Random Attachment Research Articles

Emergent scale-free networks.

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

A New Approach to Computationally- Successful Linear and Polynomial Regression Analytics of Large Data in Medicine

Features like assortative mixing; high clustering, short average path lengths, broad degree distributions, and community structure have been the subject of numerous recent social network studies. All of these features are met by the model that is introduced in this study. Additionally, our model enables interactions between various communities, fostering a rich network environment. The asymptotically scale-free degree distribution is maintained by our model, which achieves a high clustering coefficient. In our model, the community structure is generated by a mix of mechanisms involving implicit preferential attachment and random attachment. We expand our consideration to include neighbor of neighbor of Initial Contact (NNIC) as well, in contrast to earlier approaches that solely focused on neighbor of Initial Contact (NIC) as an implicit preferential contact. If a newly added vertex chooses more than one initial contact, this extension makes it possible for contacts between those initial contacts to occur. Consequently, our model facilitates the development of complex social networks beyond those used as basic references. Finally, we conduct centrality calculations on both the existing model and our developed model, providing a comparative analysis of the results.

A Novel Approach for Analyzing Temporal Influence Dynamics in Social Networks

Features like assortative mixing; high clustering, short average path lengths, broad degree distributions, and community structure have been the subject of numerous recent social network studies. All of these features are met by the model that is introduced in this study. Additionally, our model enables interactions between various communities, fostering a rich network environment. The asymptotically scale-free degree distribution is maintained by our model, which achieves a high clustering coefficient. In our model, the community structure is generated by a mix of mechanisms involving implicit preferential attachment and random attachment. We expand our consideration to include neighbor of neighbor of Initial Contact (NNIC) as well, in contrast to earlier approaches that solely focused on neighbor of Initial Contact (NIC) as an implicit preferential contact. If a newly added vertex chooses more than one initial contact, this extension makes it possible for contacts between those initial contacts to occur. Consequently, our model facilitates the development of complex social networks beyond those used as basic references. Finally, we conduct centrality calculations on both the existing model and our developed model, providing a comparative analysis of the results.

Electron-Transport-Chain-Mediated Selective Growth of Gold Nanocrystals in the Intermembrane Space of Live Microbial Cells.

Hybridization of microbial cells with inorganic nanoparticles that could dramatically improve cellular functions such as electron transfer has been realized by the random attachment or stochastic entry of the nanoparticles. Clearly, the selective growth of inorganic nanoparticles on target functional organelles is ideal for such hybridization. Here, we report the selective growth of gold nanocrystals in the intermembrane space (IMS) of Escherichia coli by exploiting the electron transport chain (ETC). We systematically show that gold ions are permeated through porins in the outer membrane of E. coli and further reduced to gold nanocrystals by the ETC in live E. coli. We directly observe that the resulting gold nanocrystals exist only in the IMS by transmission electron microscopy measurements of cross-sectioned E. coli. Molecular dynamics simulations suggest that once gold ions are reduced to small nuclei by the ETC, the nuclei can be stably physisorbed onto ETC complexes, further supporting the ETC-mediated growth. Finally, we show that the ATP synthesis of E. coli where gold nanocrystals are formed in the IMS is up to 9 times higher than that of E. coli alone. We believe that our work can significantly contribute to not only improving microbial metabolic functions for biological energy conversion but also restoring physiological dysfunctions of microbial cells for biomedicine.

Organic-Solvent-Free "Lego-Like" Modular Preparation of Fab-Nondestructive Antibody-Drug Conjugates with Ultrahigh Drug-to-Antibody Ratio.

Antibody-drug conjugates (ADCs) have exciting possibilities in targeted tumor therapy. However, in the existing ADC preparation processes, the random attachment of the payloads to the antigen-binding fragments (Fab) greatly increases the risk of disrupting its antigen recognition ability, while the drug-antibody ratio(DAR) is low, leading to a cumbersome preparation process and low drug delivery efficiency. Herein, poly(glutamic acid) is used to expand the number of drug binding sites, based on the "click chemistry" of azide and DBCO, and the high affinity of Fc-III-4C peptide to the crystalline fragment (Fc) of the monoclonal antibodies. Various antibody-polymer-drug conjugates are obtained with ultrahigh DAR using this organic-solvent-free "Lego-like" modular construction. Among them, aHER2-P-MMAE with DAR of 41.6 achieves tumor growth inhibition (TGI) of 99.7% for both medium-sized and large SKOV-3 ovarian tumors, and aPDL1-P-MMAE (DAR = 40.7) achieves TGI of 98.5% for MC38 colon tumors. In summary, a universal platform is created to prepare Fab-nondestructive ADCs with ultrahigh DAR, which can be used to develop precision medicine for personalized anticancer therapy.

