We prove that a thin layer of surface amorphous structure and residual lattice strain are formed in Si by high energy ball-milling treatment. Those manipulations improve Li intercalation kinetics and thus enabling a capacity fading of less than 10% (from 1860 to 1650 mAhg−1) for Si negative electrode in 50 cycles. Of utmost importance, this study discloses a robust assessment for revealing mechanism on amorphous and strain related silicide formation and predicting cyclability of negative electrode by quantitative phase evolution rate of FSN additive in LIB.Artificial graphite (FSN) additive is employed as internal structural label for projecting cyclability of Si material native electrode in a mass ratio of Si/FSN = 1.0 in Li ion battery (LIB). Results of  However, as compared to existing negative electrode materials, the lithium diffusion coefficient and intrinsic conductivity of Si are relatively low hindering its steps into the market3. Furthermore, Si possesses a volume expansion up to 400% due to a solid-state alloying reaction in lithiation (charge) process4. It induces extensive stress between residual Si and Li interacted domain, therefore, leading to pulverization of silicon powder in negative electrode and instability of the solid electrolyte interphase (SEI) layer6. Those characteristics pill off Si powders from current collector and increase electrical contact resistance in electrode9. Meanwhile, crashed Si powders exposes fresh surface to electrolyte thus causing overgrowth of solid electrolyte interface (ESI). In past decades, improvements in the cyclability and rate capability of silicon negative electrodes have been demonstrated by reducing particle size to the nanoscale10, seeking conductive coatings11 or conductive networks12, providing void space to accommodate volume expansion13, and introducing highly elastic phases to mitigate the deleterious effect of large volume changes15. Regardless of attempts been utilized, cyclability of LIB has been partially improved and is still far from criteria of the commercialization.With raising demand of power consumption in transportation platforms, portable electronics, as well as high energy density modulation and storage system, high capacity materials are evitable issues for development of Li ion battery (LIB). With such a consideration, silicon based materials are no doubt the next generation negative electrode material in LIB due to its highest theoretical capacity among studied materialsoperando LIB devices. In this study, artificial conducting graphite (FSN) is employed as internal structural label for verifying lithiation and delithiation affinity of active materials operando LIB. By cross-referencing results of density functional theory (DFT) calculation and operando XRD, effects of high energy milling on silicide formation and lithiation/delithiation affinity of Si materials are quantitatively revealed by changes of crystal structure of FSN as a function of applied potential of LIB. Notice that, priority of phase evolution between Si and graphite domains are dominated by their affinity to lithiation. Consequently, obtained results not only disclose the Li ion insertion/extraction behaviours in negative electrode but provide a new class of internal standard method for projecting quality and cyclability of silicon-based materials in formation stage of LIB.With safety and cost considerations, capacity, failure mode, and cyclability are essential inspections for commercialization of LIB. In these inspections, cyclability takes the longest time and largest resources, therefore, are long-standing issue for the slow development of LIB. Internal standard could be an efficient label as comparison for redox responses of active materials in electrochemical devices. Such a technique is conventionally employed by adding stable materials with known structure and chemical state in target sample for calibrating spatial or chemical specifications upon physical inspections (including X-ray absorption spectroscopy (XAS), X-ray diffraction (XRD), and X-ray photoemission (XPS)) and process development in semiconductor. However, internal standard is rarely being used due to the insufficient information on kinetics of structural evolutions for active materials P and SiH+W is revealed by using XRD analysis. Structure parameters are quantitatively determined by Rietveld refinement on their diffraction patterns  Å and unit cell volume is 160.218(0) Å3. As a result, one can notice a slight distortion and lattice expansion by 0.24% on Si crystal (SiH+W) by high energy ball-milling treatment. Diffraction patterns of the two electrode are compared in Fig. H+W + FSN possess lower intensity, broader peak width accompanied with stronger diffusion scattering background (denoted by Q and Q” in inset) as compared to those of SiP + FSN. As consistently proved by high resolution transmission electron microscopy (HRTEM) images in Fig. H+W by HEMM. Quantitative structural parameters are summarized in Table P + FSN, average coherent length (Davg) is 834.9 Å for (111), 790.5 Å for (220), and 757.1 Å for (311) facets in Si phase. After HEMM treatments, Davg of SiH+W + FSN is respectively reduced by 20% in (111) facet (624.2 Å), ~43% in (220) facet (451.7 Å), and 46% in (311) facet (455.4 Å) as compared to those of SiP + FSN. In a meantime, H(111)/H(220) and H(111)/H(311) is respectively increased to 2.215 and 4.493 revealing its significant preference along Si (111) facet as compared to that of ideal Si crystal and SiP. Changes of those parameters resembles the “damage” of Si phase by high energy treatment and thus increasing the asymmetric crystal ratio in (111) facet.Crystal structure of Sirns Fig.  and corroperando LIB. Figure 15Si4 clusters in Si slabs and corresponding formation energy (Eform (eV/atom)) with facets indexed by the first three diffraction peaks in Fig. 15Si4 clusters built from its primitive bulk structure (ICSD no.167674) are homogeneously packing on surface with a slab size of 116.5–134.5 Å2. Such a result can be rationalized by steric effects when two Li15Si4 clusters are stacked in a surface with an area smaller than their close packed dimension (cross section area). Formation energies (Eform) are −0.019 (eV/atom) for (111), 0.02 (eV/atom) for (220), and 0.028 (eV/atom) for (311) facets. A negative value of Eform indicates an exothermic reaction meaning the strong preference of silicide formation in (111) facet when slab size is 3 × 3 (134.5 Å2). As for models with slab sizes of 207.1 to 253.7 Å2, Eform follows the same trend to that of model with small slabs (126.8 to 134.5 Å2). Among them, Eform of (111) is 0.019 (eV/atom). This value is reduced respectively by 52.5% and 65.4% as compared to that of (220)  and (311)  facets again showing a strong preference of silicide formation in (111) facet regardless the presence of steric effects in Si phase interface. Preference of silicide formation is further revealed by the homogeneous distribution of atomic packing scheme in (111) facet. For rest of two facets ((220) and (311)), silicide clusters tend to accumulate in slab corners. Given that accumulation of silicide occurs both in slabs with a large (253.7 Å2) and small (207.1 Å2) areas, steric effects should be a minor factor in Li silicide formation as compared to facet selectivity  in crystal surface once improper atomic packing configuration is adopted. With proper atomic packing configuration  facet), Eform is substantially reduced by 0.038 (eV/atom) with slab size by ~70 Å2 (from 207.1 to 134.5 Å2). Above interpretations could be explained by effects of interface lattice mismatch and atomic arrangements on heterogeneous crystal growth of silicide in a Si crystal surface. For growing polycrystalline thin films on a crystal with completely different structure symmetry, increasing surface atomic packing density reduces local steric barrier for the subsequent position of film atoms. On the other hand, with a large local steric barrier in opened facets  and (311)) reduction of Eform with slab size is insignificant and is because of the higher energy for silicide to packing in surface defects. It is surely that presence of surface defects facilitates Li intercalation to growth Li silicide, however, such assessment and effect might lead to side effects  and are not the topic to be discussed in this study.Affinity of Li silicide formation is a crucial factor in performance of Si materials as negative electrode in LIB. This factor is a combination results including atomic packing density of facets, size of Si crystal, and anchoring ligands as dock for Li intercalation. Effects of proposed high energy milling treatment on Li silicide formation affinity are evidenced by cross-referencing DFT (with structure models determined by XRD fitting) and XRD determined crystal structure evolution of experimental materials in operando XRD analysis on LIB with experimental Si materials (SiP + FSN and SiH+W + FSN) as negative electrode. Figure H+W indicates its smaller interfacial charge-transfer resistance (Rct) as compared to that of SiP. Such a phenomenon reveals the facilitation of Li intercalation in SiH+W surface due to the surface amorphization (probed by HRTEM images), preferential (111) facets, and reduced Davg (at (220) and (311) facets) in SiH+W. In a meantime, a uniform and local disordered amorphous Si thin layer  is formed in fresh silicon surfaces by wet milling which provides an easy access for Li intercalation. In addition, physical impacts of high energy mechanical milling and wet milling process reduce the primary and agglomerate size which shorten the Li ion diffusion length in a silicon particle. Presence of FSN provide additional Li storage sites. Those Li storage sites share the redox loading and thus reduce chemical stress in Si surface. As denoted by reducing of semicircle diameters, presence of FSN modulates the redox kinetics and thus reduces impedance of Li intercalation in silicon-based electrode.Effects of preferential facet and particle size  on Li intercalation and Li silicide formation are complementary revealed combining electrochemical and operando XRD analysis further confirms the effects of crystal size, facet selectivity on silicide formation affinity, therefore, projects cyclability of Si materials in LIB. XRD patterns of LIBs containing negative electrodes of SiP + FSN and SiH+W + FSN in the first lithiation/delithation cycle are compared in Fig. 12 (002), graphite (002) (C (002)), and Si (111) facets. Rest of peaks at 9° and 10.48° are diffraction lines for (211) (peak S) and (220) facets (peak T) of Li15Si4. For SiP + FSN , quantitative results for crystal structure evolution of graphite (C), Si, and Li15Si4 phases in the first lithiation and delithiation cycle of LIB are cross-referenced in Fig. 24, d(002)G ~3.47 Å), dilute lattice-gas disordered stage 2 (LiC18), stage 2 G ~3.53 Å), and stage 1 G ~3.70 Å) both in SiP + FSN and SiH+W + FSN16. In those stages, numerals  refer to number of empty layers between each Li-filled layer, and different stage reflects different d-spacing16. By cross-referencing changes of d-spacing between crystal structures of graphite and active material, an interesting correlation to the affinity of Si with adopted treatments are revealed in Fig. For clarifying lithiation affinity of SiP + FSN. As shown, increasing d-spacing at graphite (002) facet (d(002)G) from 3.36 Å to 3.52 Å indicates a successive evolution of Li-intercalated LiCx to stage 2 phase with capacity from 0 to 150 mAh g−1 in lithiation process. By increasing capacity to 150 mAh g−1, d(002)G is linearly expanded to that of Li enriched stage . Given that mass ratio of FSN is 50 wt%  in active material, transformation of graphite to stage 2 (LiC12) suggests that Li ions are mostly intercalate in FSN. In this event, FSN performs a substantial higher affinity for Li intercalation as compared to that of SiP in active material. After capacity higher than 150 mAh g−1, d(002)G is slightly increased by 0.01 Å (from 3.52 to 3.53 Å) by a subsequent lithiation to 1720 mAh g−1. For structural interpretation in details, changes of peak intensity for Si phases in SiP + FSN and SiH+W + FSN with lithiation ratio are compared in Figs 6 phase and vibration of Si (111) peak intensity  and is possibly due to a crack of LixSi phases from Si surface in a SiP + FSN electrode.Figure ity Fig. , one canH+W + FSN electrode , 3L (590–1200 mAh g−1), stage 2 (1200–1890 mAh g−1), and stage 1 (1900–2030 mAh g−1) are found in graphite phase evolution by lithiation from 0 to 100% (2030 mAh g−1). Among them, stage 1 is fully lithiated graphite phase and can only be formed by a kinetics balance between intercalation and diffusion rates of Li ions in graphite surface in a negative electrode lithiated higher than 94%. It is important to note that active materials possess a higher than 98% of ideal capacity of graphite and Si phases in SiH+W + FSN; where capacity contribution is ~170–180 mAh g−1 for graphite phase and ~1800–1900 mAh g−1 for Si phase. Compared to that of SiP + FSN, graphite phase delay with lithiation ratio indicates that affinity of Li ion to surface modified Si phase is substantial higher than that of graphite phase in SiH+W + FSN. Those scenarios are direct evidences rationalizing the strong lithiation preferential of Si (111), amorphous Si, and defect regions in SiH+W + FSN electrode. Facile delithiation from SiH+W + FSN is consistently revealed by phase transition of graphite. Shown in Fig. (002)G hold in stage 1 by delithiation from 0 to 125 mAh g−1 and then move to stage 2 until 235 mAh g−1. Further delithiation from 235 to 375 mAh g−1 results in a transition from stage 3L to stage 4L in graphite. In this region, this delithiation value is doubled to that can be offered by graphite meaning that Li extraction is mainly from Si phase in a SiH+W + FSN electrode. On the other hand, in delithiation process of a SiP + FSN electrode . Preference of lithiation/delithiation remaining hold even at the 50th cycles and is consistently revealed by comparing changes of