These findings shed new light on the understanding of spatiotemporal regulation of vesicle trafficking through the interplay between membranous structures and membraneless condensates.ELKS proteins play a key role in organizing intracellular vesicle trafficking and targeting in both neurons and non-neuronal cells. While it is known that ELKS interacts with the vesicular traffic regulator, the Rab6 GTPase, the molecular basis governing ELKS-mediated trafficking of Rab6-coated vesicles, has remained unclear. In this study, we solved the Rab6B structure in complex with the Rab6-binding domain of ELKS1, revealing that a C-terminal segment of ELKS1 forms a helical hairpin to recognize Rab6B through a unique binding mode. We further showed that liquid-liquid phase separation (LLPS) of ELKS1 allows it to compete with other Rab6 effectors for binding to Rab6B and accumulate Rab6B-coated liposomes to the protein condensate formed by ELKS1. We also found that the ELKS1 condensate recruits Rab6B-coated vesicles to vesicle-releasing sites and promotes vesicle exocytosis. Together, our structural, biochemical, and cellular analyses suggest that ELKS1,  Intracellular vesicle trafficking is a complex and tightly controlled process that involves the transport of membrane-bound vesicles between different cellular compartments , 2, 3. TRab6 is an evolutionarily conserved Rab-GTPase that associates with vesicles and regulates vesicle trafficking to and from the Golgi apparatus , 9, 10. Erc1 and Erc2, expressing ELKS1 and ELKS2, respectively  for regulating synaptic vesicle unloading and neurotransmitter release (A). The Rab6-binding site has been mapped to a C-terminal part of the coiled-coil region in ELKS  in vertebrates are encoded by two genes, ectively , 19, 20.ectively . In neur release , 23. In  release . In panc release , 26. The release A. The Ra in ELKS , 18. HowQ72L. Our structural analyses revealed a unique Rab6-binding mode for ELKS by comparison to other Rab/effector interactions. In addition, we found that Rab6-coated vesicles can be recruited to the ELKS1 condensates formed via LLPS. Disrupting the ELKS1/Rab6 interaction or the condensate formation of ELKS1 led to the defective secretion of neuropeptide Y carried by Rab6-positive vesicles. These results suggest that by capturing Rab6-coated vesicles, ELKS facilitates the unloading of cargo from the transport machine and promotes their secretion.To better understand the structural basis of the ELKS/Rab6 interaction and its role in vesicle trafficking and targeting, we biochemically characterized the interaction between ELKS1 and Rab6B and determined the atomic structure of the Rab6-binding domain of ELKS1 in complex with the constitutively active Rab6B mutant, Rab6BA). In agreement with the previous GST-pull down assay (Q72L but not the inactive mutant Rab6BT27N (B and B), named the Rab-binding domain or RBD hereafter. Similarly, the corresponding region in ELKS2 (ELKS2_RBD) also formed the complex with Rab6BQ72L in the analytical gel filtration (C).To gain insights into the role of ELKSs in the trafficking of Rab6-coated vesicles, we set out to characterize the binding mode between ELKSs and Rab6. We purified the C-terminal coiled-coil region (residues 761–927) of ELKS1 and the GTPase domain of Rab6B A. In agrwn assay , our anaab6BT27N B and S1.7N B and B, named ltration C.D and E, ELKS1_RBD and ELKS2_RBD bind to Rab6BQ72L with a similar affinity of ∼8 μM, whereas the coiled-coil at the N-terminal to ELKS1_RBD showed no detectable binding to Rab6BQ72L, consistent with the analytical gel filtration analysis. Surprisingly, despite the location of the RBD in the predicted coiled-coil region, the molecular weight (MW) of the Trx-tagged ELKS1_RBD protein was measured to be ∼24 kDa using analytical gel filtration chromatography coupled with multi-angle static light scattering (F). This measured MW is close to the theoretical MW of the monomeric form, indicating that ELKS1_RBD may not form a dimeric coiled-coil and instead exists as a monomer in solution.To measure the binding affinity between ELKS_RBDs and Rab6B, we performed isothermal titration calorimetry (ITC)-based experiments. As indicated in attering F. This mQ72L, we crystallized the ELKS1_RBD/Rab6BQ72L complex and determined the complex structure at 2.04 Å resolution . In the complex structure (A), Rab6BQ72L adopts the typical active GTPase conformation with a bound GTP molecule and ELKS1_RBD consists of two α-helices (αN and αC) to interact with the switch Ⅰ, switch Ⅱ, and interswitch regions of Rab6BQ72L. The two helices in ELKS1_RBD fold as a helical hairpin through interhelical hydrophobic interactions (B), which resembles the coiled-coil conformation and explains the monomer instead of dimer formation of this predicted coiled-coil region.To dissect the molecular basis governing the ELKS/Rab6 interaction, we tried to solve the complex structure of ELKS1 and Rab6B by using crystallography. Using high-quality samples of ELKS1_RBD and Rab6Bsolution . In the ab6BQ72L A. In thetructure A, Rab6BQractions B, which Q72L involves both polar and hydrophobic interactions. Specifically, four pairs of charged residues  form salt bridges to stabilize the interaction between ELKS1 and Rab6B (B). The ITC-based analysis showed that the charge-reverse mutations, including D871RELKS1, K905EELKS1, D49RRab6B, and R63ERab6B, abolished the ELKS1_RBD/Rab6BQ72L interaction (C). Among these charged residues, D871ELKS1 and K13Rab6B are further involved in the hydrogen bond network connected by S868ELKS1 and Q65Rab6B (B), which further strengthens the binding of ELKS1_RBD to Rab6BQ72L. Consistently, disrupting the hydrogen bond network by replacing either Q65 or S868 with an alanine dramatically decreases the binding affinity (D). In addition to the polar interactions, several hydrophobic residues in ELKS1_RBD, including L864, I867, T908, and M912, pack with the hydrophobic pocket formed by the switch II and interswitch regions of Rab6B (B). Also, A875, L876, and L879 in αN of ELKS1_RBD form a hydrophobic patch to accommodate L11 in Rab6B. Interfering with the hydrophobic interaction by mutating the hydrophobic residues to hydrophilic ones impairs the interaction between ELKS1_RBD and Rab6BQ72L (E).The binding of ELKS1_RBD to Rab6Bnd Rab6B B. The ITeraction C. Among Q65Rab6B B, which affinity D. In addof Rab6B B. Also, ab6BQ72L E.A and B). However, the residue R63 in Rab6B is substituted with a glycine in Rab6C and Rab6D, which eliminates the R63-mediated salt bridging to ELKS1. Moreover, the residue G25 in Rab6B is substituted with alanine in Rab6C and Rab6D, leading to steric hindrance that interferes with GTP binding . Th. ThA andC), they cover a similar surface on Rab6, mainly formed by the switch Ⅰ and Ⅱ regions (A and D). Compared to the other Rab6 effectors, ELKS1 occupies a larger surface on Rab6, encompassing not only the switch regions but also the interswitch region . Additionally, despite its coiled coil-like fold, ELKS1_RBD adopts a distinct binding orientation with Rab6, as compared to the coiled coils of KIF20A_RBD and GCC185_RBD . Nevertheless, ELKS1_RBD binds to Rab6 with an affinity in the μM range (D), similar to other Rab6 effectors . Similar to ELKS1_RBD, the two RBDs of Rabenosyn-5 mainly bind to the switch II and interswitch regions of the Rab proteins (A and F). However, compared to those in Rabenosyn-5, the helical hairpin of ELKS1_RBD extends further toward the N-termini of the Rab structures (E). The extended part of ELKS1_RBD interacts with a region outside the canonical effector binding site, specifically, the residues L11 and K13 in the β1-strand of Rab6BQ72L (B), which are strictly conserved in Rab6 family members but not in other Rab members (A). Disrupting this interaction using the L11 E mutation in Rab6BQ72L dramatically decreases the binding affinity of ELKS1_RBD (E). Thus, our structure explains the specific binding of ELKS proteins to Rab6 rather than other Rabs.To understand the binding specificity of ELKS1_RBD for Rab6, we compared our structure with other Rab structures that have a bound effector with a similar helical hairpin conformation, including the Rab4A and Rab22 structures in complex with the two RBDs of Rabenosyn-5, respectively E. Similaproteins , A and Fructures E. The exab6BQ72L B, which  members A. DisrupLKS1_RBD E. Thus, A) (B) (Q72L-transfected HeLa cells drove the accumulation of Rab6BQ72L in the ELKS1 puncta rather than the opposite (C and D). To test whether the RBD in ELKS1 is essential for Rab6 recruitment, we mutated the Rab6-binding sites in ELKS1 according to our structural and biochemical findings. Although the ELKS1 mutants remained their punctate distribution in cells, they failed to accumulate Rab6BQ72L in their puncta (C and D). Likewise, mutations in Rab6BQ72L that disrupt the ELKS1 binding diminished the accumulation of Rab6BQ72L in the ELKS1 puncta (D and E), indicating that ELKS1 can recruit and accumulate Rab6 in an RBD-dependent manner. The D49R and R63E mutants of Rab6BQ72L were observed to accumulate modestly in the Golgi apparatus (E), likely due to the involvement of D49 and R63 in Rab6’s binding to Golgi-related effectors, such as Rab6IP1 and GCC185 . The above observations confirm our structural finding that although the ELKS-binding surface on Rab6 largely overlaps with other effector-binding surfaces, ELKS proteins possess a unique mode of binding to Rab6 (D). Notably, as L11E did not affect the Golgi localization of Rab6BQ72L, this mutation may be used to study Rab6 functions specifically related to ELKS in the future.ELKS1 forms many small puncta in cells A 31, 32, 32A (31A) B 13, 15, 15A (B)opposite , C and Dr puncta , C and D1 puncta , D and Epparatus E, likelyd GCC185 , 29. Inthe Golgi , D and E to Rab6 D. NotablQ72L and BICD2_RBD when the two proteins were co-expressed (F). However, co-transfecting ELKS1, BICD2_RBD, and Rab6BQ72L into HeLa cells, we found that BICD2_RBD and ELKS1 mutually limited each other from entering their puncta, despite their puncta both accumulating Rab6BQ72L (F). As BICD proteins have a sub-μM binding affinity to Rab6 that is much stronger than ELKS1’s binding to Rab6 (D)  proteins are Rab6 effectors that link Rab6-bound membranes with cytoskeletal motors, including dynein and kinesins , 34, 35.xpressed F. Howeveab6BQ72L F. As BIC to Rab6 D 36), i, iQ72L avia LLPS, which increases the local concentration of ELKS1 and thereby facilitates the acquisition of Rab6. To test this hypothesis, we designed a ELKS1 mutant that lacks the N-terminal IDR to disrupt the LLPS capability of ELKS1, as this region has been shown to be essential for LLPS in ELKS proteins (ΔIDR showed a predominantly diffused distribution in the transfected cells (ΔIDR (via the coiled coil-mediated dimer formation (ΔIDR-enriched puncta failed to accumulate Rab6BQ72L (C and D), although ELKS1ΔIDR contains the intact RBD, suggesting that the condensed ELKS1 enhances the acquisition of Rab6 from other Rab6 effectors.Considering ELKS proteins have the propensity to undergo liquid-liquid phase separation (LLPS) , 32, 37,ed cells . A smallls  similar to those previously reported using the N-terminal fragment . However, the mutations in Rab6BQ72L that disrupt its binding to ELKS1 led to a depletion of Rab6BQ72L accumulation (A), indicating the crucial role of the RBD-dependent interaction in recruiting Rab6 to the ELKS1 condensate.Since ELKS1 was reported to capture Rab6-coated vesicles in neurons and nonneuronal cells , 15, 38,fragment . By the ndensate A. Howevemulation A, indicaB). Next, we chemically linked Rab6BQ72L to rhodamine-liposomes and mixed them with ELKS1 droplets . Interestingly, the Rab6BQ72L-linked liposomes did not enter the ELKS1 condensate but instead accumulated on the surface of ELKS1 droplets (C and D). This observation indicates that the condensed ELKS1 phase can indeed capture vesicles, but the ELKS1 condensate is not immersible for membranous vesicles. In contrast, the liposome accumulation on the ELKS1 condensate was blocked by the D49R, R63E, and L11E mutations in Rab6BQ72L (C and E), confirming that the liposome recruitment by the ELKS1 condensate is dependent on Rab6 binding. Moreover, the inactive mutant Rab6BT27N-coated liposomes showed no accumulation on the droplet surface (C), further