Clickable Shiga Toxin B Subunit for Drug Delivery in Cancer Therapy.

In recent years, receptor-mediated drug delivery has gained major attention in the treatment of cancer. The pathogen-derived Shiga Toxin B subunit (STxB) can be used as a carrier that detects the tumor-associated glycosphingolipid globotriaosylceramide (Gb3) receptors. While drug conjugation via lysine or cysteine offers random drug attachment to carriers, click chemistry has the potential to improve the engineering of delivery systems as the site specificity can eliminate interference with the active binding site of tumor ligands. We present the production of recombinant STxB in its wild-type (STxBwt) version or incorporating the noncanonical amino acid azido lysine (STxBAzK). The STxBwt and STxBAzK were manufactured using a growth-decoupled Escherichia coli (E. coli)-based expression strain and analyzed via flow cytometry for Gb3 receptor recognition and specificity on two human colorectal adenocarcinoma cell lines-HT-29 and LS-174-characterized by high and low Gb3 abundance, respectively. Furthermore, STxBAzK was clicked to the antineoplastic agent monomethyl auristatin E (MMAE) and evaluated in cell-killing assays for its ability to deliver the drug to Gb3-expressing tumor cells. The STxBAzK-MMAE conjugate induced uptake and release of the MMAE drug in Gb3-positive tumor cells, reaching 94% of HT-29 cell elimination at 72 h post-treatment and low nanomolar doses while sparing LS-174 cells. STxBAzK is therefore presented as a well-functioning drug carrier, with a possible application in cancer therapy. This research demonstrates the feasibility of lectin carriers used in delivering drugs to tumor cells, with prospects for improved cancer therapy in terms of straightforward drug attachment and effective cancer cell elimination.

Broadcasting‐induced colorings of preferential attachment trees

AbstractWe consider random two‐colorings of random linear preferential attachment trees, which includes recursive trees, plane‐oriented recursive trees, binary search trees, and a class of d‐ary trees. The random coloring is defined by assigning the root the color red or blue with equal probability, and all other vertices are assigned the color of their parent with probability and the other color otherwise. These colorings have been previously studied in other contexts, including Ising models and broadcasting, and can be considered as generalizations of bond percolation. With the help of Pólya urns, we prove limiting distributions, after proper rescalings, for the number of vertices, monochromatic subtrees, and leaves of each color, as well as the number of fringe subtrees with two‐colorings. Using methods from analytic combinatorics, we also provide precise descriptions of the limiting distribution after proper rescaling of the size of the root cluster; the largest monochromatic subtree containing the root.

Evolutionary modeling and robustness analysis of multi-domain cooperative communication network under the environment of Internet of things

In the future, the Internet of things will reduce the cell radius and increase the number of low-power nodes to support thousands of times of traffic growth under 5G. As a virtual multiple input multiple output technology, cooperative communication technology can solve these problems effectively. According to the evolution characteristics of cooperative communication networks, a multi-domain cooperative communication network evolution model with preferential attachment and random attachment is constructed in this article. And then, the network properties and robustness are analyzed using the mean-field method and different attacks. Aiming at the resource constraints and resource allocation problems of communication nodes, a relay selection strategy based on the combination of maximum degree and minimum clustering coefficient is proposed. The simulation results show that the relay node selection strategy based on the combination of maximum degree and minimum clustering coefficient has significant advantages in selection steps and selection time, which greatly enhanced the performance of relay selection in multi-domain cooperative communication networks. Through real-time monitoring and updating of the performance and security indicators of the multi-domain cooperative communication networks, it provides a strong guarantee for the node deployment and security management of the Internet of things cooperative communication system.

Structure of networks that evolve under a combination of growth and contraction.