d(002)G between SiP + FSN and SiH+W + FSN with respect to lithiation ratios of LIBs  peak is vibrating between 200 to 1100 a.u. with increasing capacity to 1490 mAh g−1 and then dramatically decreased to 0 by a subsequent lithiation till 1570 mAh g−1  by increasing capacity higher than 500 mAh g−1 in Fig. P is shown in Fig. H+W + FSN, Li15Si4 (211) peak intensity is increased from 0 to 40 a.u. by delithiation from 0 to 500 mAh g−1 and then progressively decreased to 0 in a subsequent delithiation till 1200 mAh g−1  peak reveal a suppression of Li silicide. Such a characteristic can be attributed to formation of local disordered Si/SiOx and increased ratio of (111) facets dimension with proper interface to facilitate Li intercalation and formation of amorphous Li silicide in SiH+W + FSN surface  facets and reduced Davg as predicted by DFT calculation and EIS analysis. Meanwhile, the same scenario on graphite phase evolutions delay proves the substantial improvement of Li intercalation/extraction performances of Si materials even hold after 50 cycles of LIB. Details of graphite evolutions in 50th cycles are given in ESI G to lithiation ratio (Δd(002)G/ΔL) of negative electrodes are compared Fig. + ions. Shown in Fig. (002)G/ΔL peaks in (1) 3.9%, (2) 6.8%, (3) 8.1%, and (4) 94.8% of lithiation ratios correspond to the maximum rate for graphite phase transition  in SiP + FSN. The first three peaks incur 97.8% of area indicate that most of active sites in graphite phase are lithiated in a lithiation ratio of 8.1%  for negative electrode. For the case of SiH+W + FSN, the Δd(002)G/ΔL peaks at lithiation ratios of (1) 4.6%, (2) 28.6%, (3) 30.2%, (4) 36.1, and (5) 51.8% suggest the presence of five transient states in graphite phase of SiH+W + FSN. In this event, as revealed by area of all Δd(002)G/ΔL peaks, most active sites (~96.1%) in graphite phase are lithiated in 36.1% of lithiation (~1240 mAh g−1) for SiH+W + FSN. In a subsequent lithiation, a broad peak across 36.1 to 58.1% can be attributed to formation of LiC6 transient state. As compared to that of SiP + FSN, transition of graphite phases is delayed by 20–25% of lithiation ratio in SiH+W + FSN. Given that phase transition rate of active materials is dominated by their affinity to Li+ ion, such a phase delay again consistently revealed the improvement of Li affinity on Si phase in SiH+W + FSN. Taking results of operando XRD analyses together, changes of graphite and Si phases with lithiation ratios in the first lithiation step of SiP + FSN and SiH+W + FSN can be respectively summarized in P + FSN and ~10% in SiH+W + FSN. Such a scenario can be attributed to a reduced lithiation/delithiation barrier by LiSix formation again proving the concept of graphite phase evolution as internal structural label to Li+ ion affinity of active materials in LIB.Structure evolutions of Si phases provide complimentary information to the preferential lithiation of active materials in negative electrode of LIB. Shown in Fig. g−1 Fig. . A dramag−1 Figs  and S3b.ESI Fig.  and latt(002)G) with lithiation/delithiation ratios of SiP + FSN in the 1st and 50th cycles are shown Fig. + ions move from Li metal to the negative electrodes. In SiP + FSN, d(002)G is significantly increased to a value of ~3.53 Å by increasing lithiation ratios from 5 to ~15%. Such a value is commonly known as a d-space of fully lithiated graphite (LiC12). Without significant differences of Si phase evolution in LIB, such a result suggests a lower diffusion barrier for Li+ ions in solid electrolyte interface (SEI) and graphite surface as compared to that in Si surface. For the case of SiH+W, situation goes to the opposite. As shown in Fig. (002)G is increased to that of stage 4L (3.37 Å) by increasing lithiation ratios from 5% to 30%. After that, d(002)G is then dramatically increased to that of stage 3L (3.47 Å) when ratio of lithiation is 40% and then progressively increased to 3.53 Å (stage 2) till 100%. Hereafter, one can notice a delay of d(002)G in SiH+W + FSN as compared to that in SiP + FSN in lithiation/delithiation processes. Such a delay on d(002)G with lithiation extent coincide to an inverse proportional of formation energy of Li15Si4 in Si surface as consistently proved by DFT calculations.Quantitative structural parameters on influences of surface modification to cyclability of silicon are determined by fitting the experimental diffraction patterns by the LAMP program. Results of graphite phase evolution (changes of d(002)G with capacity in lithiation and delithiation processes of SiP + FSN and SiH+W + FSN in the 50th cycle are respectively shown in Fig. (002)G evolutions in SiP + FSN in the 50th cycle is differed from that in the 1st cycle. Shown in Fig. (002)G in SiP + FSN is significantly retarded in the 50th delithiation process as compared to that in the 1st delithiation process. Such a delayed response on graphite phase and be explained by formation of significant amount of amorphous Si and retained Li silicide  in SiP + FSN in long-cycle test. Meanwhile, as consistently explained by vibration of Si(111) peak intensity, loss of capacity reveals a crack (pulverization) of Si particle in SiP + FSN in long cycle test which increases internal resistance between Si phases and back contact electrode. Changes of d(002)G for SiP + FSN and SiH+W + FSN in delithiation process are compared in Fig. P + FSN and SiH+W + FSN perform a similar decay trend on d(002)G. It means that graphite phase possesses a similar energy barrier for Li intercalation in both the two electrodes in the 50th delithiation process. It is worth to note that, as compared to that of SiH+W + FSN in the 1st cycle, d(002)G is suspended in stage IV by further increasing delithiation by 8–9%. It rationalizes the Li intercalation in Si phase is further facilitated in the 50th cycle for SiH+W + FSN in a LIB. Those results prove that formation of surface amorphous Si layer and increase ratio of (111) facet domain size with local distortion and surface modification by HEMM treatments improve the affinity of Li intercalation and extraction in Si surface . For SiH+W, capacity is 3470 mAh g−1 in lithiation and 2845 mAh g−1 in delithiation processes. It is important to note that both the SiP and SiH+W perform a columbic efficiency of ~82% in formation stage, however, with completely different fading manners of CR fading in a cycle test. For SiP, CR is exponentially decreased to 10% till the 50th cycle. On the other hand, CR is decreases by ~15% in the first five cycles (region A) and then slightly decreased by ~4% in the subsequent cycles (region B) for SiH+W. As compared to that of SiP, a substantially reduced CR decay in region A implies a reduction of energy barrier for silicide formation therefore improving cycle stability of SiH+W.Cycle performance test further confirms the prediction on cyclability of Si materials by P and SiH+W. Show in Fig. P is substantially reduced by mixing FSN in a weight ratio of FSN/SiP = 1.0. Although capacity fading is improved by 19.6%, its CR fading remaining