Small Cluster Sizes Research Articles

Does arginine aggregate formation in aqueous solutions follow a two-step mechanism?

The formation of aggregates was studied in arginine aqueous solutions using light scattering. The main driving force for aggregate formation is hydrogen bonding between the arginine (Arg) amino acids, which is partially verified using density functional theory calculations. The measurement of energy loss during this process, coupled with Cryo-EM morphology data, indicates that these aggregates are in the solid state. The aggregation occurs in two steps, with a liquid intermediate stage. The investigation of the effect of pH and solute concentration on aggregate formation for other amino acid aqueous solutions verifies that aggregate formation is amino-acid specific, while small-sized clusters formed by weak interactions lead to large-sized aggregation. The water structure around amino acid molecules sheds light on the prediction of their aggregate formation. Homochirality is observed in the aggregates; its existence sheds light on the origin of protein homochirality.

Photocatalytic Production of Ethanolamines and Ethylenediamines from Bio-Polyols over a Cu/TiO2 Catalyst.

Valorization of biomass-derived polyols into high-value-added ethanolamines and ethylenediamines is highly attractive. Herein, we report a one-step photocatalytic protocol to convert bio-polyols into a 60 % yield of ethanolamines and ethylenediamines over a multifunctional Cu/TiO2 catalyst. This catalyst enables a tandem process of photocatalytic polyol C-C bond cleavage and reductive amination in one pot at room temperature, and also allows the selective conversion of various bio-polyols and amines. Mechanistic studies revealed that photogenerated holes in TiO2 promote the retro-aldol C-C bond cleavage or oxidative dehydrogenation of polyols, and photogenerated electrons accumulate on small-sized Cu clusters, which facilitate the reductive amination via hydrogen transfer and prevent the H2 generation. This strategy provides new opportunities for the development of non-noble metal photocatalysts and methods of biomass conversion under mild conditions.

Anomalous dimensions of the Smoluchowski coagulation equation.

The coagulation (or aggregation) equationwas introduced by Smoluchowski in 1916 to describe the clumping together of colloidal particles through diffusion, but has been used in many different contexts as diverse as physical chemistry, chemical engineering, atmospheric physics, planetary science, and economics. The effectiveness of clumping is described by a kernel K(x,y), which depends on the sizes of the colliding particles x,y. We consider kernels K=(xy)^{γ}, but any homogeneous function can be treated using our methods. For sufficiently effective clumping 1≥γ>1/2, the coagulation equationproduces an infinitely large cluster in finite time (a process known as the gel transition). Using a combination of analytical methods and numerics, we calculate the anomalous scaling dimensions of the main cluster growth. Apart from the solution branch which originates from the exactly solvable case γ=1, we find a branch of solutions near γ=1/2, which violates matching conditions for the limit of small cluster sizes, widely believed to hold on a universal basis.

Exploring the Potential of Hierarchical Zeolite-Templated Carbon Materials for High-Performance Li-O2 Batteries: Insights from Molecular Simulations.

The commercialization of ultrahigh capacity lithium-oxygen (Li-O2) batteries is highly dependent on the cathode architecture, and a better understanding of its role in species transport and solid discharge product (i.e., Li2O2) formation is critical to improving the discharge capacity. Tailoring the pore size distribution in the cathode structure can enhance the ion mobility and increase the number of reaction sites to improve the formation of solid Li2O2. In this work, the potential of hierarchical zeolite-templated carbon (ZTC) structures as novel electrodes for Li-O2 batteries was investigated by using reactive force field molecular dynamics simulation (reaxFF-MD). Initially, 47 microporous zeolite-templated carbon morphologies were screened based on microporosity and specific area. Among them, four structures (i.e., RHO-, BEA-, MFI-, and FAU-ZTCs) were selected for further investigation including hierarchical features in their structures. Discharge product cluster analysis, self-diffusivities, and density number profiles of Li+, O2, and dimethyl sulfoxide (DMSO) electrolyte were obtained to find that the RHO-type ZTC exhibited enhanced mass transfer compared to conventional microporous ZTC (approximately 31% for O2, 44% for Li+, and 91% for DMSO) electrodes. This is due to the promoted formation of small-sized product clusters, creating more accessible sites for oxygen reduction reaction and mass transport. These findings indicate how hierarchical ZTC electrodes with micro- and mesopores can enhance the discharge performance of aprotic Li-O2 batteries, providing molecular insights into the underlying phenomena.