We present analytical results for the emerging structure of networks that evolve via a combination of growth (by node addition and random attachment) and contraction (by random node deletion). To this end we consider a network model in which at each time step a node addition and random attachment step takes place with probability P_{add} and a random node deletion step takes place with probability P_{del}=1-P_{add}. The balance between the growth and contraction processes is captured by the parameter η=P_{add}-P_{del}. The case of pure network growth is described by η=1. In the case that 0<η<1, the rate of node addition exceeds the rate of node deletion and the overall process is of network growth. In the opposite case, where -1<η<0, the overall process is of network contraction, while in the special case of η=0 the expected size of the network remains fixed, apart from fluctuations. Using the master equationand the generating function formalism, we obtain a closed-form expression for the time-dependent degree distribution P_{t}(k). The degree distribution P_{t}(k) includes a term that depends on the initial degree distribution P_{0}(k), which decays as time evolves, and an asymptotic distribution P_{st}(k) which is independent of the initial condition. In the case of pure network growth (η=1), the asymptotic distribution P_{st}(k) follows an exponential distribution, while for -1<η<1 it consists of a sum of Poisson-like terms and exhibits a Poisson-like tail. In the case of overall network growth (0<η<1) the degree distribution P_{t}(k) eventually converges to P_{st}(k). In the case of overall network contraction (-1<η<0) we identify two different regimes. For -1/3<η<0 the degree distribution P_{t}(k) quickly converges towards P_{st}(k). In contrast, for -1<η<-1/3 the convergence of P_{t}(k) is initially very slow and it gets closer to P_{st}(k) only shortly before the network vanishes. Thus, the model exhibits three phase transitions: a structural transition between two functional forms of P_{st}(k) at η=1, a transition between an overall growth and overall contraction at η=0, and a dynamical transition between fast and slow convergence towards P_{st}(k) at η=-1/3. The analytical results are found to be in very good agreement with the results obtained from computer simulations.

Emergence of robust and efficient networks in a family of attachment models

Self-organization of robust and efficient networks is important for the future designs of communication or transportation systems, because both characteristics are not coexisting in many real networks. As one of the candidates for the coexisting, the optimal robustness of onion-like structure with positive degree–degree correlations has recently been found, and it can be generated by incrementally growing methods based on a pair of random and intermediation attachments with the minimum degree selection. In this paper, we introduce a continuous interpolation by a parameter β≥0 between random and the minimum degree attachments to investigate the reason why the minimum degree selection is important. However, we find that the special case of the minimum degree attachment can generate highly robust networks but with low efficiency as a chain structure. Furthermore, we consider two intermediation models modified with the inverse preferential attachment for investigating the effect of distance on the emergence of robust onion-like structure. The inverse preferential attachments in a class of mixed attachment and two intermediation models are effective for the emergence of robust onion-like structure. However, a small amount of random attachment is necessary for the network efficiency, when β is large enough. Such attachment models indicate a prospective direction to the future growth of our network infrastructures.

3-Aminopropyltriethoxysilane-directed formation of Au popcorns for colorimetric and SERS dual detection of cysteine

In this work, we demonstrated a facile approach for preparation of Au popcorns by using 3-aminopropyltriethoxysilane (APTES) as ligand. It was identified that APTES monomers acted as ligand to tune activity of the gold precursor and direct the preferential growth of (111) plane of face-centered cubic (fcc) Au, resulting in the formation of Au particles popcorn-like in shape via random attachment fashion. Attributed to the existence of APTES layer on their surface, the resulting Au popcorns presented excellent stability and improved peroxidase-like activity, making them qualified as probe for colorimetric and surface-enhanced Raman scattering (SERS) dual detection of cysteine.

Community modulated recursive trees and population dependent branching processes

AbstractWe consider random recursive trees that are grown via community modulated schemes that involve random attachment or degree based attachment. The aim of this article is to derive general techniques based on continuous time embedding to study such models. The associated continuous time embeddings are not branching processes: individual reproductive rates at each time t depend on the composition of the entire population at that time, and hence vertices do not reproduce independently. Using stochastic analytic techniques we show that various key macroscopic statistics of the continuous time embedding stabilize, allowing asymptotics for a host of functionals of the original models to be derived.