reaction control. On the other hand, CR fading of SiH+W shows the opposite way to that of SiP when mixing with FSN in the same weight ratio. In the 2nd cycle, a slight increase of capacity might be attributed to the excess Li storage in FSN as compared to that of the 1st cycle. Such a hypothesis is further revealed by results of operando XRD analysis and impedance test which proving the insufficient/uncompleted Li intercalation in FSN in the first lithiation process. Similar to that of SiP, CR fading of SiH+W is improved by 12% by FSN additive with cycle number to 50.Addition of conducting graphite (FSN) further enhances the difference of capacity fading mode between Sioperando LIB cell. At the first cycle, delay of d(002)G expansion with increasing capacity reveals a strong preference of Li ion intercalation in silicon phase in negative electrode of SiH+W + FSN comprising modified Si powder in lithiation process. As compared to that of SiH+W + FSN, d(002)G is linearly increased and stabled to that of fully lithiated graphite (3.53 Å) with respect to a capacity of ~170–180 mAh g−1 revealing that Li+ ions are mostly intercalated in graphite phase by lithiation to 5% in negative electrode of SiP + FSN comprising pristine Si powder. Those scenarios are further confirmed by cross-referencing results of XRD, HRTEM, and DFT calculation indicating that performance of Si materials are improved by formation of preferential (111) facet accompanied with certain amorphous structure by HEMM treatment. A most important finding is that d(002)G delay remaining hold in an operando LIB cell even in the 50th lithiation/delithiation cycle. Such a scenario, in a fact of phase evolution dominated by Li interaction, proves that changes of d(002)G with lithiation ratios is a robust qualitative index for predicting the cyclability of active materials in the formation stage of a LIB.Graphite phase evolution is employed to evaluate cyclability of Si based materials as negative electrode in an P, ~10 um). To modify surface conformation of SiP, high energy mechanical milling (HEMM) and wet ball-milling in a planetary miller for 20 hours at room temperature were employed and resulting product is named as SiH+W15. Artificial graphite  was employed as an internal structure standard for labelling lithiation and delithiation affinity of active materials in negative electrode of LIB. The electrochemical measurements and the investigations of structural evolution of the silicon/graphite composite electrode were carried out by using CR2032 coin-cells. Experimental negative electrodes were prepared by casting 15 μm of active materials mixture in aluminium foil. The mixture contains 65 wt% of active materials , 20 wt% of carbon black (Super P), 9 wt% of poly(acrylic acid) , 3.5 wt% of styrene butadiene rubber , and 2 wt% of carboxymethylcellulose (CMC) dissolved in deionized water. Cells were assembled inside an Ar-filled glovebox. In a coin cell, counter (reference) electrode is lithium metal foil and separator is micro-porous polypropylene. The electrolyte was 1 M LiPF6 solution in a mixture of EC and DMC (1:1 in volume ratio). After assembly, experimental coin cells were stored for two days prior to electrochemical test. Electrochemical impedance spectroscopy (EIS) is employed to investigate the charge transfer resistance and the electronic resistance of activated material in lithium ion batteries. In this study, EIS characterization were performed on coin cells at open-circuit voltage (OCV) and the frequency was swept from 1 MHz to 10 mHz.Commercial silicon powder was purchased from Fuzhou Hokin Chemical Technology, China  of Spring-8 . XRD patterns are measured with an incident x-ray wavelength of 0.688992 Å by an area detector. Pixel resolution of detector equivalents to a step size of ~0.015° in two-theta and covers a range from 0 to 30°. The system temperature is ~30 °C and exposure time is 3~4 min per pattern in transmission mode. During the data collection, the operation voltage of LIB is ranged from 2 mV to 1.5 V at a constant current (CC) of ~0.5 mA . For conducting 17 code within the projected augmented wave (PAW) approach18. The Perdew-Burke-Ernzerhof form of the generalized gradient correction (GGA-PBE) functional is used to describe the exchange-correlation interaction19. The cutoff energy of 370 eV and Monkhorst-Pack k-point sampling of 12 × 12 × 12 were employed in Si bulk structure optimization, which were sufficient to obtain converged total energies. The calculated bulk lattice constant of 5.468 Å is consistent with experimental value of 5.431 Å. We utilized large slabs of Si(100)−12L, Si(110)-8L, and Si(111)-8L to simulate silicon surfaces. The bottom two layers were fixed in its bulk structure and the dangling bonds were saturated by hydrogen atoms, all the rest atoms were optimized with the convergence threshold for energy is 10–5 eV which could provide sufficient accuracy. A vacuum larger than 12 Å is used to avoid the interaction between periodic images. To clarify the affinity of Li silicide on Si surface, we performed analyses of Li15Si4 (most highly lithiated phases) on a series of Si surfaces with different size of area. The structure of Li15Si4 primitive cell consisting of thirty Li atoms and eight Si atoms is available in Inorganic Crystal Structure Database 20 by utilizing database from Material Project21. The formation energy can be obtained from the formula: Eform = /Ni, where Etotal and Esurface are the total energies of Li@Si and Si bare surface, respectively. Ni and μi are atom number and chemical potential of species i (Li and Si). Here, we used bulk energy per atom as the chemical potential.All calculations were performed in the framework of density functional theory, as implemented in the Vienna Ab initio Simulation PackageSupplementary Information for review and publication


Serum uric acid (UA) level has been reported to be associated with chronic allograft nephropathy and graft failure in patients who undergo kidney transplantation (KT). However, the role of serum UA level in renal graft survival remains controversial.This study aimed to investigate the effect of mean serum UA level during two different post-KT periods on long-term renal graft outcomes in a large population cohort in which living donor KT prevails.A retrospective cohort study was performed using KT data prospectively collected at a single institution. Patients  were divided into low-, normal-, and high-UA groups according to the mean serum UA level within the first year (1-YR) and 1–5 years (5-YR) after transplantation.In the 1-YR Cox proportional hazards analysis, the low- and high-UA groups had a significantly decreased and increased risk, respectively, for overall graft failure (OGF), death-censored graft failure (DCGF), and composite event  compared with the normal-UA group. Similarly, in the 5-YR analysis, the low-UA group had a significantly reduced risk of DCGF compared with the normal-UA group, whereas the high-UA group had a significantly increased risk of all three