highlighting the importance of the specific binding of ELKS1 to the active form of Rab6 in vesicle recruitment.As ELKS proteins play a critical role in the targeting of secretory vesicles to specific areas of the plasma membrane and melanosomes , 39, we Fig. 5 surface C, furtheQ72L-coated liposome and phase-separated ELKS1. Although having a 10-fold higher protein concentration in solution, these RBDs showed little effect on the accumulation of Rab6BQ72L-coated liposomes on the ELKS1 droplets (F), confirming the capability of the condensed ELSK1 in acquiring Rab6-coated vesicles from other Rab6 effectors.To further validate the competition between the condensed ELKS1 and other Rab6 effectors in the dilute phase for recruiting Rab6-coated vesicles, we added the purified RBDs of BICD2, GCC185, and KIF20A into the mixture of the Rab6Bdroplets F, confirA and B), indicating that the RBD-dependent binding of ELKS1 to Rab6 is required for the recruitment of NPY vesicles to the secretion site enriched with ELKS1. Importantly, NPY vesicles accumulated at the cell edge when the K905E or S868A mutant of ELKS1 was overexpressed, which was not observed in the cells overexpressing the wild-type ELKS1 (C and D). These results suggest that the accumulation of Rab6 in the ELKS puncta is critical for the efficient secretion of NPY vesicles.Since ELKS1 may surpass other Rab6 effectors for binding to Rab6 by forming condensates, we speculated that ELKS1 unloads Rab6-coated vesicles to the releasing site by competing with Rab6 effectors in the transport machine. As ELKS proteins and Rab6 are known to be involved in the secretion of neuropeptide Y (NPY) , we decipe ELKS1 , C and DΔIDR mutant in the NPY secretion assay. As shown in A and B, ELKS1ΔIDR was unable to colocalize with NPY vesicles, despite retaining its RBD for Rab6 binding, likely because ELKS1ΔIDR cannot effectively compete with other Rab6 effectors in NPY vesicles for Rab6 binding. Consequently, ELKS1ΔIDR failed to promote the NPY secretion and led to the NPY vesicle aggregation at the cell edge (C and D), confirming the critical role of IDR-dependent LLPS in boosting Rab6 binding for ELKS1-mediated vesicle capturing and exocytosis.Next, we asked whether the LLPS of ELKS1 contributes to the NPY secretion. To address this question, we used the ELKS1ell edge , C and DAlthough intracellular vesicle trafficking has been studied for decades, the molecular mechanism underlying the unloading of transporting vesicles from the transport machine upon arrival at their destination remains poorly understood. As master organizers in vesicle trafficking, Rab GTPases, particularly Rab6, play a central role in regulating vesicle mobility, including transport, tethering, docking, and fusion , 38. In D and E) and Rab proteins attached to secretory vesicles have small protein copy numbers , ELKS pto ∼1 mM  for the to ∼1 mM , may aidA)  , while RA) . Thus, tEmerging evidence has shown that membraneless condensates can interact with membrane-bound organelles to modulate their organization or facilitate the trafficking and storage of organelles , 50. In NM_173781.4) were amplified from a mouse brain cDNA library. The full-length (1–208) or GTPase domain (8–176) of Rab6B was cloned into the modified pET-32a vector with an N-terminal Trx-His6 tag and an HRV-3C protease cutting site. Plasmids containing rat ELKS1 (GenBank: AY174115.1) were kind gifts from Prof. Mingjie Zhang. The full-length ELKS1 was first cloned into a modified pETL7 vector with an N-terminal His6-MBP-GFP tag followed by a TEV-protease cutting site that is kindly provided by Prof. Pilong Li and then His6-MBP-GFP-ELKS1 was subcloned into the pCAG vector with an N-terminal FLAG tag. The ELKS1 fragments used for biochemical and structural characterizations were subcloned into the modified pET-32a vector. The Rab6 binding domains (RBDs) of human BICD2 (aa 662–804), KIF20A (aa 603–665), and GCC185 (aa 1547–1612) were amplified from the cDNA library and cloned into the modified pET-32a vector. DNA encoding sequences of human NPY (GenBank: NM_000905.3) were commercially synthesized by Tsingke Biotech and cloned into the pEGFP-N1 vector. All point mutations were created using a site-directed mutagenesis kit and confirmed by DNA sequencing.DNA encoding sequences of mouse Rab6B  (Polysciences). For transfection, approximately 0.5 mg ELKS1 plasmids were pre-mixed with 1 mg PEIs in 50 ml fresh medium for 15 min and then 50 ml mixture was added to 500 ml cell culture. After 72 h culture, cells were collected at 4 °C and lysed with 50 mM Tris pH 7.5, 500 mM NaCl, 1 mM EDTA, 0.5% Triton X-100, and 1 × protease inhibitor cocktail. The ELKS1 protein was purified by anti-FLAG affinity chromatography and competitively eluted with buffer containing 100 to 500 μg FLAG peptide (DYKDDDDK) and followed by size-exclusion chromatography  with a buffer containing 50 mM Tris pH 7.5, 100 mM NaCl, and 2 mM DTT.For LLPS assay, the full-length ELKS1 was expressed in HEK 293F suspension cells (Thermo Fisher Scientific), cultured in Freestyle 293 medium (OPM-293 CD05 Medium) at 37 °C supplied with 5% CO6 fusion proteins were expressed in BL21(DE3) E. coli cells in LB medium after induction with 500 μM isopropyl-β-d-thiogalactopyranoside at 16 °C overnight. Cells were collected at 4 °C and disrupted with high pressure in 50 mM Tris pH 8, 500 mM NaCl, 10 mM Imidazole, 1 × protease inhibitor cocktail, and PMSF. Fusion proteins were purified by Ni2+-NTA affinity chromatography and followed by size-exclusion chromatography  in 50 mM Tris pH 7.5, 100 mM NaCl, 2 mM MgCl2, and 1 mM DTT. To prepare the Rab6BQ72L and ELKS_RBD fragments for crystallization, the N-terminal His6-Trx tags were cleaved by HRV 3C protease and removed by size-exclusion chromatography .Trx-HisQ72L and ELKS1_RBD were mixed with a 1:1 ratio and incubated overnight at 4 °C in 50 mM Tris pH 7.5, 100 mM NaCl, 2 mM MgCl2, 1 mM DTT, and 1 mM GTP. For crystallization, the complex was purified on Superdex 75 16/60 and concentrated at 15 mg/ml. Crystals were obtained by the sitting drop vapor diffusion method at 16 °C. To set up a sitting drop, 1 μl of concentrated protein solution was mixed with 1 μl of crystallization solution. The crystals of Rab6BQ72L and ELKS1_RBD complex were grown in the conditions containing 0.1 M HEPES pH 7.5 and 42 % v/v Polyethylene glycol 200. Before X-ray diffraction experiments, crystals were soaked in the crystallization solutions containing additional 30% v/v glycerol for cryoprotection. Diffraction data were collected at the Shanghai Synchrotron Radiation Facility. Data were processed and scaled using HKL2000 software (Rab6Bsoftware .Q72L and ELKS1_RBD complex were determined by molecular replacement in PHASER (Q72L apo structure (PDB ID: 2FFQ) as the search model. The structural models were refined in PHENIX (https://www.pymol.org).The crystal structures of Rab6Bn PHASER  using thn PHENIX  and adjun PHENIX . The modn PHENIX . The fin2, and 1 mM DTT.Analytical gel filtration chromatography was carried out on an ÄKTA pure system . Protein samples were loaded with 100 μl sample loop into a Superdex 200 Increase 10/300 Gl column  and equilibrated with a buffer containing 50 mM Tris pH 7.5, 100 mM NaCl, 2 mM MgClA miniDAWN TREOS (Wyatt Technology Corporation) coupled with an ÄKTA pure system  was used for molar mass measurement. The procedure follows the protocol used for analytical gel filtration analysis.2. Each titration point typically is consisted of injecting 3 μl aliquots of one protein (400 μM) in the syringe into its binding protein (40 μM) in the cell. A time interval of 150 s between two titration points was used to ensure the complete equilibrium of each titration reaction.Isothermal titration calorimetry (ITC) experiments were carried out on MicroCal PEAQ-ITC  at 25 °C. All proteins were dissolved in a buffer containing 50 mM Tris pH 7.5, 100 mM NaCl, and 2 mM MgCl3 pH 8.3, 100 mM NaCl, and 2 mM DTT. Fluorophores were mixed with the corresponding proteins in a 1:1 ratio and incubated at room temperature for 1 h. The reaction was quenched by adding 200 mM Tris pH 8.2. To remove the unlabeled fluorophores, the labeled proteins were exchanged into a buffer containing 50 mM Tris pH 8.2, 100 mM NaCl, and 2 mM DTT using a HiTrap desalting column. Fluorescence labeling efficiency was measured by Nanodrop 2000 (ThermoFisher). For imaging assays, fluorescently labeled proteins were added to the corresponding unlabeled proteins in the same buffer with the final molar ratio of 1:20.Cy5 NHS ester (AAT Bioquest) was dissolved by DMSO at a stock concentration of 5 mM. Purified proteins were exchanged to a buffer containing 100 mM NaHCO2, and 1 mM EGTA and passed 25 times through an extruder containing a 200-nm pore-size polycarbonate filter (Avanti). A 3:1 ratio of Rab6BQ72L to maleimide lipids was then mixed and incubated overnight. Next, DTT was added to a final concentration of 10 mM to quench the reaction, and the liposomes were pelleted at 160,000g for 20 min to remove unlabeled protein. The pellet was finally washed and resuspended with buffer.Liposomes were prepared with commercially available lipid components from Avanti Polar Lipids. A mixture of lipids was dissolved in chloroform at . The lipids were then dried under a constant stream of nitrogen and desiccated in a vacuum overnight. The lipid film was resuspended with a buffer containing 30 mM Hepes, pH 7.4, 50 mM KCl, 2 mM MgClg for 10 min at 4 °C to remove any precipitations and diluted to designed concentrations with a dilution buffer containing 50 mM Tris pH 7.5, 100 mM NaCl, and 2 mM DTT. To induce LLPS, MBP-GFP tagged ELKS1 were treated with TEV protease for ∼20 to 30 min and then dripped onto the 384-well glass bottom plates . The confocal images were taken by A1R (Nikon) with 100× oil objective lens. To monitor proteins in different samples under light microscopes, the unlabeled corresponding proteins were premixed with the fluorophore-labeled proteins. The volume of phase separated protein solution adding to the well was ∼5 μl. For the treatment with liposomes, each protein with an assigned concentration and liposomes were mixed in tubes for 3 min and then dropped onto the 384-well plates for imaging. During imaging, the 384-well plates were sealed by plastic stickers to reduce solution evaporation. Image fluorescence intensities were analyzed by the ImageJ/Fiji software.Stock proteins were centrifuged at ∼20,0002. Transfections of indicated plasmids were performed with Lipofectamine 3000 Transfection Reagent (ThermoFisher) according to the manufacturer’s instructions, and the cells were transferred to fibronectin (12.5 μg/ml)-coated coverslips at 24 h after transfection. After 12 h, the cells were fixed with 4% paraformaldehyde for 15 min at 37 °C. After washing with the PBS buffer for three times, the cells were treated with 0.1% Triton X-100 for 10 min at room temperature and blocked in 2% bovine serum albumin. The cells were visualized with 100× objective using a Nikon A1R Confocal Microscope. Image pro plus software was used for line analysis and NIS-Elements AR Analysis software was used for co-localization analysis.HeLa cells were maintained in Minimum Essential Medium Eagle supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin solution, and 1% non-essential amino acids. The cells were cultured at 37 °C in an incubator with 5% COe.g., number of experiment replications, number of cells, number of droplets, etc.) are reported in the figures or corresponding legends. All data were expressed as mean ± SD; ns, not significant; ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, and ∗∗∗∗p < 0.0001 using unpaired Student’s t test. ITC titration data were analyzed using the program MicroCal PEAQ-ITC Analysis. MALS data were analyzed in program ASTRA 6.1.2 (Wyatt Technology Corporation). Statistical data were conducted in GraphPad Prism 6 software.Statistical parameters including the definitions and exact values of n (Q72L have been deposited in the Protein Data Bank (https://www.wwpdb.org/) under the accession code 8IJ9. All other relevant data are available from the authors.Atomic coordinates and structure factor amplitudes for the complex structure of ELKS1_RBD/Rab6BThis article contains The authors declare no competing financial interests.