Decoding meningioma heterogeneity and neoplastic cell—macrophage interaction through single-cell transcriptome profiling across pathological grades

BackgroundAnalyzing meningioma of distinct pathological types at the single-cell level can provide new and valuable insights into the specific biological mechanisms of each cellular subpopulation, as well as their vital interplay within the tumor microenvironment.MethodsWe recruited patients diagnosed with four distinct types of meningioma and performed single-cell RNA sequencing on their tumor samples, concurrently analyzing a publicly available dataset for comparison. Next, we separated the cells into discrete clusters and identified their unique identities. Using pseudotime analysis, we demonstrated cellular differentiation and dynamics. To investigate biological function, we employed weighted gene co-expression network analysis, gene regulatory network, and gene set enrichment analysis. Additionally, we conducted cell–cell communication analyses to characterize interactions among different clusters and validated a crucial interaction using multiple immunofluorescence staining.ResultsThe single-cell transcriptomic profiles for five meningioma of different pathological types demonstrated that neoplastic cells exhibited high inter-sample heterogeneity and diverse biological functions featured by metabolic regulation. A small cluster of neoplastic cells (N5 cluster, < 3%) was most proliferative, indicated by high expression of MKI67 and TOP2A. They were primarily observed in our atypical and transitional meningioma samples and located at the beginning of the pseudotime differentiation branch for neoplastic cells. Macrophages, the most abundant immune cells present, showed two distinct developmental trajectories, one promoting and the other suppressing meningioma growth, with the MIF-CD74 interaction serving as the primary signaling pathway for MIF signals in the tumor environment. Unexpectedly, despite its small cluster size, the N5 cluster demonstrated a significant contribution in this interaction. By staining pathological sections of more samples, we found that this interaction was widely present in different types of meningiomas.ConclusionsMeningioma neoplastic cells' diverse types cause inter-sample heterogeneity and a wide range of functions. Some proliferative neoplastic cell may educate macrophages, which promotes tumorigenesis possibly through the MIF-CD74 interaction. It provides novel clues for future potential therapeutic avenues.

Maximizing Signal Quality for One Dimensional Cells In Mobile Communications

In this work, the cellular network performance based on other cell interference predictions is presented. It presents a mathematical model of co-channel interference analysis in hexagonal and linear cell shapes through a log-distance propagation model to investigate the effect of path loss exponent value on the received signal quality of the downlink. Simulation results obtained show that as the power exponent value increase, the interfering signals attenuation is increased resulting in received signal quality improvement. The signal-to-interference ratio (SIR) received by subscribers close to the cell edge will be less due to the contribution of the near-interfering cells especially when multiple layers of interfering cells are considered. The simulations confirmed that the impact of multi tiers of interfering cells cannot be ignored in systems of small cluster size as they may contribute to system performance degradation.

Displacement cascade under high-energy irradiation and tensile property in Fe-Cr-Al alloys: A molecular dynamics simulation

Fe-Cr-Al alloy is considered as one of the most prospective accident-tolerant fuel cladding materials due to its excellently comprehensive properties. Irradiation performance evaluation of Fe-Cr-Al alloy is the key for successful development of fuel-clad system with enhanced accident tolerance. However, the microstructural response is largely dependent on irradiated conditions. In the present work, molecular dynamic method is employed to simulate the cascade collision processes under high-energy irradiation and tensile deformation behaviors of Fe-Cr-Al alloys. The threshold displacement energies (TDEs) in Fe-Cr-Al alloys are calculated at different temperatures and along various directions, and the TDEs along different directions exhibit significant anisotropy. The effect of alloy compositions on the displacement cascade of Fe-Cr-Al alloys is systematically studied, as well as the direction of the primary knock-on atom (PKA), temperature, and PKA energy. At 300 K and 700 K, no significant dependence between the number of surviving Frenkel pairs and PKA energies is observed in Fe-Cr-Al alloys under high-energy irradiation. High temperature and high-energy PKA could potentially lead to form large-sized interstitial clusters and a few small-sized vacancy clusters in Fe-Cr-Al alloys. Dislocations or dislocation loops can easily form at high temperatures and in high-energy irradiation. The effect of alloy compositions, temperatures and PKA energies on the tensile properties of Fe-Cr-Al alloys before and after irradiation, is systematically evaluated. By analyzing the corresponding yield strength, ultimate tensile strength and elastic modulus, Fe-Cr-Al alloys exhibit the characteristic of irradiation-induced hardening and embrittlement with the increase of PKA energy. As the temperature increases, the rigidity of alloys decreases while the irradiation embrittlement significantly increases.

