistant (R) and susceptible (S) plants among the two parents and progenies of Brassica carinata × B. napus. The amount of cuticular wax and callose deposition on the R plants was much lower than that on the S plants; hence, these chemicals are not all essential to pre-penetration resistance, although the cuticular wax on the R plants had more needle-like crystals. A total of 1049 genes involved in various defense responses were expressed differentially among the R/S plants. The expression levels of two well-known susceptibility genes, MLO6 and MLO12, were much lower in the R plant, indicating an important role in PM resistance. A set of genes related to wax biosynthesis (KCS6, LACS2, CER and MAH1), cell wall modification (PMR5, PMEI9, RWA2, PDCB1 and C/VIF2), chloroplast function (Chlorophyllase-1, OEP161, PSBO1, CP29B and CSP41b), receptor kinase activity (ERECTA, BAK1, BAM2, LYM1, LYM3, RLK902, RLP11, ERL1 and ERL2), IPCS2, GF14 lambda, RPS4 and RPS6 were highly expressed in the R plants. In the S plants, most highly expressed genes were involved in later defense responses, including CERK1, LYK4, LIK1, NIMIN-1, CHITINASE 10, PECTINESTERASE, CYP81F2 and RBOHF and the genes involved in salicylic acid-dependent systemic acquired resistance and hypersensitive responses, indicating the occurrence of severe fungal infection. The results indicate that some uncertain pre-penetration defenses are pivotal for high resistance, while post-penetration defenses are more important for the S plant survival.

Synergistic Dissociation-and-Trapping Effect to Promote Li-Ion Conduction in Polymer Electrolytes via Oxygen Vacancies.

Despite their promised safety and mechanical flexibility, solvent-free polymer electrolytes suffer from low Li-ion conductivities due to poor dissociation of conducting salts and low Li+ -transference numbers due to Li+ -trapping by ether-linkages. In this work, the authors found that oxygen vacancies carried by nanosized Al2 O3 fillers preferentially promotes Li+ -conduction in poly(ethylene oxide) (PEO). These vacancies and free electrons therein, whose concentration can be tuned, effectively break up the ion pairs by weakening the Coulombic attraction within them, while simultaneously interacting with the anions, thus preferentially constraining the movement of anions. This synergistic dissociation-and-trapping effect leads to the significant and selective improvement in Li-ion conductivity. Solid state batteries built on such PEO-based electrolytes exhibits superior performance at high current density. This discovery reveals a molecular-level rationale for the long-observed phenomenon that certain inorganic nano-fillers improve ion conduction in PEO, and provides a universal approach to tailor superior polymer-based electrolytes for the next generation solid-state batteries.

A Consortium Blockchain-Based Agricultural Machinery Scheduling System.

The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud.

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users' requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.

Long-term effects of placing one or two cages in instrumented posterior lumbar interbody fusion.

PURPOSE: Posterior lumbar interbody fusion (PLIF) is an established surgical procedure for spine stabilization after the removal of an intervertebral disc. Researches have shown that inserting a single oblique cage has a similar immediate effect to coupled cages, and it has been proposed that single-cage PLIF is a useful alternative to traditional two-cage PLIF. However, it is not clear whether placing one or two cages represents the best choice for long-term fusion. The aim of this study is to examine how cage placement affects bone remodeling after PLIF surgery, and how this consequently impacts the long-term fusion process. METHODS: A finite element model of a L3-L4 lumbar spine with PLIF was developed. The spinal segment was modeled with a partial laminectomy and a discectomy with partial facetectomy, and implanted with posterior pedicle screws. Two models were analyzed, one with coupled parallel cages and one with a single oblique cage. Adaptive bone remodeling was simulated according to Huiskes' criterion. RESULTS: The results showed that in the initial state prior to any bone remodeling, cage stress, cage subsidence and cage dislodgement in the single cage model were all greater than in the coupled cage model. In the final state after significant bone remodeling had taken place, these parameters had decreased in both models and the differences between the two models were reduced. Also, the single cage model demonstrated superior bone development in the bone graft when placed under a constant 400 N axial compressive load. CONCLUSION: Based on the long-term results, instrumented PLIF with a single cage could also be encouraged in clinical practice.

Influence of Li Content on the Topological Inhibition of Oxygen Loss in Li-Rich Cathode Materials.

