Pyrolysis-catalysis upcycling of waste plastic using a multilayer stainless-steel catalyst toward a circular economy.

Current un-sustainable plastic management is exacerbating plastic pollution, an urgent shift is thus needed to create a recycling society. Such recovering carbon (C) and hydrogen (H) from waste plastic has been considered as one practical route to achieve a circular economy. Here, we performed a simple pyrolysis-catalysis deconstruction of waste plastic via a monolithic multilayer stainless-steel mesh catalyst to produce multiwalled carbon nanotubes (MWCNTs) and H2, which are important carbon material and energy carrier to achieve sustainable development. Results revealed that the C and H recovery efficiencies were as high as 86% and 70%, respectively. The unique oxidation-reduction process and improvement of surface roughness led to efficient exposure of active sites, which increased MWCNTs by suppressing macromolecule hydrocarbons. The C recovery efficiency declined by only 5% after 10 cycles, proving the long-term employment of the catalyst. This catalyst can efficiently convert aromatics to MWCNTs by the vapor-solid-solid mechanism and demonstrate good universality in processing different kinds of waste plastics. The produced MWCNTs showed potential in applications of lithium-ion batteries and telecommunication. Owing to the economic profits and environmental benefits of the developed route, we highlighted its potential as a promising alternative to conventional incineration, simultaneously achieving the waste-to-resource strategy and circular economy.

Regulating Lewis Acidic Sites of 1T-2H MoS2 Catalysts for Solar-Driven Photothermal Catalytic H2 Production from Lignocellulosic Biomass.

Solar-driven photothermal catalytic H2 production from lignocellulosic biomass was achieved by using 1T-2H MoS2 with tunable Lewis acidic sites as catalysts in an alkaline aqueous solution, in which the number of Lewis acidic sites derived from the exposed Mo edges of MoS2 was successfully regulated by both the formation of an edge-terminated 1T-2H phase structure and tunable layer number. Owing to the abundant Lewis acidic sites for the oxygenolysis of lignocellulosic biomass, the 1T-2H MoS2 catalyst shows high photothermal catalytic lignocellulosic biomass-to-H2 transformation performance in polar wood chips, bamboo, rice straw corncobs, and rice hull aqueous solutions, and the highest H2 generation rate and solar-to-H2 (STH) efficiency respectively achieves 3661 µmol·h-1·g-1 and 0.18% in the polar wood chip system under 300 W Xe lamp illumination. This study provides a sustainable and cost-effective method for the direct transformation of renewable lignocellulosic biomass to H2 fuel driven by solar energy.

Machine Learning for Experimental Reactivity of a Set of Metal Clusters toward C-H Activation.

Understanding the mechanisms of C-H activation of alkanes is a very important research topic. The reactions of metal clusters with alkanes have been extensively studied to reveal the electronic features governing C-H activation, while the experimental cluster reactivity was qualitatively interpreted case by case in the literature. Herein, we prepared and mass-selected over 100 rhodium-based clusters (RhxVyOz- and RhxCoyOz-) to react with light alkanes, enabling the determination of reaction rate constants spanning six orders of magnitude. A satisfactory model being able to quantitatively describe the rate data in terms of multiple cluster electronic features (average electron occupancy of valence s orbitals, the minimum natural charge on the metal atom, cluster polarizability, and energy gap involved in the agostic interaction) has been constructed through a machine learning approach. This study demonstrates that the general mechanisms governing the very important process of C-H activation by diverse metal centers can be discovered by interpreting experimental data with artificial intelligence.

Mechanisms of action of berberine hydrochloride in planktonic cells and biofilms of Pseudomonas aeruginosa.