Configurations, electronic and magnetic properties of small-sized iron clusters on the graphdiyne surface

Using first-principles calculations, we have investigated the growth mode, magnetic and electronic properties of Fen clusters (n=1–10,13) on graphdiyne (GDY) surface. The small-size Fen with n≤3 clusters adsorbed on the surface of GDY favor a planar configuration and other clusters with n>3 have three-dimensional structures. The original morphology was maintained upon adsorption on the GDY surface. The Fen clusters with n=3–6,13 tend to bind with the surface with the lower adsorption energy. The relative stability of Fen clusters was not changed upon the adsorption. The remarkable charge transfers from the neighboring Fe atoms to the GDY surface account for the strong interactions of Fen with GDY. The magnetic moments of the adsorbed clusters were significantly reduced compared with free clusters. The adsorption of Fen clusters improves the conductivity of GDY.

A Federated Learning-Based Patient Monitoring System in Internet of Medical Things

Patient activities’ monitoring is a promising application of the Internet of Medical Things (IoMT), revolutionizing clinical diagnosis. An IoMT uses sensory data collected from smart devices to train a model on the server. The trained model recognizes the patient activities on smart devices. However, training the model on the server has raised privacy concerns and security threats. The sensitive medical data transferred from the smart devices to the Cloud poses different cybersecurity challenges, such as distributed denial of service (DDoS), phishing, network penetration, and side-channel. This article proposes a secure patient monitoring system using federated learning (FL). The system performs training on local devices and sends only weight matrices to the server for aggregation; thus, it preserves data privacy and security compromises. The system intelligently divides the participants into clusters based on the available resources, trains suitable models on each cluster, and enhances the performance via knowledge distillation (KD). The model of high-performing clusters distills knowledge to the model of small-size clusters to improve their performance. The experimental results illustrate that the proposed system successfully work in the presence of unequal resources.

Structures, Electronic, and Magnetic Properties of CoKn (n = 2-12) Clusters: A Particle Swarm Optimization Prediction Jointed with First-Principles Investigation.

Transition-metal-doped clusters have long been attracting great attention due to their unique geometries and interesting physical and/or chemical properties. In this paper, the geometries of the lowest- and lower-energy CoKn (n = 2-12) clusters have been screened out using particle swarm optimization and first principles relaxation. The results show that except for CoK2 the other CoKn (n = 3-12) clusters are all three-dimensional structures, and CoK7 is the transition structure from which the lowest energy structures are cobalt atom-centered cage-like structures. The stability, the electronic structures, and the magnetic properties of CoKn clusters (n = 2-12) clusters are further investigated using the first principles method. The results show that the medium-sized clusters whose geometries are cage-like structures are more stable than smaller-sized clusters. The electronic configuration of CoKn clusters could be described as 1S1P1D according to the spherical jellium model. The main components of petal-shaped D molecular orbitals are Co-d and K-s states or Co-d and Co-s states, and the main components of sphere-like S molecular orbitals or spindle-like P molecular orbitals are K-s states or Co-s states. Co atoms give the main contribution to the total magnetic moments, and K atoms can either enhance or attenuate the total magnetic moments. CoKn (n = 5-8) clusters have relatively large magnetic moments, which has a relation to the strong Co-K bond and the large amount of charge transfer. CoK4 could be a magnetic superatom with a large magnetic moment of 5 μB.

Longitudinal changes in qualitative aspects of semantic fluency in presymptomatic and prodromal genetic frontotemporal dementia.