graft outcomes. In a marginal structural model, hyperuricemia had a significant causal effect on worsening graft outcomes, with consideration of all confounding variables .A low-to-normal serum UA level within the first year and 1–5 years after KT is an independent factor for better renal allograft outcomes in the long-term follow-up period rather than high serum UA level. Kidney transplantation (KT) has been considered the best treatment for patients with end-stage renal disease. However, the exact mechanisms of renal graft failure remain unclear in pediatric and adult patients despite various studies on improvement in graft survival ,2. ThereThe mean serum uric acid (UA) level during the first 6 months after transplantation has been reported to be an independent predictor of long-term graft survival and short-term graft function , and earHowever, numerous studies on patients with chronic kidney disease (CKD) have suggested a link between serum UA levels and renal dysfunction ,9. MoreoThe present study aimed to investigate the effect of low, normal, or high post-transplant serum UA levels during two different post-KT periods on long-term renal graft outcomes in a large Korean population cohort in which living donor KT prevails. We used an approach that simultaneously accounts for time-varying exposures and confounders, allowing valid inferences to be made from complex longitudinal data in observational cohort studies ,11.A retrospective cohort study was conducted using data of 2,993 patients who underwent KT from January 1992 to December 2014 at Severance Hospital, Yonsei University College of Medicine. The serum UA level was measured at 1, 3, 6, 9, and 12 months after transplantation and then annually throughout the study period. The serum creatinine level was also measured at the same time points, and the estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration formula [The study population selection process and exclusion criteria are shown in th percentile value from our data distribution  and 1–5 years (5-YR) after KT. High serum UA level was defined as a mean serum UA level >7.0 mg/dL in men and >6.0 mg/dL in women. Cutoff values for the low-UA group were defined using the sex-specific 10ribution . Each grribution .Patients with grafts and survival exceeding 1 year were included  in the 1-YR analysis, with a mean follow-up period of 130.9±74.4 months , whereas patients who underwent KT from 2011 to 2014 and those with survival <5 years were excluded in the 5-YR analysis. Thus, a total of 1,807 patients were eligible for the 5-YR analysis, with a mean follow-up period of 160±64.6 months .The primary endpoints were overall graft failure (OGF), death-censored graft failure (DCGF), and composite event, defined as the sum of return to dialysis, retransplantation, death from graft dysfunction, and >40% decline in eGFR from the baseline level, which was measured at 1 year  or 5 years  after transplantation. The secondary endpoint was eGFR decline.The study was performed in accordance with the Declaration of Helsinki principles and approved by the independent institutional review board of Yonsei University College of Medicine (IRB no. 4-2017-0834). Moreover, the clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the “Declaration of Istanbul on Organ Trafficking and Transplant Tourism.”Group differences in baseline characteristics were evaluated using Pearson’s chi-squared test for categorical variables and one-way analysis of variance with post-hoc testing using Bonferroni’s method for continuous variables. The association between serum UA level and graft outcomes was evaluated using Kaplan–Meier survival curves and log-rank test . To determine whether UA group was an independent risk factor for the three graft outcomes, Cox proportional hazards analyses were performed with the following models: 1) model 1 adjusted for transplant era, age, sex, body mass index, donor type, donor age, donor sex, number of human leukocyte antigen mismatches, pre-transplant diabetes mellitus, duration of pre-transplant dialysis, retransplantation, tacrolimus use, delayed graft function, biopsy-proven acute rejection within 1 year, systolic/diastolic blood pressure, and eGFR at 1 month after KT and 2) model 2 adjusted for covariates in model 1 with eGFR at 1 year after KT rather than at 1 month. The results of the Cox analysis are presented as hazard ratios (HRs) with 95% confidence intervals.Repeated-measures variables, such as serum UA level and eGFR, were evaluated using a linear mixed model. We estimated eGFR variation based on the baseline value and evaluated group differences in eGFR changes over time. Considering serum UA level as a time-varying factor, time-varying Cox models were fitted with adjustment for baseline covariates to confirm the effects of serum UA level on graft outcomes. However, this analysis could not confirm a causal relationship between serum UA level and graft outcomes, as serum UA and eGFR affect each other. Therefore, we used marginal structural models (MSMs)  to estimP-values <0.05 were considered significant.Most statistical analyses were performed using SPSS version 23.0 . SAS version 9.4  was used for the linear mixed model and time-varying Cox analyses, whereas R software version 3.4.3  was used for the MSMs and multiple imputation. The baseline characteristics are summarized in Tables In the 1-YR analysis, there were significant group differences in several variables known to affect graft outcomes, including transplant era, age, donor type, donor age, pre-transplant diabetes mellitus, delayed graft function, and biopsy-proven acute rejection within 1 year , as wellIn the 1-YR analysis, there were significant group differences in the Kaplan–Meier survival curves for overall graft survival, death-censored graft survival, and composite event-free survival. The low- and high-UA groups had significantly better and worse outcomes, respectively, than the normal-UA group. Similar results were obtained in the 5-YR analysis, with significantly worse outcomes in the high-UA group than in the normal-UA group; however, only death-censored graft survival was significantly higher in the low-UA group than in the normal-UA group .Cox proportional hazards analyses were performed to confirm that serum UA level was an independent factor affecting graft outcomes .In the 1-YR analysis, the low-UA group had a lower risk of OGF, DCGF, and composite event than the normal-UA group when fitted with model 1. In model 2, the low-UA group still had a lower risk of DCGF and composite event than the normal-UA group. The high-UA group had significantly higher HRs for all three graft outcomes than the normal-UA group in models 1 and 2.In the 5-YR analysis, the low-UA group had a significantly lower risk of DCGF than the normal-UA group in models 1 and 2. The risk of OGF and composite event did not significantly differ between the low- and normal-UA groups. The high-UA group had significantly higher HRs for all three graft outcomes than the normal-UA group in models 