The increasing prevalence of extensively drug- and pan-drug-resistant Pseudomonas aeruginosa is a major concern for global public health. Therefore, it is crucial to develop novel antimicrobials that specifically target P. aeruginosa and its biofilms. In the present study, we determined that berberine hydrochloride inhibited the growth of planktonic bacteria as well as prevented the formation of biofilms. Moreover, we observed downregulation in the expression of pslA and pelA biofilm-related genes. Compared with existing antibiotics, berberine hydrochloride exhibits multiple modes of action against P. aeruginosa. Our findings suggest that berberine hydrochloride exerts its antimicrobial effects by damaging bacterial cell membranes, generating reactive oxygen species (ROS), and reducing intracellular adenosine triphosphate (ATP) levels. Furthermore, berberine hydrochloride showed minimal cytotoxicity and reduced susceptibility to drug resistance. In a mouse model of peritonitis, it significantly inhibited the growth of P. aeruginosa and exhibited a strong bacteriostatic action. In conclusion, berberine hydrochloride is a safe and effective antibacterial agent that inhibits the growth of P. aeruginosa.

Spectroscopic Characterization of Thermal Methane Activation by Lewis-Acid-Base Pair in a Gas-Phase Metal Nitride Anion Ta2N3.

Activation and transformation of methane is one of the "holy grails" in catalysis. Understanding the nature of active sites and mechanistic details via spectroscopic characterization of the reactive sites and key intermediates is of great challenge but crucial for the development of novel strategies for methane transformation. Herein, by employing photoelectron velocity-map imaging (PEVMI) spectroscopy in conjunction with quantum chemistry calculations, the Lewis acid-base pair (LABP) of [Taδ+-Nδ-] unit in Ta2N3 - acting as an active center to accomplish the heterolytic cleavage of C-H bond in CH4 has been confirmed by direct characterization of the reactant ion Ta2N3 - and the CH4-adduct intermediate Ta2N3CH4 -. Two active vibrational modes for the reactant (Ta2N3 -) and four active vibrational modes for the intermediate (Ta2N3CH4 -) were observed from the vibrationally resolved PEVMI spectra, which unequivocally determined the structure of Ta2N3 - and Ta2N3CH4 -. Upon heating, the LABP intermediate (Ta2N3CH4 -) containing the NH and Ta-CH3 unit can undergo the processes of C-N coupling and dehydrogenation to form the product with an adsorbed HCN molecule.

Size-dependent reactivity of VnO+ (n = 1-9) clusters with ethane.

Cost-effective and readily accessible 3d transition metals (TMs) have been considered as promising candidates for alkane activation while 3d TMs especially the early TMs are usually not very reactive with light alkanes. In this study, the reactivity of Vn+ and VnO+ (n = 1-9) cluster cations towards ethane under thermal collision conditions has been investigated using mass spectrometry and density functional theory calculations. Among Vn+ (n = 1-9) clusters, only V3-5+ can react with C2H6 to generate dehydrogenation products and the reaction rate constants are below 10-13 cm3 molecule-1 s-1. In contrast, the reaction rate constants for all VnO+ (n = 1-9) with C2H6 significantly increase by about 2-4 orders of magnitude. Theoretical analysis evidences that the addition of ligand O affects the charge distribution of the metal centers, resulting in a significant increase in the cluster reactivity. The analysis of frontier orbitals indicates that the agostic interaction determines the size-dependent reactivity of VnO+ cluster cations. This study provides a novel approach for improving the reactivity of early 3d TMs.

Experimental Reactivity of (MoO3)NO- (N = 1-21) Cluster Anions with C1-C4 Alkanes: A Simple Model to Predict the Reactivity with Methane.

Metal oxide clusters with atomic oxygen radical anions are important model systems to study the mechanisms of activating and transforming very stable alkane molecules under ambient conditions. It is extremely challenging to characterize the activation and conversion of methane, the most stable alkane molecule, by metal oxide cluster anions due to the low reactivity of the anionic species. In this study, using a ship-lock type reactor that could be run at relatively high pressure conditions to provide a high number of collisions in ion-molecule reactions, the rate constants of the reactions between (MoO3)NO- (N = 1-21) cluster anions and the light alkanes (C1-C4) were measured under thermal collision conditions. The relationships among the reaction rates of different alkanes were obtained to establish a model to predict the low rate constants with methane from the high rate constants with C2-C4 alkanes. The model was tested by using available experimental results in literature. This study provides a new method to estimate the relatively low reactivity of atomic oxygen radical anions with methane on metal oxide clusters.

Difference of carrier dynamics in a semiconductor saturable absorber mirror with and without B+ ion-implantation.