MXene-Ti3C2/Co NSs were characterized, and their enzyme-like activities were investigated. Under the catalysis of MXene-Ti3C2/Co NSs, 3,3′,5,5′-tetramethylbenzidine (TMB) could be oxidized by H2O2, with the color changing from colorless to blue. The affinity of MXene-Ti3C2/Co NSs to H2O2 and TMB was better than that of nanozymes reported in previous studies. The MXene-Ti3C2/Co NSs were used for the colorimetric determination of H2O2/glucose, with limits of detection (LODs) of 0.033 mM and 1.7 μM, respectively. MXene-Ti3C2/Co NSs embedded in sodium alginate (SA) hydrogel were used to construct a sensor platform. The digital pictures combined with a smartphone-installed app (color recognizer) could be used to analyze RGB values for colorimetric detection of glucose in beverages. This point-of-care testing platform has the advantages of cost-effectiveness and good transferability, with the potential to realize quick, intelligent and on-site detection.A one-step cobaltous chloride (CoCl Glucose is the most important energy source and metabolic intermediate of the human body and is widely present in various food products and beverages . Human c3O4) was reported to exhibit peroxidase-like activity in 2007, a large number of inorganic nanomaterials, including metal nanoparticles ) + 1/maxV) was generated according to the Michaelis–Menten equation, where V represents the initial reaction rate, maxV is the maximum reaction rate, mK is the Michaelis constant and can measure the affinity between enzyme and substrate and [S] represents the concentration of the substrate , which exhibits absorption peaks at 520 nm and 550 nm , which easily reacted with ·OH to generate 2-hydroxyl terephthalic acid with a unique fluorescence at 435 nm, was introduced as a fluorescence probe to track ·OH. As shown in 2O2 system increases continuously with the increase in the MXene-Ti3C2/Co NSs, which indicates that the ·OH produced by the TA + H2O2 system under acidic conditions constantly increased. Therefore, the catalytic system complies with heterogeneous Fenton catalytic reactions.In addition, MXene-Tid 550 nm . If Cyt 2O2 detection performance of our sensing system using MXene-Ti3C2/Co NSs as peroxidase-like enzymes. As shown in 2O2 increased regularly in an appropriate range, the absorbance of the ternary MXene-Ti3C2/Co NSs + H2O2 + TMB system at 652 nm also gradually increased. 2O2 concentration, ranging from 0.1 mM to 1.0 mM with a calibration function of A = 0.064C + 0.076 (C was the concentration of H2O2 in mM), and the lowest detected concentration was 0.033 mM. MXene-Ti3C2/Co NSs were used to catalyze the substrate of TMB, which was ultimately oxidized into oxTMB, with its color changing from colorless to bright blue  for RGB value analysis, and the relationship between glucose concentration and color was explored, realizing the intelligent detection of glucose. It is well known that any color composed of red (R), green (G) and blue (B) has corresponding R, G and B values. Therefore, the intensity of color signal can be calculated as B/(R + G + B). Through calculation and analysis, the color intensity of the hydrogel detection platform containing different concentrations of glucose showed a good linear relationship with glucose concentration from 0.01 to 0.1 mM. The calibration curve was I = 1.172C + 0.0391 (C represents glucose concentration in mM), and the detection limit was 0.001 mM . Based on the established detection system, the standard addition method was used to detect the glucose content. 3AlC2 was purchased from Forsman Technology (Beijing) Co., LTD . CoCl2·6H2O, H2O2 and sodium acetate were purchased from Sinopharm Chemical Reagent Co., Ltd. . Glucose and TMB were purchased from Aladdin Reagents Co., Ltd. . Acetic acid was purchased from Tianjin Tianli Chemical Reagent Co., Ltd. . Glucose oxidase (GOX), Tris-HCl, sodium alginate (SA) and terephthalic acid were purchased from Shanghai Macklin Biochemical Co., Ltd. . All chemical reagents were of analytical grade and used without further purification.TiFluorescence spectra were evaluated by an LS55 fluorescence spectrometer . UV-vis absorption spectra were collected with a Lambda 35 UV-visible spectrometer . X-ray photoelectron spectroscopy (XPS) was measured with an Escalab 250Xi spectrometer . X-ray diffraction (XRD) was recorded with a D8 DISCOVER diffractometer . Transmission electron microscopic (TEM) patterns were obtained by a TecnaiG20 transmission electron microscope . High-resolution transmission electron microscopic (HRTEM) images were observed using a JEM-2100 UHR . Scanning electron microscopic (SEM) images were taken with a Zeiss SIGMA field-emission scanning electronic microscope . An SX-4-10 box electric resistance furnace was purchased from Beijing Yongguangming Medical Instrument Factory . An RZ10 medical centrifuge was purchased from Changsha Ordinary Instrument Co., Ltd. .3AlC2 and 3.5 g of CoCl2·6H2O were weighed and placed in a mortar, mixed evenly and ground for 10 min. The pulverized mixture was transferred to a porcelain crucible with a volume of 50 mL and calcined at 730 °C for 1 h. After cooling and quenching, 20 mL of hydrochloric acid solution (3.0 M) was added to the powder and stirred continuously for 24 h. Next, the precipitate was washed with deionized water in a centrifuge until the supernatant pH was 7. Finally, the precipitates were collected and dried in vacuum at 70 °C for 12 h.Amounts of 0.5 g of Ti3C2/Co NSs were collected and dispersed in 5 mL deionized water for subsequent use.Then, 100 mg of the dried powder was dispersed in 25 mL deionized water with ultrasound for 5 h. The suspension was centrifuged at 5000 r for 8 min to remove precipitation; then, the supernatant was centrifuged at 9500 r for 30 min. Finally, MXene-Ti3C2/Co NSs and 10 µL 500 U mL−1 GOX. Then, MXene-Ti3C2/Co NS-SA hydrogel was formed with 5 mg mL−1 drops of CaCl2 solution.An amount of 0.1 g of SA power was dissolved in 5 mL of Tri-HCl buffer (0.01 M) with 100 µL of MXene-Ti2O2 concentration are as follows: 10 µL of TMB (0.1 M), 30 µL of MXene-Ti3C2/Co NSs and 10 µL of H2O2 with different concentrations were added to HAc-NaAc buffer solution (pH = 3.5) in turn and reacted at room temperature for 5 min. The UV absorbance at 652 nm was recorded by a UV spectrophotometer.The detailed steps for detection of H−1) and 10 µL of glucose solution with different concentrations were successively introduced into 80 μL of Tris-HCl buffer (0.01 M) and incubated at 37 °C for 40 min. Then, 10 µL of TMB, 30 µL of MXene-Ti3C2/Co NSs and 860 µL of HAc-NaAc were added to the reacted mixture in turn and incubated at room temperature for 5 min. Finally, the UV absorbance at 652 nm was measured by UV spectrophotometer.The steps of glucose concentration detection are as follows: 10 μL of GOX (500 U mL−1 GOX and 10 μL of glucose solution with different concentrations were mixed in 80 μL of Tri-HCl buffer (0.01 M) and incubated at 37 °C for 40 min. The obtained mixed solution, 60 μL of HAc-NaAc buffer (pH = 3.5) and 10 μL TMB (0.1 M) were successively added into SA hydrogel to react for 30 min. Color photographs of hydrogels with different glucose concentrations were taken by a smartphone. The color signal  including a red channel (R), blue channel (B) and green channel (G) was obtained by a smartphone application (color recognizer). The linear relationship between the B/(R + G + B) value and the concentration of glucose was obtained.The intelligent detection steps for glucose are as follows: 10 µL 500 U mL3C2/Co NSs were introduced into the mixture to cultivate for 5 min. The absorbance of the solution was determined by a UV spectrometer.We selected three popular sugar-free beverages, namely Yuanqi forest bubble water, alien electrolyte water and soda water, as actual samples and analyzed their glucose contents. The beverage samples were diluted 50 times as the actual samples to be tested. Then, 70 µL of Tris-HCl buffer (0.01 M), 10 µL of GOX and 20 µL of the beverage samples were incubated at 37 °C for 40 min. Then, 845 µL of HAc-NaAc buffer (pH = 3.5), 25 µL of TMB and 30 µL of MXene-Ti3C2/Co NSs with excellent dispersion were prepared by efficient molten salt method. MXene-Ti3C2/Co NSs were successfully applied in a UV detection system for the determination of trace concentrations of H2O2 and glucose due to their excellent peroxidase-like activity. In order to realize the rapid detection of glucose concentration, a hydrogel detection platform based on MXene-Ti3C2/Co NSs was constructed in combination with a smartphone analyzer by recording changes in RGB value with a smartphone app, and the results were consistent with those of the UV spectrophotometer. Compared with existing glucose monitoring approaches, the mK values of H2O2 and TMB in our proposed method were lower, indicating that the affinity of MXene-Ti3C2/Co NSs with substrate H2O2 and TMB was significantly better than that of many previously reported nanoenzymes. Owing to the simplicity of the method, the sample throughput can be easily increased, making it potentially useful for on-site monitoring of glucose.In conclusion, MXene-Ti


The solid-state structure of this precatalyst along with relevant homoleptic CoII and FeII complexes has been characterized by X-ray crystallography. In the solid state, 1 features one-dimensional hydrogen-bonded chains that are further stacked by interchain π⋯π interactions. The newly synthesized complexes (1–3) along with several known analogues (4–6) were examined as precatalysts for the hydroboration of alkenes. The best-performing system, 1/KOtBu was found to promote Markovnikov hydroboration of substituted styrenes with high turnover frequencies (TOFs) up to ∼47 000 h−1, comparable to the most efficient polymeric catalyst [Co(pytpy)Cl2]n reported to date. Although some limitations in substrate scope as well as functional group tolerance exist, the catalyst shows good promise for several relevant hydrofunctionaliation reactions.Homoleptic complexes adopting octahedral coordination modes are usually less active in catalysis due to the saturated coordination around metal centers that prevents substrate activation in a catalytic event. In this work, we demonstrated that a homoleptic octahedral cobalt complex (1)  ii) with 4′-pyridyl-2,2′;6′,2′′-terpyridine ligand is an efficient precatalyst for hydroboration of styrene derivatives with Markovnikov selectivity, displaying turnover frequencies up to 47 000 h−1.Monoprotonated homoleptic complex of cobalt( While all cobalt-based discrete molecular catalysts reported thus far realized alkene hydroboration with relatively low TOFs (<300 h−1), we have disclosed that a one-dimensional CoII-coordination polymer assembled from a divergent ligand, 4′-pyridyl-2,2′; 6′,2′′-terpyridine (pytpy), enabled Markovnikov-selective hydroboration of aryl alkenes with high TOFs up to 47 520 h−1.8 However, the origin of the extremely high catalytic efficiency of this polymeric cobalt catalyst remained unclear. To better understand whether the polymeric structure of catalyst is vital for the unexpected activity, we decided to explore discrete molecular analogues of the polymeric precatalyst by using the same pytpy ligand and different cobalt salts 2·6H2O in MeOH onto a CH2Cl2–MeOH solution  of pytpy over two weeks led to the formation of red block-like crystals of 1 that were suitable for single-crystal X-ray diffraction analysis. A bulk sample of the crystals has been isolated in 92% yield. The solution 1H NMR spectrum of 1 reveals broadened paramagnetic signals that could not be unambiguously assigned. The mass spectrum shows the only peak envelope at 679.1766 that can be assigned to the cation of the complex and the isotope pattern matches with that simulated. The solid-state structure of 1 was confirmed unambiguously by X-ray crystallography as a mono-protonated complex of [Co(pytpy)(H-pytpy)][BF4]3, a product obtained serendipitously during attempts to produce the expected complex [Co(pytpy)2][BF4]2. 