The semantic fluency test is one of the most widely used neuropsychological tests in dementia diagnosis. Research utilizing the qualitative, psycholinguistic information embedded in its output is currently underexplored in presymptomatic and prodromal genetic FTD. Presymptomatic MAPT (n = 20) and GRN (n = 43) mutation carriers, and controls (n = 55) underwent up to 6years of neuropsychological assessment, including the semantic fluency test. Ten mutation carriers became symptomatic (phenoconverters). Total score and five qualitative fluency measures (lexical frequency, age of acquisition, number of clusters, cluster size, number of switches) were calculated. We used multilevel linear regression modeling to investigate longitudinal decline. We assessed the co-correlation of the qualitative measures at each time point with principal component analysis. We explored associations with cognitive decline and grey matter atrophy using partial correlations, and investigated classification abilities using binary logistic regression. The interrater reliability of the qualitative measures was good (ICC = 0.75-0.90). There was strong co-correlation between lexical frequency and age of acquisition, and between clustering and switching. At least 4years pre-phenoconversion, GRN phenoconverters had fewer but larger clusters (p < 0.001), and fewer switches (p = 0.004), correlating with lower executive function (r = 0.87-0.98). Fewer switches was predictive of phenoconversion, correctly classifying 90.3%. Starting at least 4years pre-phenoconversion, MAPT phenoconverters demonstrated an increase in lexical frequency (p = 0.009) and a decline in age of acquisition (p = 0.034), correlating with lower semantic processing (r = 0.90). Smaller cluster size was predictive of phenoconversion, correctly classifying 89.3%. Increase in lexical frequency and decline in age of acquisition were associated with grey matter volume loss of predominantly temporal areas, while decline in the number of clusters, cluster size, and switches correlated with grey matter volume loss of predominantly frontal areas. Qualitative aspects of semantic fluency could give insight into the underlying mechanisms as to why the "traditional" total score declines in the different FTD mutations. However, the qualitative measures currently demonstrate more fluctuation than the total score, the measure that seems to most reliably deteriorate with time. Replication in a larger sample of FTD phenoconverters is warranted to identify if qualitative measures could be sensitive cognitive biomarkers to identify and track mutation carriers converting to the symptomatic stage of FTD.

Genomic tracking and precise control of Klebsiella pneumoniae transmission in a newly established hospital: a prospective molecular epidemiological study

Carbapenem-resistant Klebsiella pneumoniae (CRKP) pose an emerging clinical threat. We investigated its introduction and transmission in a new hospital, evaluating the effect of whole-genome sequencing (WGS) as an infection control measure. Based on WGS of identified K. pneumoniae (Kpn) strains, a prospective molecular epidemiological study of nosocomial transmission of CRKP in a newly established Chinese hospital was conducted. Between September 2018 and August 2020, 206 Kpn strains were isolated, including 180 CRKP, from 152 patients. The first imported and nosocomial transmission cases were recorded in December 2018 and April 2019, respectively. Overall, 22 nosocomial transmission clusters involving 85 patients were identified, among which 5 were large-size clusters comprising 5-18 patients. Index cases of the large-size clusters were more likely associated with lower Glasgow Coma Scale scores than those of small-size clusters. Furthermore, results of multivariable logistic regression indicated that Kpn tended to transmit more among patients in the ICU [adjusted odds ratio (aOR)=4.96, 95% confidence interval (CI) 1.97-13.47] and those infected with a ST11 strain (aOR=8.04, 95% CI 2.51-29.53) or tetracycline-resistant strains (aOR=17.63, 95% CI 6.32-57.32). However, transmission was less likely in strains bearing the rmpA gene (aOR=0.12, 95% CI 0.03-0.37). The rate of nosocomial CRKP cases decreased by 2.25 with the intervention of WGS-based infection control. Kpn transmission in the newly established hospital originated from several imported cases. Rates of nosocomial CRKP infection were reduced considerably through precise infection control measures.

Is periventricular heterotopia a useful endpoint for developmental thyroid hormone system disruption in mouse toxicity studies?

In rats, hypothyroidism during fetal and neonatal development can disrupt neuronal migration and induce the formation of periventricular heterotopia in the brain. However, it remains uncertain if heterotopia also manifest in mice after developmental hypothyroidism and whether they could be used as a toxicological endpoint to detect TH-mediated effects caused by TH system disrupting chemicals. Here, we performed a mouse study where we induced severe hypothyroidism by exposing pregnant mice (n = 3) to a very high dose of propylthiouracil (PTU) (1500 ppm) in the diet. This, to obtain best chances of detecting heterotopia. We found what appears to be very small heterotopia in 4 out of the 8 PTU-exposed pups. Although the incidence rate could suggest some utility for this endpoint, the small size of the ectopic neuronal clusters at maximum hypothyroidism excludes the utility of heterotopia in mouse toxicity studies aimed to detect TH system disrupting chemicals. On the other hand, parvalbumin expression was manifestly lower in the cortex of hypothyroid mouse offspring demonstrating that offspring TH-deficiency caused an effect on the developing brain. Based on overall results, we conclude that heterotopia formation in mice is not a useful toxicological endpoint for examining TH-mediated developmental neurotoxicity.