Lithium-rich layer oxide cathodes are promising energy storage materials due to their high energy densities. However, the oxygen loss during cycling limits their practical applications. Here, the essential role of Li content on the topological inhibition of oxygen loss in lithium-rich cathode materials and the relationship between the migration network of oxygen ions and the transition metal (TM) component are revealed. Utilizing first-principles calculations in combination with percolation theory and Monte Carlo simulations, it is found that TM ions can effectively encage the oxidized oxygen species when the TM concentration in TM layer exceeds 5/6, which hinders the formation of a percolating oxygen migration network. This study demonstrates the significance of rational compositional design in lithium-rich cathodes for effectively suppressing irreversible oxygen release and enhancing cathode cycling performance.

Typhoon-induced stormwater drives nutrient dynamics and triggers phytoplankton blooms in Laizhou Bay, China.

In this study, we investigated the hydrological and ecological impacts of heavy rainfall caused by the storm Rumbia and Typhoon Lekima on Laizhou Bay (LZB) through land‒sea synchronous field surveys, online remote sensors, and simulated enclosure experiments. Within two weeks of Rumbia, approximately 9% and 16% of the annual riverine total nitrogen (TN) and total phosphorus (TP) fluxes, respectively, were transported to the LZB and the proportions were 17% and 35%, respectively, for Lekima. The land use on the watersheds increased the rates of land-derived nutrient loading and altered their biogeochemical forms. Consequently, the average concentrations of dissolved inorganic nitrogen (DIN) and phosphorus (DIP) in the LZB increased by 2.6 and 1.0 times post-Rumbia and by 3.5 and 1.3 times post-Lekima, respectively. Relatively lower salinity and temperature, sudden increases in DIN, and strengthened coastal currents stimulated the growth of highly adaptable and small diatoms, resulting in the first diatom blooms. Subsequently, a bloom of Noctiluca scintillans formed.

Boosting Efficiency in Piezo-Photocatalysis Process Using Poled Ba0.7Sr0.3TiO3 Nanorod Arrays for Pollutant Degradation and Hydrogen Production.

Recently, the combination of the piezoelectric effect in the photocatalytic process, referred to as piezo-photocatalysis, has gained considerable attention as a promising approach for enhancing the degradation of organic pollutants. In this investigation, we studied the piezo-photocatalysis by fabricating arrays of barium strontium titanate (Ba0.7Sr0.3TiO3) nanorods (BST NRs) on a glass substrate as recoverable catalysts. We found that the degradation rate constant k of the rhodamine B solution achieved 0.0447 min-1 using poled BST NRs in the piezo-photocatalytic process, indicating a 2-fold increase in efficiency compared to the photocatalytic process (0.00183 min-1) utilizing the same material. This is mainly ascribed to the generation of the piezopotential in the poled BST NRs under ultrasonic vibration. Moreover, the BST NR array demonstrated a hydrogen (H2) production rate of 411.5 µmol g-1 h-1. In the photoelectrochemical process, the photocurrent density of poled BST NRs achieved 1.97 mA cm-2 at an applied potential of 1.23 V (ERHE (reversible hydrogen electrode)) under ultrasonic vibrations, representing a 1.7-fold increase compared with the poled BST NRs without ultrasonic vibrations. The measurement results from the liquid chromatograph mass spectrometer (LC-MS) demonstrated the formulation of a degradation pathway for rhodamine B molecules. Moreover, ab initio molecular dynamics (AIMD) simulation results demonstrate the dominance of hydroxyl radicals (•OH) rather than superoxide radicals (•O2-) in the degradation process. This study not only benefits the understanding of the principle of the piezo-photocatalytic process but also provides a new perspective for improving the catalytic efficiency for organic pollutants degradation.

Data-Driven Discovery of Gas-Selective Organic Linkers in Metal-Organic Frameworks for the Separation of Ethylene and Ethane.

Metal-organic frameworks (MOFs) are potential candidates for gas-selective adsorbents for the separation of an ethylene/ethane mixture. To accelerate material discovery, high-throughput computational screening is a viable solution. However, classical force fields, which were widely employed in recent studies of MOF adsorbents, have been criticized for their failure to cover complicated interactions such as those involving π electrons. Herein, we demonstrate that machine learning force fields (MLFFs) trained on quantum-chemical reference data can overcome this difficulty. We have constructed a MLFF to accurately predict the adsorption energies of ethylene and ethane on the organic linkers of MOFs and discovered that the π electrons from both the ethylene molecule and the aromatic rings in the linkers could substantially influence the selectivity for gas adsorption. Four kinds of MOF linkers are identified as having promise for the separation of ethylene and ethane, and our results could also offer a new perspective on the design of MOF building blocks for diverse applications.