1 crystallizes in the monoclinic space group P21/c. The spontaneous partial protonation of pytpy ligand during the complexation with transition metals is reminiscent of known Ru and Fe complexes of the same ligand.17,18 The synthesis of 1 was well reproducible as evidenced from several independent layering experiments as well as the direct reaction between the ligand and Co(BF4)2·6H2O in a CH2Cl2–MeOH solution. In contrast, the originally expected homoleptic complex [Co(pytpy)2][BF4]2 (2) has been synthesized by adopting the standard reaction sequence, i.e. the solution reaction of pytpy and CoCl2·6H2O followed by an anion exchange with excess amount of NaBF4 2 complexes.19 The ligand conformations in 1 and 2 are not the same. In both structures, the non-coordinated pyridine ring is twisted with respect to the tpy domain to which it is attached. For 1, the angles between the least squares planes of the rings containing atoms N2 and N4, and N6 and N8 are 33.32° and 34.84°, respectively. For 2, the relevant twist angles are 40.36° and 34.71°. In addition, the deviation away from linearity of the N4⋯Co⋯N8 angle is notable for the cation of 1. The angle of N4⋯Co1⋯N8 is 167.73(2)° for the cation of 1, but it is closer to linear in the cation of 2 (177.44(5)°). This is likely due to the ligand protonation in 1 and the resulting formation of one-dimensional (1-D) hydrogen-bonded chains as well as the major interchain π⋯π stacking between ‘side-arm’ pyridine rings observed in 1  hydrogen bonds  Å, H4⋯N8 = 1.82(9) Å, N4–H4⋯N8(i) = 172(7)°) and the chains are packed by π⋯π interaction of ‘side-arm’ pyridine rings (the closest C⋯C contact is 3.446(4) Å). Other types of π-stacking patterns are also observed in the 3-D packing framework of 1. The intermolecular packing in the cation of 2 is dominated by π⋯π stacking between ‘side-arm’ pyridine rings (the closest C⋯C contact is 3.604(7) Å), similar to that of 1.The ORTEP representations of cations of 1 and 2 are shown in ved in 1 . A simil4)2·6H2O using the same layering method as for 1 resulted in the isolation of X-ray quality crystals of 3. Complex 3 is isomorphic to 1 and also crystallizes in the monoclinic space group P21/c. A disordered solvent molecule in the asymmetric unit of 3 could not be well modelled and so it was treated as a diffuse contribution using PLATON/SQUEEZE.20 The monoprotonated cation of 3 is shown in 6]·2H2O and [Fe(pytpy)(pytpyH)][Fe(NCS)6]·MeCN.18 The bending conformation in cation of 3 is very close to that found in 1 (angle N4⋯Fe1⋯N8 is 168.04(6)°), resulting from the intermolecular hydrogen bond N4–H4⋯N8(i)  Å, H4⋯N8 = 1.80(3) Å, N4–H4⋯N8(i) = 167(8)°). Again, a similar 1-D hydrogen-bonded chain was found in 3. As referenced, homoleptic complexes 4–6 Cl2]n to perform catalytic hydroboration of styrene with pinacolborane (HBpin). The results of catalytic screening are summarized in tBu (1 mol%) were combined in THF, the hydroboration of styrene was realized in 5 min to give the branched (7) and linear alkylboronates (8) in 98% total yield (TOF = ∼47 000 h−1 for both regioisomers) and 9 : 1 regioselectivity  complex to enable alkene hydroboration with extremely high efficiency. Likewise, when 2 was used for the reaction under the same conditions, similar regioselectivity was found, while the yield dropped slightly to 80% (TOF = ∼38 000 h−1). Interestingly, going from CoII to FeII resulted in a notable loss of catalytic activity, as 3 catalysed the reaction with 17% yield in 5 min (8000 h−1), although the regioselectivity remained (entry 3). In addition, both complexes 4 and 5 are moderately active precatalysts for styrene hydroboration (entries 4 and 5), indicating the importance of both the pytpy ligand and BF4− counterions for high catalytic efficiency. According to these results, it is not surprising to see that complex 6, Fe(pytpy)2(PF6)2, is an inactive precatalyst (entry 6) under standard conditions.In order to evaluate whether new octahedral Coentry 1, , closelytBu as an activator for both high yield and regioselectivity, while other common activators such as NaOtBu, K2CO3 and NaHBEt3 all led to relatively poor results. Finally, control experiments were carried out to show that both cobalt complex and an activator are required to initiate the reaction (entries 18 and 19). It is also worth noting that when Co(BF4)2 was used instead complex 1 as a precatalyst, no hydroboration of styrene was detected (entry 20). Under the optimal conditions with 1/KOtBu as catalyst, the reaction did not proceed when it was exposed to the air, indicating that a highly air-sensitive reactive intermediate  must have formed and was responsible for the high-efficiency catalysis (entry 21). It is worth mentioning that to achieve the high TOF and regioselectivity, a single crystalline sample was not necessary. Instead, microcrystalline material of 1 could be synthesized in a gram-scale within 1–2 h by simply mixing concentrated, equimolar solutions of pytpy and Co(BF4)2·6H2O in CH2Cl2 and MeOH, respectively . Much lower yield and regioselectivity were observed when the reaction was conducted without a solvent. Toluene and dichloromethane were incompatible solvents for this reaction, as only trace amount of product has been detected for reactions in these solvents. Diethyl ether and dimethylsulfoxide are relatively better, yet still inferior to THF in terms of yield and selectivity. Next, we examined the reaction with different activators (entries 12–17). The results suggest the key role played by KO−1) was also found in the case of 4- trifluoromethylstyrene as a substrate. Styrene with an electron-donating 4-methoxy group proceeded well with good yield, while the regioselectivity dropped to 5 : 1 (7g). cis-Stilbene was found to be active substrate for hydroboration to afford 7h with appreciable isolated yield and high TOF. However, 1,1-disubstituted alkene shows poor reactivity under standard conditions (7i). Styrenes containing functional groups such as nitro, amino or pyridyl are inactive substrates, similar to the results reported previously using polymeric [Co(pytpy)Cl2]n as a precatalyst.8 In addition, aliphatic and cyclic alkenes are also reactive substrates, however, poor regioselectivity was obtained for 1-hexene (7n) and anti-Markovnikov selectivity was found for vinylcyclohexane (7o).To further demonstrate the effectiveness of 1 as a precatalyst for other alkene substrates, we employed the optimized conditions entry 1,  to examitBu system for styrene hydroboration by adding a second reducible substrate, which is so called a fast catalyst robustness screening.22 Thus, styrene was chosen to react with HBpin under standard conditions in the presence of equimolar additives as listed in 2]n.8Next, we explored the functional group tolerance of 1/KO2]n as precatalyst where very high TOFs could be achieved.14 In addition, hydrosilylation of styrene and phenylacetylene using phenylsilane as a Si–H source has been investigated. It was found that styrene has experienced effective hydrosilylation to afford the anti-Markovnikov product with complete regioselective control. However, hydrosilylation of phenylacetylene was accomplished within 16 h in 95% yield with poor regioselectivity (b/l = 4 : 5). The results indicate that both reactivity and regioselectivity are highly substrate-dependent when using 1/KOtBu as the catalyst.The ability of precatalyst 1 to promote hydroboration and hydrosilylation for several other substrates was further evaluated. The preliminary results are presented in 4]3 (M = Co or Fe). X-ray structural analysis reveals the conformational bending of these complexes in the solid state due to the formation of 1-D hydrogen-bonded chains, in comparison with the common complex, Co(pytpy)2(BF4)2. These new complexes (1–3) along with several known analogues (4–6) have been explored as precatalysts for the regioselective hydroboration of alkenes. The best catalytic system 1/KOtBu was found to furnish the hydroboration of styrene with a very high TOF of ∼47 000 h−1, comparable to the most efficient precatalyst [Co(pytpy)Cl2]n reported thus far. The method can be applied to a range of styrene derivatives for the regioselective synthesis of branched alkylboronates. However, some limitation of substrates has been disclosed. Catalyst robustness screening experiments provide further insights into the functional group tolerance of current catalyst. Preliminary experiments on relevant substrates for hydroboration and hydrosilylation catalysis were also conducted to extend its applicability for other conversions.In summary, we have synthesized and characterized homoleptic complexes of cobalt or iron with pytpy ligand. The direct reaction of pytpy and metal tetrafluoroborate resulted in the monoprotonation at the non-coordinating pyridyl moiety, namely [M(pytpy)(H-pytpy)], Cald. 679.1769). Anal. Calcd. for C40H29B3CoF12N8: C 51.05, H 3.11, N 11.91%. Found C 50.82, H 3.01, N 11.69. In a separate experiment, in a 100 mL flask, pytpy  was dissolved in MeOH/CH2Cl2 , to which a solution of Co(BF4)2·6H2O  in MeOH (20 mL) was added dropwise in 3 min. The reaction mixture was allowed to stay for an additional 1.5 h, red microcrystals had formed and were filtered to give bulk sample of 1 . Catalytic experiment confirmed its activity and efficiency for styrene hydroboration  in MeOH/CH2Cl2  in a 20 mL vial, to which was added a solution of CoCl2·6H2O  in MeOH (3 mL). The mixture was stirred for 15 min at ambient temperature and then a solution of NaBF4  in MeOH (2 mL) was added, the resulting precipitate was filtered, washed with MeOH and dried in vacuo. X-ray quality crystals were obtained by slow diffusion of diethyl ether into a concentrated solution of 2 in acetonitrile. Yield: 34.5 mg (81%). FT-IR : 3057m, 1619m, 1597s, 1538s, 1470s, 1407s, 1245s, 1053br, 896m, 823m, 789s, 732m. 1H NMR  δ 83.42 (bs), 48.26 (bs), 31.69 (bs), 30.80 (bs), 12.82 (bs), 10.43 (bs), 9.92 (bs) ppm. HR-MS (ESI positive): 679.1766 ([M-2(BF4−)], Cald. 679.1769). Anal. Calcd. for C40H28B2CoF8N8: C 56.31, H 3.31, N 13.13%. Found C 56.05, H 3.19, N 13.04.pytpy  was dissolved in MeOH/CH4)2·6H2O  was used. Brown plate-like crystals of 3 were collected in 85% yield (40 mg). FT-IR : 3603w, 3540w, 3075m, 1595s, 1537s, 1482m, 1467m, 1408s, 1286m, 1246m, 1053br, 895m, 821s, 788s, 755s, 733m. HR-MS (ESI positive): 676.1774 ([M-3(BF4−)-H+], Cald. 676.1780).The procedure is similar to that for 1, except that Fe and KOtBu  was dissolved in THF (0.5 mL) in a 3.8 mL glass vial equipped with a small stir bar. The mixture was stirred for 1 min. Alkenes (1.0 mmol) and pinacolborane  were then added. The reaction mixture was allowed to stir