Three samples whose growth temperatures were 450°C, 500°C, and 560°C for S E S A M 1, S E S A M 2, and S E S A M 3, respectively, were tested by femto-second time-resolved transient absorption spectroscopy. The results indicate that the carrier dynamics of excited state absorption were dominant, and the lifetimes of carriers trapped by defect levels were about tens of pico-seconds. To further study the influence of carrier dynamics and recovery time of samples by ion-implantation, B + ions of 80 and 130 KeV were implanted into the samples with dose of 1014/c m 2. The modified samples showed a dominance of ultra-fast carrier dynamics of ground-state bleaching and direct recombination, which lasted for hundreds of femto-seconds, over excited state absorption. Additionally, carrier fast trapping was observed to be competitive with the excited state absorption process. After ion-implantation, the carrier dynamics of carrier trapping were enhanced, which contributed to forming an ultra-short laser, while the carrier dynamics of absorption of the excited state were suppressed. The conclusion that defect levels were partially eliminated by B + ion-implantation can be drawn.

Unveiling the Nature and Strength of Selenium-Centered Chalcogen Bonds in Binary Complexes of SeO2 with Oxygen-/Sulfur-Containing Lewis Bases: Insights from Theoretical Calculations.

Among various non-covalent interactions, selenium-centered chalcogen bonds (SeChBs) have garnered considerable attention in recent years as a result of their important contributions to crystal engineering, organocatalysis, molecular recognition, materials science, and biological systems. Herein, we systematically investigated π-hole-type Se∙∙∙O/S ChBs in the binary complexes of SeO2 with a series of O-/S-containing Lewis bases by means of high-level ab initio computations. The results demonstrate that there exists an attractive interaction between the Se atom of SeO2 and the O/S atom of Lewis bases. The interaction energies computed at the MP2/aug-cc-pVTZ level range from -4.68 kcal/mol to -10.83 kcal/mol for the Se∙∙∙O chalcogen-bonded complexes and vary between -3.53 kcal/mol and -13.77 kcal/mol for the Se∙∙∙S chalcogen-bonded complexes. The Se∙∙∙O/S ChBs exhibit a relatively short binding distance in comparison to the sum of the van der Waals radii of two chalcogen atoms. The Se∙∙∙O/S ChBs in all of the studied complexes show significant strength and a closed-shell nature, with a partially covalent character in most cases. Furthermore, the strength of these Se∙∙∙O/S ChBs generally surpasses that of the C/O-H∙∙∙O hydrogen bonds within the same complex. It should be noted that additional C/O-H∙∙∙O interactions have a large effect on the geometric structures and strength of Se∙∙∙O/S ChBs. Two subunits are connected together mainly via the orbital interaction between the lone pair of O/S atoms in the Lewis bases and the BD*(OSe) anti-bonding orbital of SeO2, except for the SeO2∙∙∙HCSOH complex. The electrostatic component emerges as the largest attractive contributor for stabilizing the examined complexes, with significant contributions from induction and dispersion components as well.

Distribution characteristics of soil carbon density and influencing factors in Qinghai-Tibet Plateau region.

The Qinghai-Tibet Plateau has low anthropogenic carbon emissions and large carbon stock in its ecosystems. As a crucial region in terrestrial ecosystems responding to climate change, an accurate understanding of the distribution characteristics of soil carbon density holds significance in estimating the soil carbon storage capacity in forests and grasslands. It performs a crucial role in achieving carbon neutrality goals in China. The distribution characteristics of carbon and carbon density in the surface, middle, and deep soil layers are calculated, and the main influencing factors of soil carbon density changes are analyzed. The carbon density in the surface soil ranges from a minimum of 1.62 kg/m2 to a maximum of 52.93 kg/m2. The coefficient of variation for carbon is 46%, indicating a considerable variability in carbon distribution across different regions. There are substantial disparities, with geological background, land use types, and soil types significantly influencing soil organic carbon density. Alpine meadow soil has the highest carbon density compared with other soil types. The distribution of soil organic carbon density at three different depths is as follows: grassland > bare land > forestland > water area. The grassland systems in the Qinghai-Tibet Plateau have considerable soil carbon sink and storage potential; however, they are confronted with the risk of grassland degradation. The grassland ecosystems on the Qinghai-Tibet Plateau harbor substantial soil carbon sinks and storage potential. However, they are at risk of grassland degradation. It is imperative to enhance grassland management, implement sustainable grazing practices, and prevent the deterioration of the grassland carbon reservoirs to mitigate the exacerbation of greenhouse gas emissions and global warming. This highlights the urgency of implementing more studies to uncover the potential of existing grassland ecological engineering projects for carbon sequestration.