Seasonal dynamics of dissolved organic matter bioavailability coupling with water mass circulation in the South Yellow Sea.

As a typical shelf-marginal sea, the South Yellow Sea (SYS) is significantly influenced by various factors such as land-based inputs and water mass movements, leading the complex biogeochemical processes of dissolved organic matter (DOM) to become highly dynamic. However, the bioavailability of dissolved organic matter (DOM) coupled with water mass circulation has not been accurately assessed, despite being crucial for understanding the source-sink pattern of organic carbon in marginal sea. In this study, four cruises were conducted in the SYS to analyze the spatial and temporal distribution characteristics of dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and total dissolved amino acids (TDAA). Combined with the bioassay experiments, TDAA carbon normalized yield [TDAA (%DOC)] and TDAA degradation index (DIAA) were used as indicators to explore the bioavailability of DOM across different water masses. Results show that the DOC of the SYS exhibits higher average value in late autumn and early winter, and lower value in spring and summer due to the seasonal alternation of water mass and biological activities. The collective results indicate that DOM bioavailability is higher in the Changjiang River diluted water (CDW) and lower in the Yellow Sea warm current (YSWC) and the Yellow Sea cold water mass (YSCWM). Approximately 20 % of DON can be degraded in the YSCWM during autumn. Notably, although the YSCWM constitutes merely constitutes 10 % of the SYS volume, it stores 18.1 % dissolved inorganic nitrogen (DIN) and 23.9 % PO43- of total nutrients, indicating that the YSCWM is a significant nutrient reservoir within the SYS.

Closo-[B12 H12 ]2- Derivatives with Polar Groups As Promising Building Blocks in Metal-Organic Frameworks for Gas Separation.

Engineering design of metal organic frameworks (MOFs) for gas separation applications is nowadays a thriving field of investigation. Based on the recent experimental studies of dodecaborate-hybrid MOFs as potential materials to separate industry-relevant gas mixtures, we herein present a systematic theoretical study on the derivatives of the closo-dodecaborate anion [B12 H12 ]2- , which can serve as building blocks for MOFs. We discover that amino functionalization can impart a greater ability to selectively capture carbon dioxide from its mixtures with other gases such as nitrogen, ethylene and acetylene. The main advantage lies in the polarization effect induced by amino group, which favors the localization of the negative charges on the boron-cluster anion and offers a nucleophilic anchoring site to accommodate the carbon atom in carbon dioxide. This work suggests an appealing strategy of polar functionalization to optimize the molecule discrimination ability via preferential adsorption.

Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.

Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.

Differences in the expression profiles of lncRNAs and mRNAs in partially injured anterior cruciate ligament and medial collateral ligament of rabbits.

Long noncoding RNAs (lncRNAs), as a novel regulatory factor, are considered to play a vital role in various biological processes and diseases. However, the overall expression profile and biological functions of lncRNAs in the partially injured anterior cruciate ligament (ACL) and medial collateral ligament (MCL) have not been clearly explored. Partially injured models of ACL and MCL were established in 3-month-old healthy male New Zealand white rabbits. Expression of lncRNAs and mRNAs in the ligament tissue was detected by high-throughput sequencing technology, and biological functions of differentially expressed RNAs were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Validation of several differentially expressed RNAs was performed using quantitative real-time PCR (qRT-PCR). Protein-protein interaction (PPI) analysis and competitive endogenous RNA (ceRNA) prediction were used to identify interactions among hub genes and the interaction among lncRNAs, miRNAs, and mRNAs. The results showed that compared with the normal group, there were 267 mRNAs and 329 lncRNAs differentially expressed in ACL and 726 mRNAs and 609 lncRNAs in MCL in the injured group. Compared with MCL, 420 mRNAs and 470 lncRNAs were differentially expressed in ACL in the normal group; 162 mRNAs and 205 lncRNAs were differentially expressed in ACL in the injured group. Several important lncRNAs and genes were identified, namely, COL7A1, LIF, FGFR2, EPHA2, CSF1, MMP2, MMP9, SOX5, LOX, MSTRG.1737.1, MSTRG.26038.25, MSTRG.20209.5, MSTRG.22764.1, and MSTRG.18113.1, which are closely related to inflammatory response, tissue damage repair, cell proliferation, differentiation, migration, and apoptosis. Further study of the functions of these genes may help to better understand the specific molecular mechanisms underlying the occurrence of endogenous repair disorders in ACL, which may provide new ideas for further exploration of effective means to promote endogenous repair of ACL injury.