at room temperature for 5 min and then the reaction was quenched by exposing the reaction solution to air and adding CH2Cl2 (1 mL) to the solution. The crude reaction mixture was first analyzed by GC-MS to determine the total yields of desired alkylboronates and the ratio of the regioisomeric products by comparing the GC traces with those of authentic samples.8 The reaction mixture for several selected products was then evaporated under reduced pressure and the product was purified through a SiO2 column chromatography using ethyl acetate/hexane as an eluent. The pure alkylboronates of major products were characterized by 1H and 13C NMR spectroscopies.In a glovebox under N2 atmosphere, cobalt precatalyst 1  and KOtBu  was dissolved in THF (0.5 mL) in a 3.8 mL glass vial equipped with a stir bar. The mixture was stirred for 1 min. Styrene , additive (0.5 mmol) and pinacolborane  were then added sequentially. Hexamethylbenezene (25 mg) was added as an internal standard for GC analysis. The reaction mixture was allowed to stir at room temperature for 5 min. The reaction was quenched by exposing the reaction solution to air and adding CH2Cl2 (1 mL) to the solution. The crude product was analyzed by GC-MS to determine the GC yield and ratio of the regioisomeric products from styrene hydroboration. In each case, the identification of the corresponding boronate esters have been made by comparing their GC retention time and MS data with the authentic samples.8In a glovebox under NF2 with SHELXTL.23 All hydrogen atoms bound to carbon were placed in calculated positions and refined with a riding model [Uiso(H) = 1.2 − 1.5Ueq(C)]. The hydrogen atom bound to nitrogen was located on the difference map and refined freely or with an N–H distance restraint (for 3) [Uiso(H) = 1.2Ueq(N)].X-ray diffraction data were collected on a Bruker X8 Kappa Apex II diffractometer using Mo Kα radiation (for 1) or on a Bruker D8 VENTURE diffractometer using Cu Kα radiation (for 2 and 3). Crystal data, data collection and refinement parameters are summarized in Table S1 ESI. The strG. Z. and S. Z. conceived and supervised the project. H. Z., N. Z. and C. M. performed experimental studies. M. C. N. conducted the X-ray crystallographic analysis. All authors analysed the data and wrote the article.There are no conflicts to declare.RA-013-D3RA06113B-s001RA-013-D3RA06113B-s002


Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions . The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.Ambient fine particulate matter (PM Global fine particulate matter air pollution recently pivots from increase to decrease as inferred from satellite observations, driven by unprecedented exposure reduction in China and slowed exposure growth in South Asia, the Middle East and Africa. Global population exposure to PM2.5 is skewed by the broad co-existence of high population density and high PM2.5 concentration12, especially in South and East Asia. Nearly half (47%) of current global PM2.5-attributable deaths are due to traditional sources of air pollution exposure such as fossil and solid biofuel combustion13, which can be mitigated. High-income countries in Europe and North America have for decades regulated emissions from major air pollution sources16. Ambient PM2.5 levels across the US and Canada, for example, have decreased by 64% from 1981 to 201617. Meanwhile PM2.5 exposure deteriorated in Asia following rapid industrialization and urbanization, as inferred mainly by satellite observations22. Both the increasing PM2.5 exposure and rapid population growth in Asia have led to a steady increase of the global PM2.5-associated health burden over at least 1990-201024.Global ambient fine particulate matter  PM2.5 reductions (in excess of 15 μg/m3 within 8 years) as determined by nationwide ground monitoring, satellite-derived estimates and air quality modeling29. The consequent national health benefits  are substantial26. Additionally, evidence is emerging that the growth rate of the PM2.5 and total aerosol burden is slowing in other key regions including India30, North Africa32, the Middle East34, Central Africa31, and the Amazon36. There is need to comprehensively interpret, within a global context, how these regional changes affect global PM2.5 air pollution and its health impacts.Since the early 2010s, China implemented increasingly rigorous measures to alleviate its severe air pollution2.5 air pollution and health burdens have changed during 1998-2019, both globally and in 13 regions, with a quantification of regional contribution to changes in global exposure. We use a combination of high-resolution  satellite-derived global PM2.5 data, territory-, age- and disease-specific mortality data, and a concentration-response model linking PM2.5 outdoor concentrations to adverse health impacts. We present a comprehensive analysis revealing a timely reversal of global trends in PW PM2.5, largely driven by reductions in China and bolstered by mitigation efforts across different regions. The declining trend in recent years points toward a possible path of continuing and sustained improvements in global air quality and reductions in the associated health burdens.Here, we quantify how PW PM2.5 data gridded at ~1 km2 resolution over 1998-2019, as derived from satellite observations of aerosol optical depth, chemical transport modeling, and ground monitoring data28 (“Methods” section). Annual mean estimates are consistent  with ground-based observations  over 1998–2019. Before 2011  growth in PM2.5 exposure occurred widely across 87 territories (population 4.4 billion) mainly in Asia, Africa and South America, while only 15 territories (population 0.4 billion) exhibited significant reductions. Instead, during 2011–2019  exhibited significant positive trends during 2011–2019. Overall, at the global level there has been a reversal in the pre-2011 increase of PW PM2.5. The regional trends are largely connected with local air quality management policies in each region , China (1.5 ± 0.4 μg/m3/year), and other areas of South Asia (1.3 ± 0.3 μg/m3/year), and after 2011 by marked decreases in China (−2.4 ± 0.8 μg/m3/year), Eastern Europe (−0.6 ± 0.2 μg/m3/year), and Western Europe (−0.4 ± 0.1 μg/m3/year). Although these absolute trends are useful for comparing rates of change in pollution levels in each region, regional contributions to global net trends can be compared by also considering their relative population size.Consequently, global annual mean PW PMven Fig.  by marke2.5  of global PW PM2.5 (28.3 μg/m3) after accounting for the 21.4% of global population from China. This contribution increases moderately to a peak value of 30.2% of global PW PM2.5 in 2006, weakly declines over 2006-2013, and then decreases substantially to 20.1% in 2019. Concurrently, the contribution from India increases rapidly from 22.4% in 1998 to 31.3% in 2019, driven by rapid increases in PW PM2.5 over 1998-2011  to an increase  in normalized PW PM2.5 , and decreased by 4.2 μg/m3 from 2011 to 2019 (3.8 μg/m3 from China)  and China (0.24 ± 0.09 μg/m3/year) as the strongest contributors to the pre-2011 increase, followed by Sub-Saharan Africa (0.09 ± 0.04 μg/m3/year) and the rest of South Asia (0.08 ± 0.01 μg/m3/year). For the post-2011 decrease, China (−0.5 ± 0.1 μg/m3/year) alone is the dominant driver , Southeast Asia (Other) (-0.02 ± 0.01 μg/m3/year) and Eastern Europe (−0.02 ± 0.01 μg/m3/year). Seasonal trends are further discussed in Supplementary Section SGlobal mean PW PMna) Fig. . Linearl2.5 Fig.  identifiver Fig. , followe2.5 exposure trend in China, and that since 2015 India has become the leading contributor to global ambient PM2.5 exposure. The different trajectories of population-weighted trends in China and India offer examples of how different air quality management strategies can induce pronounced air pollution changes within a short (<10 year) period. Nonetheless, the slowing growth of PM2.5 exposure in tropical regions should not be ignored. If the pre-2011 trends of PM2.5 exposure in each region had been sustained through 2019, the global PW PM2.5 would have increased by 6.5 μg/m3. In reality, global PW PM2.5 is 10.7 μg/m3 less relative to this crude “business as usual” case, with 5.5 μg/m3 contributed by China, 2.9 μg/m3 reduced in South Asia (including India), 1.1 μg/m3 alleviated in the Middle East and Africa, and 0.4 μg/m3 attributable to the two Southeast Asian regions. Relative to the scenario in which their PW PM2.5 concentrations remain stable at the 2011 level, the US and Europe contribute collectively to a 0.5 μg/m3 reduction of global PW PM2.5 in 2019.The analysis of yearly changes Fig.  and line2.5 exposure, we find a determinant role of China as well as substantial contributions from tropical regions to the recent global reversal of trends in PM2.5 exposure.Overall, using the decomposed regional contribution to global PM2.5 pollution presented above? We derive for each territory long-term changes of annual PM2.5-attributable mortality using the PW PM2.5 estimates, a concentration-response function (GEMM), and age- and disease-specific mortality data from the Global Burden of Disease (GBD) 2019 study (Methods). Figure 2.5-attributable mortality during 1998–2019. Global annual attributable mortality increased steadily from 4.04 (95% CI: 2.59–5.33) million in 1998 to 5.70 (95% CI: 3.98–7.18) million in 2011. The pace slowed afterwards, peaking at 5.83 million (95% CI: 4.05–7.38) in 2015 and then decreasing slightly to 5.74 million (95% CI: 3.92–7.35) in 2019.What are the associated health impacts of the changes in PW PM2.5 exposure and three other relevant demographic factors in driving the changes in annual PM2.5-attributable deaths. We extend previous decomposition studies that focused primarily on earlier time periods or differences between representative years20 to present contemporary global and regional time series of annual contributions to the changing mortality, especially those from changing PM2.5 exposure. A recent stagnation of global PM2.5-attributable deaths is identified and attributed to specific drivers by this continuous decomposition. Globally, annual PM2.5-attributable deaths increased by 0.97 million (0.78 million increase in India and China) from 1998 to 2011 and decreased by 0.70 million (0.50 million reduction in China) afterwards, due to changing PM2.5 exposure  from China. The larger contribution of other regions  to reductions in mortality than to reductions in exposure reflect the increasing benefits of PW PM2.5 reduction at low PM2.5 concentrations and among older populations, as discussed in the next section. Western Europe (0.30 million), Eastern Europe (0.23 million), and the US (0.13 million) have the next highest exposure-driven reduced deaths after 2011 , which are stronger than their contributions to the post-2011 reduction of global exposure  to quantify the role of PM2.5-attributable mortality (Supplementary Section S2.5-attributable deaths as accumulated since 2011 (1.20 million if benchmarked to 2013). The national reductions in China of annual PM2.5-attributable mortality and the determinant role of exposure reductions are consistent with findings of Geng et al.26 for 2012–2017. Here we further illustrate their driving role in the stagnation of growth in global PM2.5-attributable mortality. US and Europe contribute