Comparing estimation approaches for generalized additive mixed models with binary outcomes

Generalized additive mixed models (GAMMs) extend generalized linear mixed models (GLMMs) to allow the covariates to be nonparametrically associated with the response. Estimation of such models for correlated binary data is challenging and estimation techniques often yield contrasting results. Via simulations, we compared the performance of the Bayesian and likelihood-based methods for estimating the components of GAMMs under a wide range of conditions. For the Bayesian method, we also assessed the sensitivity of the results to the choice of prior distributions of the variance components. In addition, we investigated the effect of multicollinearity among covariates on the estimation of the model components. We then applied the methods to the Bangladesh Demographic Health Survey data to identify the factors associated with the malnutrition of children in Bangladesh. While no method uniformly performed best in estimating all components of the model, the Bayesian method using half-Cauchy priors for variance components generally performed better, especially for small cluster size. The overall curve fitting performance was sensitive to the prior selection for the Bayesian methods and also to the extent of multicollinearity.

FUCBR: Fuzzy‐based unequal clustering and bat‐based routing protocol for WSNs assisted IoT

SummaryThe internet of things (IoT) has recently become extremely important in our lives. With the advancement in IoT, wireless sensor networks (WSNs) have gained much importance. In WSNs, the routing of clusters has gained more interest. However, there is still a problem with the hotspot. One way to address this issue is uneven clustering. In these, nodes closer to the base station have the smallest cluster size. It decreases the relay pressure of the nodes which are close to the base station. In this paper, the unequal clustering routing based on fuzzy logic and bat algorithm is proposed, that is, FUCBR. Relative intra‐cluster costs and relative inter‐cluster costs are used in a very innovative manner. Fuzzy logic is used to create unequal clusters while the bat algorithm is used for multi‐hop routing. Unequal clustering minimizes the energy utilization for the member of the clusters. A novel probability technique is implemented in the cluster‐forming process to allow cluster members to determine which cluster they wish to join. The bat algorithm's objective function is carefully chosen to optimize the cluster heads' energy usage. After simulation, it was discovered that the proposed algorithm utilizes less energy and extends the network lifespan. The simulation results show that FUCBR's performance is 30% more improved than fuzzy‐based unequal clustering algorithm, genetic algorithm based unequal clustering and routing protocol, and grey wolf optimizer‐C algorithms. It is suitable for networks that require a long life.

Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19.

Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters. Poisson's prospective space-time analysis has been successfully implemented for cluster detection of geospatial time series data. However, its accuracy, number of clusters, and processing time are still a major problem for detecting small-sized clusters. The aim of this research is to improve the accuracy of cluster detection of COVID-19 at the county level in the U.S.A. by detecting small-sized clusters and reducing the noisy data. The proposed system consists of the Poisson prospective space-time analysis along with Enhanced cluster detection and noise reduction algorithm (ECDeNR) to improve the number of clusters and decrease the processing time. The results of accuracy, processing time, number of clusters, and relative risk are obtained by using different COVID-19 datasets in SaTScan. The proposed system increases the average number of clusters by 7 and the average relative risk by 9.19. Also, it provides a cluster detection accuracy of 91.35% against the current accuracy of 83.32%. It also gives a processing time of 5.69 minutes against the current processing time of 7.36 minutes on average. The proposed system focuses on improving the accuracy, number of clusters, and relative risk and reducing the processing time of the cluster detection by using ECDeNR algorithm. This study solves the issues of detecting the small-sized clusters at the early stage and enhances the overall cluster detection accuracy while decreasing the processing time.