C-H Activation by Iron-Vanadium Bimetallic Oxide Cluster Anions FeV3 O10 - and FeV5 O15 - : A Comparison with Scandium-Vanadium Oxide Clusters.

Late transition metal-bonded atomic oxygen radicals (LTM-O⋅- ) have been frequently proposed as important active sites to selectively activate and transform inert alkane molecules. However, it is extremely challenging to characterize the LTM-O⋅- -mediated elementary reactions for clarifying the underlying mechanisms limited by the low activity of LTM-O⋅- radicals that is inaccessible by the traditional experimental methods. Herein, benefiting from our newly-designed ship-lock type reactor, the reactivity of iron-vanadium bimetallic oxide cluster anions FeV3 O10 - and FeV5 O15 - featuring with Fe-O⋅- radicals to abstract a hydrogen atom from C2 -C4 alkanes has been experimentally characterized at 298 K, and the rate constants are determined in the orders of magnitude of 10-14 to 10-16  cm3 molecule-1 s-1 , which are four orders of magnitude slower than the values of counterpart ScV3 O10 - and ScV5 O15 - clusters bearing Sc-O⋅- radicals. Theoretical results reveal that the rearrangements of the electronic and geometric structures during the reaction process function to modulate the activity of Fe-O⋅- . This study not only quantitatively characterizes the elementary reactions of LTM-O⋅- radicals with alkanes, but also provides new insights into structure-activity relationship of M-O⋅- radicals.

Catalytic NO Reduction by NO Pre-Adsorbed RhCeO2 NO- Clusters.

A fundamental understanding on the dynamically structural evolution of catalysts induced by reactant gases under working conditions is challenging but pivotal in catalyst design. Herein, in combination with state-of-the-art mass spectrometry for cluster reactions, cryogenic photoelectron imaging spectroscopy, and quantum-chemical calculations, we identified that NO adsorption on rhodium-cerium bimetallic oxide cluster RhCeO2 - can create a Ce3+ ion in product RhCeO2 NO- that serves as the starting point to trigger the catalysis of NO reduction by CO. Theoretical calculations substantiated that the reduction of another two NO molecules into N2 O takes place exclusively on the Ce3+ ion while Rh behaves like a promoter to buffer electrons and cooperates with Ce3+ to drive NO reduction. Our finding demonstrates the importance of NO in regulating the catalytic behavior of Rh under reaction conditions and provides much-needed insights into the essence of NO reduction over Rh/CeO2 , one of the most efficient components in three-way catalysts for NOx removal.

MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation.

BACKGROUND: Imminent new vertebral fracture (NVF) is highly prevalent after vertebral augmentation (VA). An accurate assessment of the imminent risk of NVF could help to develop prompt treatment strategies. PURPOSE: To develop and validate predictive models that integrated the radiomic features and clinical risk factors based on machine learning algorithms to evaluate the imminent risk of NVF. MATERIALS AND METHODS: In this retrospective study, a total of 168 patients with painful osteoporotic vertebral compression fractures treated with VA were evaluated. Radiomic features of L1 vertebrae based on lumbar T2-weighted images were obtained. Univariate and LASSO-regression analyses were applied to select the optimal features and construct radiomic signature. The radiomic signature and clinical signature were integrated to develop a predictive model by using machine learning algorithms including LR, RF, SVM, and XGBoost. Receiver operating characteristic curve and calibration curve analyses were used to evaluate the predictive performance of the models. RESULTS: The radiomic-XGBoost model with the highest AUC of 0.93 of the training cohort and 0.9 of the test cohort among the machine learning algorithms. The combined-XGBoost model with the best performance with an AUC of 0.9 in the training cohort and 0.9 in the test cohort. The radiomic-XGBoost model and combined-XGBoost model achieved better performance to assess the imminent risk of NVF than that of the clinical risk factors alone (p < 0.05). CONCLUSION: Radiomic and machine learning modeling based on T2W images of preoperative lumbar MRI had an excellent ability to evaluate the imminent risk of NVF after VA.

Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation.

BACKGROUND: Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. METHODS: A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. RESULTS: The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model. CONCLUSIONS: The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.

Motion acoustic flow field: Underwater small target motion characterization for active sonar echograph of harbor environment.

Spatial-temporal variations of active sonar echo intensity can provide effective motion information for characterizing intruding small targets and play a key role in follow-up tracking, behavioral analysis, and recognition, etc. Inspired by the idea of optical flow, which can be used to calculate subtle spatial-temporal variations of each pixel in image sequences, a different motion acoustic flow field (MAFF) is proposed for estimating the motion of underwater small targets in successive active sonar echographs from harbor environments. This is because directly applying current calculation framework for optical flow presents two challenges in this case. The first challenge is that the echo intensity fluctuation breaks its potential assumption of brightness consistency in the pre-processing stage. The second challenge is that the small size of the blob targets could be smoothed out as outliers by its median filter-based post-processing. Hence starting from the classical optical flow equation, MAFF introduces a novel spatial-temporal connected component pre-processing and a novel blob shape segmentation refinement post-processing. Experiments on a set of real-world harbor datasets demonstrate the efficacy of our MAFF calculation framework.

High-order time lacunarity feature-aided multiple hypotheses tracking for underwater active small targets in high-clutter harbor environment.

Active tracking of underwater small targets is a great challenge with kinematic information alone. This is because the active sonar often encounters multipath propagation and the induced clutter can even mask target echoes. Recently, high-order time lacunarity (HOT-Lac) has shown its ability in effectively highlighting "blob" targets from high clutter harbor environments. Hence, this paper proposes a HOT-Lac aided track scoring mechanism to solve the ambiguity of data association within the framework of Multiple Hypotheses Tracking (MHT). Specifically, the trajectory consistency of potential targets is captured by a momentum accumulation of the HOT Lac feature, which can inherit the historical information for the whole track. Meanwhile, due to the separability of the distribution of target and clutter in the HOT-Lac feature space, the probabilities of the target hypothesis and null hypothesis are modeled by the online computation of the HOT-Lac feature. Finally, the cumulative likelihood ratio based on HOT-Lac is integrated into MHT to score the potential tracks. Experiments in several real-world harbor scenarios demonstrate that the proposed HOT-Lac feature-aided tracker can suppress false tracks accurately and quickly.

Detecting moving targets in active sonar echograph of harbor environment using robust high-order flux tensor.

An important goal of an active sonar system is to detect and track underwater intruders such as frogmen, unmanned underwater vehicles, etc. Unfortunately, the intruders appear visually as a small fluctuating "blob" against the high-level fluctuating background caused by multipath propagation and reverberation in the harbor environment, making it difficult to be distinguished. Classical motion features well developed in computer vision cannot cope with an underwater environment. Thus, this paper presents a robust high-order flux tensor (RHO-FT) to characterize the small underwater moving targets against high-level fluctuating background. According to the dynamic behavior of active clutter from real-world harbor environment, we first classify it into two main types: (1) dynamic clutter but with relatively consistent spatial-temporal variation in a certain neighborhood; (2) sparkle clutter presenting completely random flashing. Then starting from the classical flux tensor, we develop a statistical high-order computation to handle the former followed by a spatial-temporal connected component to suppress the latter to achieve higher robustness. Experiments on a set of real-world harbor datasets demonstrate the effectiveness of our RHO-FT.

An Enhanced Offset Tracking Method: Providing Auxiliary Information for DInSAR Phase Filtering in Urban Areas.