to an additional reduction of 0.61 million PM2.5-attributable deaths. Oppositely, strong net growth in annual mortality is found for India and the rest of South Asia (1.38 million excess deaths after 2011), followed by North Africa and the Middle East, Indonesia & Malaysia, and Latin America, due to insufficient reductions in PM2.5 exposure to counteract the increased mortality from population growth and aging.In addition to exposure, three other factors also make sizable contributions to the changes in global PM2.5 exposure after 2011 critically offsets the effects on PM2.5-attributable mortality of a growing and aging global population, plateauing the previous growth in estimated global annual mortality caused by PM2.5 exposure.Overall, our analysis reveals that globally, the slowing growth in PM2.5, and how has this sensitivity changed as air quality and demography both evolve over time? Such insight from a global and long-term perspective is lacking while is particularly important to customize strategic mitigation policies at various levels of PM2.5 exposure. We derive a numeric representation of the changes in annual PM2.5-attributable deaths per 1 μg/m3 marginal reduction in PM2.5 exposure for each region and year, using a finite-difference approach  increasing marginal health benefits since 2011  with PM2.5 exposure (dotted) in all of 13 regions, i.e., increasing PW PM2.5 leads to decreasing sensitivity of its attributable mortality and vice versa. These globally universal strong correlations were driven by the increasing sensitivity of the overall population attributable fraction (PAF) to PW PM2.5 changes at lower PW PM2.5 levels , mitigation of PW PM2.5 will continuously increase its health benefits at lower concentrations, and among older populations in the future.Globally, the per capita (for ages <5 or ≥25) health benefits per 1 μg/mion Fig. , black r011 Fig. , because011 Fig.  were invels Fig. 2,4,38. S2.5 air pollution, and quantify regional contributions to this long-term change, due to a > 15-year sustained effort to develop long-term high-resolution seamless observation-based global PM2.5 data. The estimates bridge the gap between ground monitoring of spatially discrete surface PM2.5 measurements and satellite observations of spatially continuous columnar aerosol optical depth (AOD), by leveraging state-of-science model representation of the PM2.5-AOD relationship and a powerful statistical tool to spatially propagate measurement information42. These satellite-derived PM2.5 estimates offer valuable data for vast global regions and population without regular monitoring of PM2.5 (over a billion persons in more than 100 territories worldwide43). Many poorly monitored areas  exhibit dynamically varying changes in PM2.5 exposure  PM2.5 exposure and population, highlighting the significance and urgency of mitigating PM2.5 pollution over populous regions from another unique perspective. China was and India is now associated with >30% of global PM2.5 exposure, evidence of the critical roles played by population size and national air quality management strategies. The normalized PW PM2.5 metric is applicable to other underexploited studies, e.g., ranking the contribution to global PM2.5 pollution by populous cities, or comparison between rural vs. urban regions. Besides the global and regional insights in this paper, data of annual mean PM2.5 exposure, PM2.5-attributable mortality and marginal benefits, and their decompositions into drivers for all 204 territories are provided in an open-access data repository (see “Data Availability” Section) to support further analysis at smaller scales.The normalized PW PM2.5 exposure worldwide: (1) the recent PW PM2.5 reductions globally and in many territories did not completely counteract the increase in global PM2.5-attributable mortality due to the growth and aging of population . Emerging studies47 indicated even stronger and steeper concentration-response functions at low PW PM2.5 levels (<10 μg/m3), further strengthening the greater benefits at reduced PM2.5 exposure. However, future mitigation of PM2.5 air pollution at lower PM2.5 concentrations will be increasingly challenging, due to growing importance of aerosols arising from climate change 48, and as more readily mitigable anthropogenic sources are reduced50.Two important findings in this comprehensive study support the need for additional future measures to further mitigate PMon Figs.  and 7. T2.5 exposure estimates were found regionally variable while no systematic biases were identified  available at monthly and annual temporal resolution over 1998-2019 and 0.01° × 0.01° (~1 × 1 km2) spatial resolution as described by van Donkelaar et al.28. Briefly, daily retrievals of columnar aerosol optical depth (AOD) were obtained from seven algorithms and four satellite instruments . Each of these satellite AOD products and a chemical transport model (GEOS-Chem) simulation were compared with ground-based measurements of AOD from the global Aerosol Robotic Network (AERONET) to infer their biases and uncertainties. These daily AODs were then monthly averaged and combined using spatially resolved weights based on evaluation metrics, yielding best estimates of global monthly AOD on 0.01° × 0.01° grids through 1998-2019. The merged satellite observations comprise 91-96% of global population-weighted AOD for every month, with simulated GEOS-Chem AOD primarily used to fill the remaining grid cells (mostly snow covered) and produce the seamless AOD surface.We use global satellite-derived PM2.5 using the GEOS-Chem simulation to account for the spatial and temporal variation of their relationship (η). η describes the state-of-science model representation of the processes affecting the AOD to PM2.5 relation including aerosol chemical composition, sampling time, hygroscopicity and vertical profile. These geophysical PM2.5 estimates explain about 80% of the variance in globally measured annual mean PM2.5 at surface monitoring sites collected by the World Health Organization (WHO)51.The best estimate AODs were then related to monthly PM2.5 estimates, ground-based observations of PM2.5 were collected and compiled from over 11,000 sites worldwide  of global annual PM2.5 measurements collected by the WHO (4409 sites) to 94%28. We use these hybrid estimates in this study.To further improve the representation and fidelity of the PM2) and root mean square difference  in Eq. We mainly use coefficient of determination R and root2.5-attributable deaths are considered, including mortality from adult (25 years and older) with ischemic heart disease (IHD), Stroke, chronic obstructive pulmonary disease (COPD), lung cancer (LC), type-2 diabetes mellitus (DM), and childhood and adult (under 5 years and 25 years and older) acute lower respiratory infections (LRI). Deaths due to other non-communicable diseases are not considered in this study, which account for ~20% of total global PM2.5-attributable deaths2.We use age- and disease-specific number of deaths  in 204 global countries and territories  during 1998-2019, from the Global Burden of Disease (GBD) 2019 study. Six relevant diseases that contribute to PMP) counts for the 204 global territories are available for each year during 1998–2019 in the GBD 2019 dataset, which are combined with the age- and disease-specific deaths (D) to derive mortality rate (MR) and mean ages (Agem) for a region using Eqs. (Age-specific population (ing Eqs.  and 4)..P) countThe GBD population data for each territory contains 96 age bins, with population number for ages with <1, 1, 2,…, 94, and 95+ years. We assign an age of 0 to the <1 year group, and of 95 to the 95+ years group.Spatially resolved population estimates at 30-s resolution (~1 km) are from the Gridded Population of the World (GPW v4) database. GPW is available every five years for 2000–2020. For each year during 1998–2019, we extract the GPW population in the closest year and regridded the data into regular 0.01° × 0.01° grids. These regridded population distributions are further scaled for each of the 204 territories to match their total populations for that year in the GBD 2019 population data.P) are used with the PM2.5 estimates (both 0.01° × 0.01°) to derive PW PM2.5 exposure (E) for each territory (region) and year based on the gridded PM2.5 (PMl) from each grid cell location (l).The high-resolution GPW population estimates . We repeat the random sampling m times, where m collocated pairs of observed and estimated PW PM2.5, which are then used to calculate the NRMSD in Fig. We evaluate the PW PM2.5 can be calculated as in Eq. It can be inferred from Eq. 2.5 can be expressed as the sum of normalized PW PM2.5 (NE).Therefore, contributions from each region to the global PW PM2.5 of a region thus can be from increases in either its PM2.5 exposure or its population share of the world.Increases in normalized PW PM2.5 time series for each territory , its 95% confidence interval (CI), as well as the p-value (two-tailed Student’s t test). In our analysis, slopes with p-value of <0.05 are associated with their 95% CIs not enveloping the zero point and vice versa, therefore the p-values and 95% CIs have equivalent indications on the significance of derived trends. These multi-year slopes are less sensitive to abnormal years, relative to differences between two benchmark years. We also confirmed that the derived trends in PW PM2.5 of the 204 territories are highly consistent (R2 > 0.9) with those from non-parametric  trend estimation approach.We estimate linear trends in the  PW PMory Fig. , based o2.5-attributable deaths (DPM) based on Eqs. 2.5-attri2.5 exposure (E) is adopted for each age group and disease from the Global Exposure Mortality Model (GEMM)2, to derive the population attributable fraction (PAF) as in Eq. 2.5, and are applied after dividing by the RR at Theoretical Minimum Risk Exposure Level, consistent with McDuffie et al.13.In this study, the relative risk (RR) as the concentration response function (CRF) of PM54. We follow the conventional approach13 to calculate 95% CIs of PM2.5-attributable deaths, by applying the upper and lower bounds in the 95% CIs of the CRFs to Eq. 2.5  CRFs used by the GBD Collaborationa) for a territory or region that accounts for all relevant ages and diseases , MRi,j,k is mortality rate of disease j within age i for territory k, and PAF is a monotonic function of PM2.5 exposure (E). For two neighboring years (y and y + 1), the changes in annual deaths can be attributed to changes in these four factors.For each territory  DPM Eq.  can be e26, the differences in DPM between year and year+1 can be expanded as fractional differences by gradually varying the four factors from year to year+1:Following Geng et al.The superscript 1 indicate that this is 1 of 24 (=4 × 3 × 2 × 1) possible pathways  that the fractional differences due to each factor can be derived. The final attributed differences due to population change is the ensemble mean of the 24 scenarios:The attribution to the other three factors is similarly performed.We apply this method to every pair of neighboring years to minimize the effects of nonlinearity. These attributed differences are then summed across diseases, ages, and territories, and finally all accumulated referenced to 2011, to derive the regional attributions (e.g., Fig. 2.5 exposure.The attribution of changes in Supplementary Information forPeer Review File