Irradiation performance of concentrated solid-solution alloys: Insight into defect behaviors

Single-phase concentrated solid-solution alloys (SP-CSAs) intensively attracted researchers for their promising performances and tailorable constituents. In this work, molecular dynamic simulations were employed to investigate the radiation effects in equiatomic FeCrNi and FeNi, compared with an elemental Ni, mainly based on threshold displacement energy (Ed), individual recoil and overlapping cascades. Anisotropy of Ed distribution is a common feature in the three materials. However, defect behaviors concerning production in a single cascade and accumulation in multi-cascade process all exhibit distinctive features, which are particularly distinguished between CSAs and the elemental Ni. The discrepancy starts from the defect creation in the colliding phase, escalates in the thermal annealing phase of the single cascade, and is further amplified in the overlapping cascades. Corresponding physical processes involve atomic collision, heat dissipation and atomic diffusion. We discussed the channeling effect of atomic collision, thermal conductivity and sluggish diffusion in the CSAs to discover the origin causing the difference in defect morphology. Concentrated distribution of defects created in the colliding phase and low thermal dissipation contributes to higher defect recombination rate in the CSAs. Sluggish effect suppressing defect mobility further balances the production and annihilation of defects during the multi-cascades. We interpreted why the number of displacement defects is prone to be saturated under multi-cascades and only small-size clusters are left in the CSAs, unlike the pure Ni in which an oversized cluster was observed. The results are beneficial to designing radiation-tolerant materials by shedding light on the genesis of defect morphology in CSAs under irradiations.

Understanding the spatial configurations of Sm2O3 in NiO interfaces Embedded-Loaded for Electrocatalytic OER process

The efficacy of the oxygen evolution reaction (OER) can be enhanced by adding samarium (Sm) species to electrocatalysts as was recently found. Significantly, the spatial dispersal of Sm substances in the NiO would define their individual aids to promote electrocatalytic activity, which in turn affects the accessibility of Sm substance as a dopant material. In this work, we perform in-depth studies of two different configurations of electrocatalyst, Sm2O3-embedded NiO (Sm-NiO-E) and Sm2O3-surface-loaded NiO (Sm-NiO-L), to investigate the consequence of their distinctive configuration on OER activity. The Sm-NiO-E electrocatalysts exhibit an overpotential (ɳ) of 409 mV @ 10 mA cm-2 and a Tafel slope of 81 mV dec‑1, representing the advantage of the embedded design for water splitting in contrast to Sm-NiO-L (484 mV and 147 mV dec‑1). Embedding configuration exhibited the small-sized Sm2O3 clusters into the host for a bigger special interfacial region and enriching interfacial defects allows Sm-NiO-E to have a greater O2 adsorption capability and better tuned electronic assemblies of the interfacial active region. These results provide a valuable guide for designing electrocatalysts with different spatial layouts to enhance their electrocatalytic properties.

Fast Columnar Physics Analyses of Terabyte-Scale LHC Data on a Cache-Aware Dask Cluster

The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We present a solution to efficiently handle and accelerate physics analyses on small-size institute clusters. Our solution uses three key concepts: vectorized processing of collision events, the “MapReduce” paradigm for scaling out on computing clusters, and efficiently utilized SSD caching to reduce latencies in IO operations. This work focuses on the latter key concept, its underlying mechanism, and its implementation. Using simulations from a Higgs pair production physics analysis as an example, we achieve an improvement factor of 6.3 in the runtime for reading all input data after one cycle and even an overall speedup of a factor of 14.9 after 10 cycles, reducing the runtime from hours to minutes.

Regulated adsorption sites using atomically single cluster over biochar for efficient elemental mercury uptake

Carbon-based materials have been widely used in gaseous pollutant removal because of their sufficient surface functional groups; however, its removal efficiency for elemental mercury (Hg0) is low. In this study, we fabricated biomass using a chelated coupled pyrolysis strategy and further constructed the regulated adsorption sites for gaseous Hg0 uptake. A series of Mnδ-N2O2/BC with different manganese cluster sizes demonstrated that manganese clusters anchored on biochar acted as highly active and durable adsorbents for Hg0 immobilization, which increased the adsorption efficiency of Hg0 by up to 50%. Shrimp- and crab-based biochar adsorbents exhibited excellent Hg0 removal because of their chitosan-like structure. In particular, small Mn clusters and oxygen species around the defect led to a boost in the Hg0 adsorption by carbon. The results of density functional theory calculation revealed that the presence of oxygen in the carbon skeleton can tune the electrons of small-sized Mn clusters, thereby promoting the affinity of mercury atoms. The newly developed Mnδ-N2O2/BCshrimp had an adsorption capacity of 7.98–11.52 mg g−1 over a broad temperature range (50–200 °C) and showed a high tolerance to different industrial flue gases (H2O, NO, HCl, and SO2). These results provide novel green and low-carbon disposal methods for biomass resource utilization and industrial Hg0 emission control.Graphical