Antibody Self-Assembly Maximizes Cytoplasmic Immunostaining Accuracy of Compact Quantum Dots

Antibody conjugates of quantum dots (QDs) are expected to transform immunofluorescence staining by expanding multiplexed analysis and improving target quantification. Recently, a new generation of small QDs coated with multidentate polymers has improved QD labeling density in diverse biospecimens, but new challenges prevent their routine use. In particular, these QDs exhibit nonspecific binding to fixed cell nuclei and their antibody conjugates have random attachment orientations. This report describes four high-efficiency chemical approaches to conjugate antibodies to compact QDs. Methods include click chemistry and self-assembly through polyhistidine coordination, both with and without adaptor proteins that directionally orient antibodies. Specific and nonspecific labeling are independently analyzed after application of diverse blocking agent classes, and a new assay is developed to quantitatively measure intracellular labeling density based on microtubule stain connectivity. Results show that protein conjugation to the QD surface is required to simultaneously eliminate nonspecific binding and maintain antigen specificity. Of the four conjugation schemes, polyhistidine-based coordination of adaptor proteins with antibody self-assembly yields the highest intracellular staining density and the simplest conjugation procedure. Therefore, antibody and adaptor protein orientation, in addition to blocking optimization, are important determinants of labeling outcomes, insights that can inform translational development of these more compact nanomaterials.

Deep-sea plastisphere: Long-term colonization by plastic-associated bacterial and archaeal communities in the Southwest Atlantic Ocean

Marine plastic pollution is a global concern because of continuous release into the oceans over the last several decades. Although recent studies have made efforts to characterize the so-called plastisphere, or microbial community inhabiting plastic substrates, it is not clear whether the plastisphere is defined as a core community or as a random attachment of microbial cells. Likewise, little is known about the influence of the deep-sea environment on the plastisphere. In our experimental study, we evaluated the microbial colonization on polypropylene pellets and two types of plastic bags: regular high density polyethylene (HDPE) and HDPE with the oxo-biodegradable additive BDA. Gravel was used as control. Samples were deployed at three sites at 3300 m depth in the Southwest Atlantic Ocean and left for microbial colonization for 719 days. For microbial communities analysis, DNA was extracted from the biofilm on plastic and gravel substrates, and then the 16S rRNA was sequenced through the Illumina Miseq platform. Cultivation was performed to isolate strains from the plastic and gravel substrates. Substrate type strongly influenced the microbial composition and structure, while no difference between sites was detected. Although several taxa were shared among plastics, we observed some groups specific for each plastic substrate. These communities comprised taxa previously reported from both epipelagic zones and deep-sea benthic ecosystems. The core microbiome (microbial taxa shared by all plastic substrates) was exclusively composed by low abundance taxa, with some members well-described in the plastisphere and with known plastic-degradation capabilities. Additionally, we obtained bacterial strains that have been previously reported inhabiting plastic substrates and/or degrading hydrocarbon compounds, which corroborates our metabarcoding data and suggests the presence of microbial members potentially active and involved with degradation of these plastics in the deep sea.

Persistence of hubs in growing random networks

We consider models of evolving networks $$\left\{ {\mathcal {G}}_n:n\ge 0\right\} $$ modulated by two parameters: an attachment function $$f:{\mathbb {N}}_0 \rightarrow {\mathbb {R}}_+$$ and a (possibly random) attachment sequence $$\left\{ m_i:i\ge 1\right\} $$ . Starting with a single vertex, at each discrete step $$i\ge 1$$ a new vertex $$v_i$$ enters the system with $$m_i\ge 1$$ edges which it sequentially connects to a pre-existing vertex $$v\in {\mathcal {G}}_{i-1}$$ with probability proportional to $$f(\text{ degree }(v))$$ . We consider the problem of emergence of persistent hubs: existence of a finite (a.s.) time $$n^*$$ such that for all $$n\ge n^*$$ the identity of the maximal degree vertex (or in general the K largest degree vertices for $$K\ge 1$$ ) does not change. We obtain general conditions on f and $$\left\{ m_i:i\ge 1\right\} $$ under which a persistent hub emerges, and also those under which a persistent hub fails to emerge. In the case of lack of persistence, for the specific case of trees ( $$m_i\equiv 1$$ for all i), we derive asymptotics for the maximal degree and the index of the maximal deg ree vertex (time at which the vertex with current maximal degree entered the system) to understand the movement of the maximal degree vertex as the network evolves. A key role in the analysis is played by an inverse rate weighted martingale constructed from a continuous time embedding of the discrete time model. Asymptotics for this martingale, including concentration inequalities and moderate deviations form the technical foundations for the main results.