Glucosamine and N-acetyl-glucosamine (NAG) are amino sugars found in human extracellular matrix with previously described anti-inflammatory effects. Despite mixed results from clinical studies, these molecules have been used extensively in supplements.We investigated the anti-inflammatory properties of two synthesized derivatives of N-acetyl-glucosamine (NAG), bi-deoxy-N-acetyl-glucosamine (BNAG) 1 and 2.Using mouse macrophage RAW 264.7 cells with lipopolysaccharide (LPS) to induce inflammation, the effects of NAG, BNAG 1, and BNAG 2 on the expression of IL-6, IL-1β, inducible nitric oxide synthase (iNOS) and COX-2 were studied using ELISA, Western blot and quantitative RT-PCR. Cell toxicity and nitric oxide (NO) production were evaluated using WST-1 assay and the Griess reagent, respectively.Among the three tested compounds, BNAG1 shows the highest inhibition of iNOS, IL-6, TNF α and IL-1β expression and NO production. All three tested compounds show slight inhibition on cell proliferation of RAW 264.7 cells, except that BNAG1 displays a remarkable toxicity at the tested maximum dose of 5 mM.BNAG 1 and 2 exhibit notable anti-inflammatory effects compared to the parent NAG molecule. Understd-glucose, along with its acetylated product N-acetyl glucosamine (NAG) are naturally occurring amino sugars found in human extracellular matrix and the synthesis of glycosylated proteins and lipids +. 1H NMR : δ 7.55 , 6.24 , 4.89 , 4.54 , 3.98–3.95 , 3.66–3.62 , 3.45–3.40 , 1.87–1.83 , 1.79 , 1.13–1.07  1.03 . The detailed synthesis of BNAG1 and BNAG2 will be reported in another article.BNAG1 (purity>95%) was synthesized from NAG through a 7-step procedure. For BNAG2 synthesis, the starting material was NAG  with protected 1, 4, 6-hydroxyl groups, and BNAG2 (purity>95%) was obtained after 6 chemical reactions. BNAG1: LC-MS: 190.20 [M+1]2.2Mouse RAW264.7 macrophage cell line  was maintained in a DMEM culture medium  containing 10% FBS  and 1% penicillin-streptomycin-glutamine mix (100X) . For cell treatment, BNAG1 or BNAG2, or NAG was added to the culture for 0.5 h before the addition of 100 ng/mL LPS  and then cells were treated with LPS and BNAG1 or BNAG2, or NAG for 24 h. In addition, 96 well plates were used for tests for cell proliferation, nitrite production and IL6 and TNF α level, while 24 well plates and 6 well plates for gene expression and Western blot assays. The initial cell numbers of approximately 20,000, 100,000 and 500,000 per well were seeded on 96, 24 and 6 well plates, respectively. Furthermore, the doses of each drug varied with different tests and were described below.2.3RAW264.7 cells were seeded on a 96 well plate, followed by treated with BNAG1, BNAG2 and NAG at doses of 0–5 mM for 24 h. The cell number in each well was counted using the WST-1 kit  according to the manufacturer's instructions. After removal of the supernatant, cells were incubated with 150 μL of the fresh medium along with 15 μL of the WST-1 reagent/each well for 3 h in dark at 37 °C. The optical density (OD) was determined at 450 nm on a microplate reader . The inh2.4RAW264.7 cells were seeded on a 96 well plate, followed by treated with BNAG1, BNAG2 and NAG at doses of 0–0.1 mM for 24 h. Nitrite in the culture medium was detected by a commercial Griess reagent kit , according to the manufacturer's manual. This kit utilizes the chemical reaction involving sulfanilamide and N-1-napthylethylenediamine dihydrochloride (NED) under acidic (phosphoric acid) conditions. Briefly, all experimental samples and standards containing the dilution series for the nitrite standard curve were mixed with 50 μL of the sulfanilamide solution ((1% sulfanilamide in 5% phosphoric acid) and incubated for 10 min at room temperature without light in a 96-well plate. Then 50 μL of the NED Solution (0.1% NED in water) was added to each well and incubated for 10 min again at room temperature without light. The OD value in each well at 530 nm of the absorption wavelength was immediately determined on a microplate reader . Meanwhi2.5Cells were seeded on a 96-well plate and the same treatments as nitrite test were performed. The amounts of IL-6 and TNF α in the culture medium from different treatments were measured by the commercial mouse IL-6 and TNF α ELISA kits , following the instructions provided by the manufacturer . Meanwhi2.6Cells were seeded on a 24-well plate and various treatments were performed with 0.5 mM of BNAG1, BNAG2, and NAG, respectively. Cellular RNA was purified using an RNeasy kit , and the total RNA yield and purity were estimated using microplate reader at 260/280 nm. Synthesis of cDNA and the quantitative PCR (qPCR) was carried out using the iscript™ cDNA synthesis kit  and the iQ™ SYBR Green Supermix kit , respectively. The qPCR conditions were for pre-denaturation (95 °C for 5 min), denaturation (95 °C for 1 min), annealing   containing a protease inhibitor cocktail  and 1 mM phenylmethylsulfonyl fluoride . Then the protein concentration was determined using the Bradford protein assay kit . SDS-polyacrylamide gel electrophoresis, protein transfer to nitrocellulose membranes , incubation of membranes with primary antibodies against mouse iNOS and Cox-2 , and horseradish peroxidase (HRP)-conjugated secondary antibody , and visualization of protein bands were performed according to the procedures described previously . β-actin2.8All data was reported as mean ± SD. A one-way ANOVA test with multiple comparisons was performed. NT group in 33.1The WST-1 assay showed that BNAG1 was the most toxic against mouse macrophage cell line RAW264.7 cells among the tested three drugs at the dose range of 0–5 mM. BNAG1 at all four doses , BNAG2 at 0.5 mM and 5 mM, and NAG at 5 mM could significantly inhibit cell proliferation . Notably, the inhibition rate for each drug at every dose was less than 10% except that the inhibition rate was 32.6% for BNAG1 at 5 mM , indicat3.2The ELISA tests showed that all three drugs tested  could significantly reduce IL-6 production in LPS-activated mouse RAW264.7 macrophages, while BNAG1 was most effective among them (n = 8). In addition, only BNAG1 at 0.01 mM and 0.1 mM was able to significantly reduce TNF α production .3.3The Western blot assay revealed that NAG > BNAG2>BNAG1 for inhibition of Cox-2 expression and BNAG1>BNAG2>NAG for inhibition of iNOS expression, respectively .Fig. 4Th3.4The RT-PCR showed that BNAG1 (0.5 mM) could inhibit mRNA expression of both iNOS and IL-1β , while NAG (0.5 mM) could inhibit IL-1β  but not iNOS mRNA expression . BNAG2 (0.5 mM) didn't inhibit either iNOS or IL-1β mRNA expression  .3.5The nitrite test showed that all three drugs tested  could significantly inhibit NO production (n = 6), while BNAG1 was most effective among them. Notably, NO production in the presence of BNAG1 at 0.1 mM significantly lower than either BNAG2 or NAG at the same concentration, reflected by OD values of 0.141 ± 0.012 for BNAG1, 0.170 ± 0.010 for BNAG2 and 0.162 ± 0.009 for NAG, respectively .4In this study, we examined two newly designed compounds, BNAG 1 and 2, which are molecules obtained from modifications of NAG. Specifically, BNAG 1 was created from the removal of the hydroxyl groups at C1 and C3 of NAG. BNAG 2, on the other hand, was created from the removal of the hydroxyl groups at C4 and C6 of NAG . We compThere has long been a recognition that part of glucosamine metabolism includes its conversion to UDP-N-acetyl-glucosamine  and thatIL-1β, TNF α and IL-6 are extensively studied proinflammatory cytokines within the human immune system. They serve important physiological roles. However, they have also been implicated in a variety of pathologies with prominent inflammation components ,22. In tOur evaluation of COX-2, iNOS, and NO, on the other hand, was designed to more closely investigate the possible downstream pathways involving BNAG 1 and BNAG 2. We already described earlier that glucosamine and its derivatives are believed to interfere with the inflammatory pathway in a multifaceted fashion, including through the NFkB pathway and the iNOS pathway . InteresWhile we have been able to elucidate the anti-inflammatory effects of two new molecules, BNAG 1 and 2, there are several limitations to the study as well as areas needing further clarification. First, BNAG 1, the more efficacious molecule of the two, has been associated with cytotoxicity. Since BNAG 1's toxicity is toward RAW 264.7 mouse macrophages, a proinflammatory population of cells, it might contribute to BNAG 1's superior anti-inflammatory response. While we have proposed a possible explanation for this effect, namely the inhibition of hexokinase, further experiments must be pursued to confirm or deny this claim. The incorporation of BNAG 1 in a proinflammatory animal model would also be helpful to evaluate the effect of this cytotoxicity on inflammatory cells in vivo. Second, we found that the removal of hydroxyl groups from the original NAG molecule resulted in its reduced affinity to the NFkB but increased activity in the iNOS pathway. However, it is not clear why this is the case. An understanding of this would be useful to guide future modification of glucosamine and its derivatives. Third, our study is entirely in vitro. Inflammation was evaluated non-specifically using common markers including IL-1β and IL-6. This study would benefit from further characterization in an inflammatory animal model. Evaluation in vivo would provide the necessary information on whether BNAGs are viable strategies to target inflammation in a variety of pathologies, including but not restricted to osteoarthritis, rheumatoid arthritis, inflammatory bowel disease, and cancer.5In summary, we have been successful in showing that BNAG 1 and BNAG 2 possess the significant capacity to reduce expression in a variety of classic and specific inflammatory markers, superior to NAG (except for COX2). BNAG 1 specifically showed the strongest suppression of the expression or production of IL-6, IL-1β, TNF α, iNOS and NO. BNAG1 and BNAG 2 might be good candidates for a novel anti-inflammatory drug, further studies are recommended.Quang Le: Performed the experiments; Wrote the paper.Zhichang Zhang; Daniel Sun: Performed the experiments.Quanjun Cui; Ameer E. Hassan: Conceived and designed the experiments; Analyzed and interpreted the data.Xinlin Yang: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.University of Virginia [104473-XY3C-DR00539-40916].This work was supported by an unrestricted grant from Dr. Ameer E. Hassan to the gift fund of the Department of Orthopaedic Surgery,