Clinical Validation of the Differences Between Two-Dimensional Radiography and Three-Dimensional Computed Tomography Image Measurements of the Spine in Adolescent Idiopathic Scoliosis.

OBJECTIVE: The purpose of this study was to investigate differences between measurements of spine on two-dimensional (2D) radiography and three-dimensional (3D) computed tomography (CT) images taken of patients with adolescent idiopathic scoliosis. METHODS: Standard preoperative CT images and posteroanterior (PA) and lateral radiography images were collected prospectively from 43 patients with adolescent idiopathic scoliosis in whom selective spinal fusions were performed. The parameters of interest were the thoracic Cobb angle, lumbar Cobb angle, T4-T12 kyphosis angle, and L1-S1 lordosis (LL) angle. The parameters were measured using 3 separate methods: 3D measurement of CT images (3D measurement), 2D measurement of radiography images (2D measurement), and 2D measurement of radiography images generated by the projection of CT images (2D XP measurement). Significant differences among the results were assessed by comparison T test. RESULTS: The mean difference between the 2D and 2D XP measurements for the thoracic Cobb, lumbar Cobb, T4-T12 kyphotic, and L1-S1 lordotic angles was 8.38°, 7.67°, 8.77°, and 10.18°, respectively. The mean difference between the 2D XP and 3D measurements was -2.81°, -2.78°, -1.29°, and -2.36°, respectively. The mean difference between the 2D and 3D measurements was 5.16°, 4.51°, 6.49°, and 7.37°, respectively. The results showed significant differences (P < 0.05) among the spinal parameters measured using the 2D, 2D XP, and 3D measurement methods on both the coronal and sagittal plane. CONCLUSIONS: Significant differences among the 2D, 2D XP, and 3D measurement methods were observed on both the sagittal plane and coronal plane of the scoliotic spines as a result of variations in posture during imaging and differences in measurement methods.

Biomechanics of adjacent segment after three-level lumbar fusion, hybrid single-level semi-rigid fixation with two-level lumbar fusion.

Multi-level spinal fusion has been reported in some cases to lead to adjacent segment disease (ASD) and proximal junctional kyphosis (PJK). The purpose of this study was to demonstrate a polyether-ether-ketone (PEEK) rod fixation system implanted adjacent to a two-level lumbar fusion would have a lower risk of PJK than three-level lumbar fusion, which was investigated by comparing the biomechanical effects on the adjacent level after surgical procedures. Four finite element (FE) models of the lumbar-sacral spine (intact model (INT), L4-S1 fusion model (L4-S1 FUS), L3-S1 fusion model (L3-S1 FUS), and single-level PEEK rod semi-rigid fixation adjacent to L4-S1 fusion model (FUSPRF)) were established. Displacement-controlled finite element (FE) analysis was used during the simulation. Compared with the two-level fusion model (L4-S1 FUS), both three-level implanted models (L3-S1 FUS and FUSPRF) showed an increase intersegmental rotation angle, and maximum von-Mises stress on the disc annulus. The results also showed that the intersegmental rotation, stress on the disc annulus and maximum stress on the rod were lower in the FUSPRF model than the L3-S1 FUS model. Though the maximum screw stress was higher in the FUSPRF model than the L3-S1 FUS model under all moments except for torsion, the maximum screw stress in the two models were far below the yield strength of titanium alloy. As the parameters above have been indicated as risk factors for PJK, it can be concluded that hybrid single-level PEEK rod semi-rigid fixation and two-level lumbar fusion have a lower risk of PJK than three-level lumbar fusion.

Graph-based discovery and analysis of atomic-scale one-dimensional materials.

Recent decades have witnessed an exponential growth in the discovery of low-dimensional materials (LDMs), benefiting from our unprecedented capabilities in characterizing their structure and chemistry with the aid of advanced computational techniques. Recently, the success of two-dimensional compounds has encouraged extensive research into one-dimensional (1D) atomic chains. Here, we present a methodology for topological classification of structural blocks in bulk crystals based on graph theory, leading to the identification of exfoliable 1D atomic chains and their categorization into a variety of chemical families. A subtle interplay is revealed between the prototypical 1D structural motifs and their chemical space. Leveraging the structure graphs, we elucidate the self-passivation mechanism of 1D compounds imparted by lone electron pairs, and reveal the dependence of the electronic band gap on the cationic percolation network formed by connections between structure units. This graph-theory-based formalism could serve as a source of stimuli for the future design of LDMs.