A new perspective of inorganic crystallization: Non-classical nucleation and growth

<p indent=0mm>Inorganic crystals surround us and play important roles in our daily life. They form integral parts of our body (e.g., bones), make up all the ground we stand on (ours and any other rocky planet consist of crystals), as well as show significant necessity for industrial processes and technologies (from table salt over concrete to biomedical nanoparticles). Accordingly, the formation mechanisms and properties of crystals have been extensively studied over the past decades. While there are longstanding theories on both the birth of crystals (nucleation) and their subsequent evolution (growth, recrystallization, and/or transformation), a vast amount of evidence suggests that the classical view on crystallization is over-simplified. In addition to the monomer-by-monomer (i.e., atoms, ions, or molecules) addition described in the classical theories, the particle attachment has been recognized as one of the most important pathways to inorganic crystallization. These particles range from the ion pairs to well-crystallized nanoparticles, such as amorphous precursors, magic-size clusters, nanocrystals, etc. The complexity of both the free-energy landscapes and reaction kinetics leads to diverse crystallization pathways. While experimental observations clearly demonstrate the non-classical crystallization pathways, many fundamental aspects remain unknown—Particularly the interaction of solution structure, interfacial forces, and particle motion. Thus, a predictive description linking molecular details to ensemble behavior is lacking. As such description develops, long-term interpretations of inorganic crystal formation should be revisited, which is important to broaden the scope of research across various disciplines such as geological events, biomineralization mechanisms, environmental remediation, and the development of environmentally functional materials. Based on several exemplary spotlights, the current state of the art in non-classical nucleation has been firstly illustrated in our review. Specifically, two non-classical nucleation pathways (i.e., the prenucleation clusters (PNCs) and aggregation pathway) in the current crystal research field are summarized. According to the PNCs pathway, PNCs were considered as the stable solute species with chainlike structural that are precursors to nucleation. Once system reaches the equilibrium ion activity product, the change in the chemistry of linkages within PNCs causes PNCs aggregation, as a consequence of nucleus formation. Another non-classical view (aggregation pathway) pointed that the fine crystalline nuclei first form in solution and then aggregate into a larger and more stable nucleus. Besides, the non-classical oriented attachment (OA) and random attachment (RA) growth pathways are also elucidated. OA and RA growth are similar in involving the self-assembly of primary nanocrystals. However, their main difference is the orientation of the crystal lattice at the grain boundary. For RA growth pathway, there is no particular preference for the attachment whereas for the OA growth, there is a common crystallographic alignment of the attachment to occur, which is allowing for continuous crystallographic planes. Finally, the implications and perspective of non-classical crystallization research are proposed. The aim of this review is to guide the readers through the complex world of crystallizing systems, highlighting a new perspective on non-classical crystallization of inorganic materials, which drives a true renaissance in the field and provides completely new perspectives on the underlying crystallization mechanisms. Additionally, through a mechanism-based understanding, we believed that non-classical crystallization processes can be used to produce innovative structures that retain the dimensional properties of their nanoscale building blocks and create materials with enhanced or novel physical and chemical properties.

Self-similar mesocrystals form via interface-driven nucleation and assembly.