Besides imparting magnetization and magnetostriction, the incorporation of CoFe2O4 particles decreased the ultimate tensile strength and increased the fracture elongation of the CNF–CoFe2O4 composite paper. CNF was responsible for the tensile properties of CNF–CoFe2O4 composite paper. Consequently, the magnetic and magnetostrictive properties and tensile properties of CNF–CoFe2O4 composite paper can be controlled by changing the mixture ratio of CNF and CoFe2O4 particles.Polymers are often combined with magnetostrictive materials to enhance their toughness. This study reports a cellulose nanofibril (CNF)-based composite paper containing dispersed CoFe When the ambient energy supply is limited, piezoelectric-energy harvesting devices generate sufficient power for targeted devices such as Internet of Things sensors4. For this purpose, piezoelectric materials, composites, and devices have been actively researched11 and their vibration-energy harvesting performances have been evaluated.To alleviate the global energy crisis and environmental pollution, many researchers are exploring alternative energy technologies that harvest energy from the ambient environment 12. The magnetostrictive effect was first described by James Prescott Joule in 184213. He reported that iron, a ferromagnetic material, changes its dimension in response to a magnetic field. Since that time, researchers have developed various magnetostrictive materials such as Tb–Dy–Fe alloys (terfenol-D), Fe–Ga alloys , Fe–Co alloys, and CoFe2O4 18. Magnetostrictive materials, composites, and devices are also attracting attention in the energy harvesting field24. Terfenol-D and galfenol are well-known giant magnetostrictive alloys showing good magnetostrictive properties at room temperature, but are brittle and expensive16.Magnetostrictive materials can deform under an external magnetic field25. Under an external magnetic field, the magnetostrictive particles deform and exert a force on the polymer matrix, deforming the whole composite. Equilibrium is achieved by balancing the stresses generated in the magnetostrictive particles and the polymer matrix, resulting in overall deformation of the MPC. MPCs are potentially applicable to current and stress sensing, vibration damping, actuation, health monitoring, and biomedical applications. In addition, they are easier to manufacture to the required geometry than the above-mentioned giant magnetostrictive alloys. Previous studies on MPCs have reported terfenol-D particles26 and galfenol particles27 dispersed through an epoxy resin matrix , Fe–Co alloy particles dispersed through a polyurethane matrix (Fe–Co/PU composites)28 and various others30. Positive magnetostriction values of 1600, 360, and 70 ppm have been reported in terfenol-D/epoxy, galfenol/epoxy, and Fe–Co/PU, respectively. However, MPCs with negative magnetostrictive effect have only been investigated to a small degree. Nersessian et al.31 reported saturation magnetostrictions of − 24 and − 28 ppm in hollow and solid nickel composites, respectively. Similarly, Ren et al.32 reported negative magnetostriction in polymer-bonded Sm0.88Dy0.12Fe1.93 pseudo-1-3 composites.To overcome the brittleness of magnetostrictive materials, many researchers have dispersed magnetostrictive particles through a polymer matrix, forming magnetostrictive polymer composites (MPCs)33 because paper is low cost (~ 0.005 $/m2), biocompatible, environmentally friendly, 100% recyclable, and more stretchable than other polymer-based flexible devices34. Cellulose fiber is inexpensive, bio-based, biodegradable, non-hazardous, recyclable, and low-density35. Cellulose nanofibrils (CNFs) in particular show outstanding strength, stiffness, and toughness36 and are expected to be utilized as reinforcing fibers43.Recently, paper- and cellulose-based devices have gained increasing attention44 showed that nanonetworks created from CNFs can form superstructures with virtually any kind of particle because of supramolecular cohesion provided by the fibrils. This cohesion was shown to be a result of the high aspect ratio of the fibrils. Yermakov et al.45 fabricated magnetostrictive nanocellulose membranes by embedding terfenol-D particles into CNFs. After evaluating the magnetostrictive properties of the membranes, they found that various orientations of the terfenol-D particles were induced in the membranes, and that particles with in-plane alignment showed the strongest magnetostrictive effect. Kim et al.46 fabricated a magnetostrictive actuator string which could respond to an external magnetic field, by combining magnetic nanoparticles of Fe2O3 in a CNF matrix. However, there are no publications where magnetostrictive composites were made by combining CNFs and CoFe2O4. Antonel et al.47 produced a CoFe2O4-poly(aniline) composite by embedding CoFe2O4 nanoparticles inside a poly(aniline) polymer matrix. They showed that due to particle-polymer interactions, by varying the particle-polymer ratio one can modulate the magnetic behavior of the material.Mattos et al.2O4 particles were dispersed through CNF to form CNF–CoFe2O4 composite papers. This paper reports the magnetic, magnetostrictive, and tensile properties of the papers. The microstructures of the CNF–CoFe2O4 composite papers were observed using a scanning electron microscope (SEM) and an X-ray diffraction (XRD) system.In the present study, CoFe2O4 composite papers. The starting materials were CoFe2O4 particles  and a 2 wt% CNF slurry . The CoFe2O4 particle size distribution was measured by a laser diffraction particle size analyzer . The CoFe2O4 particles and CNF slurry were manually mixed for 5 min at room temperature by hand-mixing. Using different weight ratios of CoFe2O4 particles: CNF slurry, 3 solutions were prepared: 5:95, 20:80 and 35:65, total weight 20 g. The solutions were sandwiched between two mesh sheets sized 100 2. The samples were compressed and dehydrated under an ultra-compact manual hydraulic heating press  operated at 120 °C for 30 s. After peeling off the mesh sheets, the dehydrated CNF–CoFe2O4 composite papers were further compressed and dried using a 2500 g weight for 24 h. The circular CoFe2O4 plate (3) was then consolidated by spark plasma sintering , under a compressive stress of 20 MPa at 1000 °C for 10 min in a vacuum. A reference CoFe2O4 plate was cut to a size of 10 3 and reserved for further measurements.Figure 2O4 composite papers and CoFe2O4 sintered plate, the lengths and thicknesses of the samples were measured using an electronic digital caliper  and a digital thickness gauge , respectively, and the weights of the samples were measured on a digital scale .To obtain the densities of the CoFe2O4 composite papers and CoFe2O4 particles were investigated in a multipurpose XRD system . The XRD patterns were obtained under CuK2O4 composite papers were observed using a field emission SEM (FE-SEM) , with an acceleration voltage of 5 kV and a working distance of 10 mm. In preparation for FE-SEM, the surfaces of the CNF–CoFe2O4 composite papers were sputtered for 30 s using an ion sputtering device  with a discharge current of 15 mA at 15 Pa to provide electrical conductivity to the composite papers. Furthermore, the FE-SEM was equipped with an energy dispersed X-ray spectrometer (EDX) for measuring the carbon (C), oxygen (O), cobalt (Co) and iron (Fe) concentrations in the CNF–CoFe2O4 composite papers. The EDX was operated at an acceleration voltage of 5 kV and a working distance of 15 mm.The microstructures of the CNF–CoFe2O4 composite papers were evaluated using a vibrating sample magnetometer (VSM)  calibrated to 4.931 emu. The VSM was calibrated on a pure nickel plate sized 10 3. The applied magnetic field range was ± 759 kA/m. The magnetostrictive properties of the CNF–CoFe2O4 composite papers were evaluated under the VSM electromagnets. The electromagnets were separated by 50 mm. The applied magnetic field was measured with a gauss meter . The strains of the CNF–CoFe2O4 composite papers were measured by an orthogonal strain gauge  positioned on the sample surface under an applied magnetic field range of ± 733 kA/m. The data were collected by data loggers 28.The magnetic properties of the CNF–CoFe2O4 composite paper specimens sized 30 2 were investigated on a compact tabletop tester  with a 500-N load cell . The tensile grips were separated by 15 mm. #600-grit sandpaper was inserted between the CNF–CoFe2O4 composite paper and the tensile grips to prevent slipping.The tensile properties of CNF–CoFe2O4 composite paper. In rectangular Cartesian coordinates xi , the constitutive equations of the heterogeneous magnetostrictive composite material are given by Eqs.  is replaced with p, q . Equations  acts along the x3-direction (the easy magnetization axis of the composite paper). The magneto-mechanical coupling factor is given by Eq. , respectively.To demonstrate the effective properties of the CNF–CoFen by Eq. 5\documennto Eqs. :6\docume2O4 (2O4 composite papers were 0.7983 g/cm3 (sample from 5:95 solution), 1.1967 g/cm3 (sample from 20:80 solution), and 1.5058 g/cm3 (sample from 35:65 solution). Assuming that all water was evaporated from the dried CNF–CoFe2O4 composite paper and taking the theoretical densities of CoFe2O4 and cellulose , the volume fractions of CoFe2O4 particles in samples made from the 5:95, 20:80, and 35:65 solutions were calculated as 10.9, 21.0, and 27.5 vol%, respectively, in the final CNF–CoFe2O4 composite papers  of the stress–elongation curves of the CNF–CoFe2O4 composite papers were calculated for the 10.9, 21.0, and 27.5 vol% specimens, and they were determined as 0.523, 0.269, and 0.195 GPa, respectively (see Table 2O4 composite papers. The CNF volume fractions in the 10.9, 21.0, and 27.5 vol% composite papers were 14.7, 5.9, and 3.6 vol%, respectively. The UTS of the CNF–CoFe2O4 composite paper was increased by the addition of CNFs. However, the addition of CoFe2O4 particles decreased the UTS, i.e., CNF was responsible for the tensile properties of CNF–CoFe2O4 composite papers. Therefore, the magnetic and magnetostrictive properties and tensile properties of CNF–CoFe2O4 composite papers can be controlled by changing the mixture ratio of CNF and CoFe2O4 particles. The apparent 2O4 composite paper were 5.45 −16, 9.37 −15, and 2.36 −14, respectively (see Table 2O4 implies that the CNF–CoFe2O4 composite paper is a promising candidate for energy harvesting applications.Figure 2O4 composite papers with different volume fractions of CoFe2O4. The XRD and EDX analyses revealed that CoFe2O4 remained stable during the fabrication process. The SEM images confirmed that CoFe2O4 particles were dispersed through the CNF matrix but were sometimes agglomerated. The CoFe2O4 particles imparted magnetization to the CNF paper and the maximum magnetization of the CNF–CoFe2O4 composite paper was a linearly increasing function of CoFe2O4 content. The magnetostriction of the CNF–CoFe2O4 composite paper was negative and positive in the directions parallel and perpendicular to the magnetic field, respectively. The apparent effective Young’s modulus and UTS of the CNF–CoFe2O4 composite paper decreased with increasing CoFe2O4. This was because increased amount of CoFe2O4 particles decreased the CNF volume fraction of the composite paper. Conclusively, the CNF was responsible for the tensile properties of CNF–CoFe2O4 composite paper. Therefore, the magnetic and magnetostrictive properties and tensile properties of CNF–CoFe2O4 composite paper can be controlled by changing the mixture ratio of CNF and CoFe2O4 particles. Overall, the CoFe2O4 additives impart magnetic and magnetostrictive properties to the CNF paper and may increase its toughness in exchange for decreasing its tensile properties. The magneto-mechanical coupling factor of the paper was improved by adding CoFe2O4 particles; therefore, the CNF–CoFe2O4 composite paper is expected to be available for energy harvesting applications.This study evaluated the magnetic, magnetostrictive, and tensile properties of CNF–CoFe