1 and 2. The complete results of the multivariate analyses are provided in To evaluate the association between UA level and progression of graft dysfunction, eGFR decline was compared among the three groups .P = 0.07). However, the cumulative eGFR decline was significantly lower and higher in the low-and high-UA groups, respectively, than in the normal-UA group, mainly during later periods, with a significant group-by-time interaction effect (P = 0.01).Because the baseline eGFR was significantly different among groups, we used a linear mixed model to compare the least-squares (LS) mean eGFR and cumulative eGFR decline at each time point, adjusting for all confounding variables. In the 1-YR analysis, the LS mean eGFR was significantly higher in the low-UA group than in the normal-UA group, especially during the first decade. In addition, the LS mean eGFR was significantly lower in the high-UA group than in the normal-UA group, especially during later periods. Changes in the LS mean eGFR over time were not significantly different among the three groups . The results of comparisons at each time point are presented in In the 5-YR analysis, the LS mean eGFR and cumulative eGFR decline were significantly lower and higher, respectively, in the high-UA group than in the normal-UA group throughout the follow-up period, whereas these measures were not significantly different between the low- and normal-UA groups. The group-by-time interactions were not significant in the 5-YR analysis , with consideration of all confounding variables .Average low-to-normal serum UA levels within the first year and 1–5 years after KT had a positive effect on long-term graft outcomes in KT recipients in this study. In particular, low serum UA levels after KT were associated with significantly better graft outcomes than normal serum UA levels. Consistent with previous reports, the present study also showed that hyperuricemia was associated with an increased risk of long-term graft failure compared with normal serum UA levels . The meaVarious time points have been used to investigate the causal relationship between serum UA level and graft outcomes  ,6,15–20.th percentile value . Furthermore, high serum UA level was defined in the present study as a mean serum UA level >7.0 mg/dL in men and >6.0 mg/dL in women, consistent with previous epidemiologic studies [The association between each cutoff value for post-transplant serum UA levels and KT outcomes has not been fully established. Higher UA levels were associated with a greater eGFR decline than a UA level of <6 mg/dL in an increasing dose–response relationship . Further studies .In this Korean cohort, only 14.9% of patients received deceased donor KT . We could not confirm the superiority of low UA level in deceased donor KT owing to the scarcity of patients with low UA level. However, high UA level was still significantly associated with worse outcomes in these patients. Further studies investigating the benefit of maintaining low UA level during the early post-transplant period in deceased donor KT patients would be required.Post-transplant hyperuricemia is associated with the use of immunosuppressive regimens that include cyclosporine, decreased glomerular filtration rate, diuretic use, and obesity during different follow-up periods ,29–32. ASeveral mechanisms for the effect of UA on renal graft failure have been proposed. Hyperuricemia may be a consequence of reduced glomerular filtration rate in the renal allograft and might contribute to glomerular hypertrophy and tubulointerstitial fibrosis itself ,36,37. IThe present study has several limitations. First, the study used a retrospective cohort design. Second, an extremely low serum UA level could be due to comorbid conditions common in KT recipients . Third, In conclusion, a low-to-normal serum UA level within the first year and 1–5 years after KT might be an independent factor for better renal allograft outcomes in the long-term follow-up period. In addition, the present study more clearly clarifies the association between serum UA levels and KT outcomes, adjusting for time-varying confounding variables. Despite the use of diverse research designs, patient populations, and statistical methods, the effect of serum UA levels on KT outcomes remains unclear. Further experimental and clinical evidence is required to determine whether serum UA level is an independent therapeutic target for KT outcomes.S1 FigThe distribution of mean UA within the first year after transplantation is shown in (a) and (b), whereas (c) and (d) show the mean UA from 1 to 5 years after transplantation in men and women, respectively.(TIF)Click here for additional data file.S2 FigP-values of the group-by-time effect between the low- and normal-UA groups; **: P-values of the group-by-time effect between the high- and normal-UA groups.The changes in the serum UA level over time according to the mean serum UA level within the first year after transplantation are shown in (a) and (b). The changes in the serum UA level over time according to the mean serum UA level from 1 to 5 years after transplantation are shown in (c) and (d). *: (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


In the past, lives and wealth have been lost due to corrosion in almost all engineering fields. Not only this, the cost of reviving damaged equipments in the industry due to corrosion contributed a lot to the gross domestic product of a nation. Thus, all hands must be on desk to combat this harzadous act via time to time research on its final resolution. However, current research works have revealed effective and reliable corrosion inhibitors from pharmaceutical drugs, plant extracts and ionic liquids as organic green corrosion inhibitors (OGCIs) with accommodative attributes such as being environmentally friendly, readily available, biodegradable, non-harmful, relatively cheap and many others to mention a few. This paper opens readers mind into the detailed classifications, mechanisms and active functional groups of these eco-friendly OGCIs. Not only the corrosion efficiency calculation ways but also influencing factors on efficiency were presented. Plant extracts, pharmaceutical drugs, ionic liquids and synthetic inhibitors, as among major sources of OGCIs, used in preventing material corrosion in corrosive media were separately and comprehensively examined. The significance of values obtained from simulating presented mathematical models governing OGCIs kinetics, adsorption isotherm and adsorption thermodynamics was also included. In conclusion, beneficial recommendations for both current and prospective researchers in the field of Corrosion Engineering were presented. Scavengers reduce medium corrosivity via aggressive substances scavenging while interface inhibitors inhibit corrosion through film formation at the environment/metal interface. The scavengers work in alkaline and almost neutral solutions by cathodic-oxygen reduction reaction simply written as However, the most prominent are the liquid-phase inhibitors which