Crystallization by particle attachment (CPA) is a frequently occurring mechanism of colloidal crystallization that results in hierarchical morphologies1-4. CPA has been exploited to create nanomaterials with unusual properties4-6 and is implicated in the development of complex mineral textures1,7. Oriented attachment7,8-a form of CPA in which particles align along specific crystallographic directions-produces mesocrystals that diffract as single crystals do, although the constituent particles are still discernible2,9. The conventional view of CPA is that nucleation provides a supply of particles that aggregate via Brownian motion biased by attractive interparticle potentials1,9-12. However, mesocrystals often exhibit regular morphologies and uniform sizes. Although many crystal systems form mesocrystals1-9 and individual attachment events have been directly visualized10, how random attachment events lead to well defined, self-similar morphologies remains unknown, as does the role of surface-bound ligands, which are ubiquitous in nanoparticle systems3,9,11. Attempts to understand mesocrystal formation are further complicated in many systems by the presence of precursor nanoparticles with a phase distinct from that of the bulk1,13,14. Some studies propose that such particles convert before attachment15, whereas others attribute conversion to the attachment process itself16 and yet others conclude that transformation occurs after the mesocrystals exceed a characteristic size14,17. Here we investigate mesocrystal formation by iron oxides, which are important colloidal phases in natural environments18,19 and classic examples of systems forming ubiquitous precursor phases and undergoing CPA accompanied by phase transformations15,19-21. Combining in situ transmission electron microscopy (TEM) at 80degrees Celsius with 'freeze-and-look' TEM, we tracked the formation of haematite (Hm) mesocrystals in the presence of oxalate (Ox), which is abundant in soils, where iron oxides are common. We find that isolated Hm particles rarely appear, but once formed, interfacial gradients at the Ox-covered surfaces drive Hm particles to nucleate repeatedly about two nanometres from the surfaces, to which they then attach, thereby generating mesocrystals. Comparison to natural and synthetic systems suggests that interface-driven pathways are widespread.

Bastnäsite nanoparticles in carbonatite-syenite-hosted REE deposit： implication for La and Ce migration and bastnäsite growth

High-resolution transmission electron microscopy observations of bastnäsite from the Maoniuping rare earth element (REE) deposit at Panxi, SW China, revealed the presence of nanoparticles in the surface of bastnäsite crystal. The nanoparticles are identified as the bastnäsite nanocrystals, with 5–30 nm in length, by energy-dispersive spectrometry and fast fourier transform patterns. This represents the first observation of bastnäsite nanoparticle in nature, confirming a new form of migration and precipitation of La and Ce in the hydrothermal fluids. Meanwhile, we herein reveal that random attachment of small bastnäsite nanoparticles during aggregation-based growth initially produces large bastnäsite nanoparticles, driven by the tectonic stress. Subsequent evolution of the random aggregation and orientation of large bastnäsite nanoparticles result in bastnäsite crystal formation, driven by the tectonic stress and inherent surface stress. This underlines the bastnäsite crystallization by random aggregation and the significant role of tectonic stress in forming the bastnäsite crystal in REE deposit.

Sensitivity analysis for network observations with applications to inferences of social influence effects

AbstractThe validity of network observations is sometimes of concern in empirical studies, since observed networks are prone to error and may not represent the population of interest. This lack of validity is not just a result of random measurement error, but often due to systematic bias that can lead to the misinterpretation of actors’ preferences of network selections. These issues in network observations could bias the estimation of common network models (such as those pertaining to influence and selection) and lead to erroneous statistical inferences. In this study, we proposed a simulation-based sensitivity analysis method that can evaluate the robustness of inferences made in social network analysis to six forms of selection mechanisms that can cause biases in network observations—random, homophily, anti-homophily, transitivity, reciprocity, and preferential attachment. We then applied this sensitivity analysis to test the robustness of inferences for social influence effects, and we derived two sets of analytical solutions that can account for biases in network observations due to random, homophily, and anti-homophily selection.

Histidine-directed formation of nearly monodispersed silver nanoflowers and their ultra-high peroxidase-like activity under physiological pH

In this work, we demonstrated a new approach for the preparation of nearly monodispersed silver nanoflowers (Ag NFs) with ultra-high peroxidase-like activity under physiological pH (6.0) by using histidine as both ligand and shape-directing agent and hydroxylamine as reductant. It was identified that the histidine could tune the activity of the silver precursor effectively and direct the preferential growth of the (111) facet of Ag particles via the random attachment. Under the appropriate concentration of histidine, both increase in the concentration of the reductant and elevation in pH of the reaction solution (to improve the activity of reductant) favorited the formation of Ag NF. The resulting histidine-coated Ag NFs showed ultra-high peroxidase-like activity at pH 6.0 due to the critical role of histidine in the enhanced binding affinity of Ag NFs towards both H2O2 and 3,3,5,5-tetramethylbenzidine (TMB) substrates. Based on the reducing ability of ascorbic acid (AA) towards the oxidized TMB, a simple and efficient colorimetric method was developed for the detection of AA under physiological pH.