In the application of synthetic aperture radar differential interferometry in urban environments, it is easy to regard the phase change in the deformation band of buildings under construction as noise that requires filtering. This introduces an error into the surrounding area while over-filtering, resulting in an error in the magnitude of the deformation measurement results for the entire region and the loss of deformation details in the surrounding area. Based on the traditional DInSAR workflow, this study added a deformation magnitude identification step, determined the deformation magnitude by using enhanced offset tracking technology, supplemented the filtering quality map and removed the construction areas that affect the interferometry in the filtering stage. The enhanced offset tracking technique adjusted the ratio of contrast saliency and coherence via the contrast consistency peak in the radar intensity image, which was used as the basis for adjusting the adaptive window size. The method proposed in this paper was evaluated in an experiment on a stable region using simulated data and in an experiment on a large deformation region using Sentinel-1 data. The experimental results show that the enhanced method has a better anti-noise ability than the traditional method, and the accuracy rate is improved by about 12%. The supplemented quality map can effectively remove the large deformation area to prevent over-filtering while ensuring the filtering quality, and it can achieve better filtering results.

Computational Insight into the Nature and Strength of the π-Hole Type Chalcogen∙∙∙Chalcogen Interactions in the XO2∙∙∙CH3YCH3 Complexes (X = S, Se, Te; Y = O, S, Se, Te).

In recent years, the non-covalent interactions between chalcogen centers have aroused substantial research interest because of their potential applications in organocatalysis, materials science, drug design, biological systems, crystal engineering, and molecular recognition. However, studies on π-hole-type chalcogen∙∙∙chalcogen interactions are scarcely reported in the literature. Herein, the π-hole-type intermolecular chalcogen∙∙∙chalcogen interactions in the model complexes formed between XO2 (X = S, Se, Te) and CH3YCH3 (Y = O, S, Se, Te) were systematically studied by using quantum chemical computations. The model complexes are stabilized via one primary X∙∙∙Y chalcogen bond (ChB) and the secondary C-H∙∙∙O hydrogen bonds. The binding energies of the studied complexes are in the range of -21.6~-60.4 kJ/mol. The X∙∙∙Y distances are significantly smaller than the sum of the van der Waals radii of the corresponding two atoms. The X∙∙∙Y ChBs in all the studied complexes except for the SO2∙∙∙CH3OCH3 complex are strong in strength and display a partial covalent character revealed by conducting the quantum theory of atoms in molecules (QTAIM), a non-covalent interaction plot (NCIplot), and natural bond orbital (NBO) analyses. The symmetry-adapted perturbation theory (SAPT) analysis discloses that the X∙∙∙Y ChBs are primarily dominated by the electrostatic component.

Characterization of heavy metal content distribution and evaluation of soil pollution in Maqin County, Qinghai Province, China.

The Qinghai-Tibet Plateau stands as the loftiest geographical area on our planet, frequently denoted as the "Crown of the Globe." To acquire an exhaustive comprehension of the heavy metal contamination situation in the topsoil of Maqin County, Qinghai Province, a total of 1616 surface soil specimens were gathered across a 6300 km2 area. An examination was carried out on 12 metallic elements to investigate the impact of diverse geological contexts, soil categorizations, and land utilization practices on the levels of heavy metals. Additionally, the fundamental factors contributing to these trends were probed. The findings unveiled that the mean levels of the 12 metallic elements in the topsoil of Maqin County surpassed or equaled the baseline values of soil heavy metal concentrations within the research region. The coefficients of variation (CV) values for Hg, Sb, Ni, and Pb exceeded 30%, with Hg showing strong variation. The overall pollution level in the study area was classified as mild, posing a moderate ecological risk. In this study, we performed a multi-factor analysis of the significant differences in heavy metal concentrations among different geological backgrounds, soil types, and land-use types. The results showed that geological background had extremely significant impacts on elements such as Ba, Be, Cd, Cr, Cu, Hg, Ni, Sb, Tl, and Zn (p < 0.01). Soil type had an extremely significant influence on Be, Cd, Cu, and Zn (p < 0.01), as well as a significant influence on Ba (p < 0.05). Land-use type had an extremely significant impact on Ba (p < 0.01) and a significant impact on Cd (p < 0.05). Building upon the amalgamation of the outcomes from the Pearson correlation analysis, it was inferred that the main source of heavy metals in Maqin County, Qinghai Province, was the geological background.