are further sub-divided into cathodic, anodic or mixed OGCIs based on the reaction type inhibit which could be any of cathodic, anodic or both electrochemical reactions. In anodic OGCIs, hydroxides, oxides or salts are produced to enhance passivating films formation which inhibits anodic metal dissolution reaction. Their mechanism is best explained by an active-passive metal polarization diagram. In cathodic OGCIs, corrosion is controlled either by cathodic poisoning or cathodic precipitation. In the former, sulfides and selenides which act as cathodic poisons are adsorbed on the surface of the metal to form protective films that reduce rate of cathodic reaction through oxygen diffusion minimization on metal surface. In the latter, insoluble compounds such as carbonates of calcium and magnesium are precipitated on metal surface in order to increase alkalinity at cathodic sites. Generally, hydrogen ions reduction to hydrogen atoms to form molecules of hydrogen as written in Eqs. Lastly, approximately 80% of OGCIs are categorised as mixed inhibitors which protect metal from corrosion by chemisorption, physisorption and film formation. Physisorption is facilitated by electrostatic attraction of negatively charged (anionic) OGCI with positively charged metal surface. Chemisorption process is slower than physisorption such that inhibition rate and adsorption increase as temperature increases 1.3p-electron and functional electronegative groups with conjugated double or triple bonds have been shown to be most effective 3 electron releasing substituents proved to have better efficiency than parent molecule having no substituents The OGCIs active ingredients consist of phytochemical constituents known to be functional groups with N, O, S, P or Se hetero atoms via which they are attached onto the metal surface , 45. ComD (-) pentose sugar (ribose) which consists of a phosphate monosodium salt and three antisymmetric carbons. The bark of Rhizophora Racemosa stem investigated to be very rich in tannins has been stated as the most effective OGCI for mild steel. Its basic structure contains residues of garlic acid attached to glucose through bonds of glycosidic Chamaerops humilis plant extract which is also rich in tannins is effective in inhibiting corrosion of mild steel in 0.5M sulfuric acid solution with 5% ethanol additive ∘C. Some prominent compounds such as benzoic acid 1.4OGCIs' efficiency in inhibiting corrosion is a function of their adsorption characteristics on the metal surface. Factors that have been considered by previous studies affecting OGCIs inhibition efficiency depend majorly on their structure, concentration, temperature and exposure time. Increase in OGCIs concentration results in simultaneous decrease in corrosion rate with increase in inhibition efficiency which approaches optimum level at certain concentration value. This resulted from formation of additional inhibitor molecules being adsorbed on the surface of metal which makes it complex for further corrosive attack to occur by the electrolyte solution. The dissolution of metal increases with corrosion exposure period in the presence of OGCIs. This is linked to previously adsorbed inhibitor molecules from metal surface resulting from partial desorption. Corrosion rate increases linearly as temperature increases such that equilibrium exists between adsorption and OGCIs molecules desorption at the surface of metal at a particular temperature. Increase in temperature as a result of higher desorption rate makes the equilibrium to shift until its' re-establishment at various equilibrium constant values. Thus, OGCIs inhibitive protectiveness decreases with increasing temperature. As earlier said, OGCIs' structural behaviour has a great influence on their efficiencies in corrosive media. Presence of heteroatom in OGCIs molecule enhanced their adsorption onto metal surface through the formation of adsorptive bond by Lewis acid-base reaction in which OGCIs and metal act as electron donor and acceptor respectively. The strength of adsorption bond is a function of electron density and polarizability of reaction centre. Conclusively, studies have shown surface active OGCIs adsorption to increase with increasing molecular weight and dipole moment.1.5The first step required in measuring efficiency of OGCIs is the preparation of metal sample to be examined for corrosion. The selection of metal coupons for checking OGCIs efficiency is vital as small changes in metal composition or available impurities during fabrication reflect in the results obtained 1.5.1θ2), Many researchers used test solutions prepared from actual field solution for corrosion testing while some used synthetic solutions prepared from analytical grade reagents in the absence of the former for WLM. Before the OGCIs efficiency is determined using WLM, metal samples are cleaned by polishing with abrasive paper of different grades and washed thoroughly using solvents  after which they are dried at room temperature. Vernier caliper is used to measure the dimensions of the metal specimen. The prepared metal coupons are then weighed prior to immersion using high-accuracy digital balance. The thoroughly rinsed corroded metal coupons are re-weighed after specified period of exposure time to check the weight loss. The corrosion rate θA and θB = respective surface area coverage by compound A and B when acting separately; and θAB = surface area coverage obtained for mixture of A and B. When the value of S approaches 1, interaction between A and B is negligible. If S is greater than 1, it reveals existence of synergism while a value of S being less than 1 signifies opposite effect between A and B However, there are cases where the inhibition efficiency of OGCI is enhanced as a result of combination with another OGCI such that the inhibition efficiency is increased by an appreciable value. This is called synergism effect which can be quantified using 1.5.2(V) of the system is measured and controlled by the reference electrode while the current (I) is measured by the counter electrode. As the electrochemical reactions progress, open circuit potential (Eocp) of the metal fluntuates. At equilibrium, a stable value is then measured afterwhich potentiodynamic polarization scan is performed. Application of a potential from a value below the initially measured Eocp to higher potential (between -0.25 to +0.25 V) then gives the Tafel plot. The corrosion current (icorr) and corrosion potential (Ecorr) can then be measured from the plots. icorr in the presence and absence of OGCIs using CR = corrosion rate (mm/yr), K1 = conversion factor = 3.27 × 10−3 mm g/μA cm yr, EW = equivalent weight (grams), ρ = density (grams/cm3), A = sample area (cm2), 2) in the absence and presence of OGCIs molecules respectively.Potentiodynamic polarization is another means of measuring OGCIs efficiency, corrosion rate and corrosion mechanism protection through electrochemical-based measurements. In most cases, the basic lab set up involves using three electrodes in the electrochemical cell which are working, counter and reference electrodes for the measurement immersed in the test solution of known volume and concentration. Platinum electrode 1.5.3in situ electrochemical changes with critical understanding of physical processes occuring at the metal/electrolyte interface 2SO4 at 30 °C by means of new schiff base extract with different concentrations as OGCI is shown in Rs (electrolyte solution resistance), in series with parallel arrangement of constant phase element (CPE) and Rct (charge transfer resistance) EIS is an essential method of monitoring Rp) obtained as real impedance difference at reduced and higher frequencies to replace the usual charge transfer resistance (Rct) Rp) was noticed to include Rct, accumulation resistance (Ra) resulting from species accumulated at metal/electrolyte interface, diffusion layer resistance (Rd) and inhibitor film resistance (Rf) at metal surface. Anode-cathode charge transfer causes metal oxidation which is usually obstructed by the presence of solvent molecules in aqueous acid solution. The resistance by the electrolyte solution is called the solution resistance (Rs). The charge transfer resistance (Rct) represents protective film capacity of adsorbed organic molecules on metallic surface to impede charges transfer the metal/solution interface. Impedance parameters which include Rp, n, Cdl and η% could then be obtained from Nyquist plot by the equivalent circuit.However, a study had used polarization resistance (C) A = CPE constant, j = imaginary number (i.e i2 = −1), n = phase shift exponent which is a measure of surface irregularity/inhomogeneity. The significance of n is that a lower surface roughness is obtained at higher value of n and vice versa. Also, n value determines the nature of constant phase element and states what A in −1), Rct(i) and Rct(o) represent charge transfer resistance in the presence and absence of OGCI various concentrations respectively.For better explanation of a frequency independent phase shift existing between an applied alternating potential and its current response, a constant phase element (CPE) being represented mathematically as In general, 2With reference to 2.1Industrial applications of corrosion inhibitors from greeners can be found in petroleum production, steel pipelines making industry, refrigerating industry, automobile, paint industry, acid producing companies and so on. 2.22.2.12+ ion boundary condition at anode (2) Anodic corrosion current density accounts for Fe2+ ion generation via electrochemical reactions at the surface of metal as the source term (3) Zero concentration of Fe2+ ion is applied at cathode due to scale formation (4) Fe2+ ion concentration in the shielded solution is the same as bulk solution in chemical equilibrium and (5) H+  anodic corrosion current density was used for Fe2), 2), The anodic corrosion current density is calculated using 2), ΔH = change in enthalpy (kJ/mol), R = gas constant (J/mol·K), T = solution temperature (/K) and Tref = reference temperature (/K).The iron oxidation exchange current density (2+ at anode (2+ at anode (mol/m2·s), 2), F = Faraday's constant (C/mole) and 2+ (moles).The mass flux of Fe2.2.2The derivation of equations governing kinetics of corrosion at the cathode is based on the assumption that oxygen and water reduction in the system is negligible such that the two cathodic reactions are stated as Eqs. + reduction partial cathodic corrosion current densities and H2CO3 reduction is stated as 2), 2), A general form used in the calculation of H+ and H2CO3 reduction at cathode are determined using The exchange current densities of HFor electro-neutrality condition in the solution, Thus, 2.2.32 stated as ia = anodic current density (A/m2), Mw,Fe = atomic mass of iron (kg/mol), 3), n = number of moles of electrons involved in iron oxidation (2 mole/mol) and F = Faraday's constant.Assuming corrosion rate is governed only by electrochemical reaction, total anodic reaction current density is used in determining corrosion rate of COThe current density for iron dissolution is obtained by using The Tafel slope of Iron oxidation 2), 2), Ecorr = corrosion potential (V), Erev,Fe = reversible potential of iron oxidation (V), Thus, 2), 2) and 2).The current density of any cathodic reaction is calculated thus 2), The charge transfer current density of cathodic reactions , 3) and F = Faraday constant The limiting current is determined from the mass transfer limitation for the case of H2 hydration reaction rate being very slow, limiting current density (3), 2CO3 in water (m2/s), 2 hydration reaction and 2 hydration reaction (s−1).Suppose there is restriction of carbonic acid reduction due to COf for A theoretical flow multiplier 2.32.3.12+ ion mass flux leaving the metal surface, can be determined using 2+ ion from the metal surface by diffusion and electro-migration, and (2) Fe2+ ion distribution in the solution is governed by Fick's second law. Thus, Fe2+ flux can be solved using Nernst-Planck equation 2·s), D = diffusivity (m2/s), C = concentration (mol/L), F = Faraday's constant (C/mole), z = valence (mole/mol), ℜ = ideal gas constant (J/mol·K), T = absolute temperature (K) and The risk of pitting corrosion could be increased under stagnant conditions in which corrosive microenvironments are established on the surface. The accumulation of stagnant electrolyte at the bottom of pipes, tubes and tanks could be prevented by both drying and ventilation. The buildup of local highly corrosive conditions could also be prevented through agitation 2+ ion and electrical field in the solution such that change in concentration with time The pitting corrosion rate is determined after computing the distributions of Fe2.3.2Stress corrosion cracking in metallic materials had been discussed previously Active path dissolution and film rupture model: For this, crack growth rate or crack velocity , The pure oxidation dissolution Faradic equation is stated as The anodic current density at time t is given as Substituting If Integrating Assuming Hydrogen embrittlement: The related stress corrosion cracking model is based on the assumption that crack advance occurred due to hydrogen assisted creep fracture (HACF) of hydrogen embrittled grain boundaries. Thus, stress corrosion crack growth rate is expressed as The critical fracture strain , 2S in bulk solution (mol/m3), 2S at steel surface (mol/m3), 2S at different mackinawite layer (mol/m2·s), 2S in liquid boundary layer (m/s), 2S in water (m2/s), The corrosion rate caused by H2.4θ) and OGCI concentration together describe the most suitable adsorption isotherm for the process. Prominent OGCIs adsorption isotherms have been stated to be Langmuir, Temkin, Frumkin, Freudlich, Virial Parson and Bockris-Swinkels isotherms R2) value that is very close to unity or equal to 1.Adsorption isotherms play key role in giving detailed information about existing interaction between molecules of OGCIs and metal surface 2.5Ea) of the process. The value of adsorption equilibrium constant (Kads) obtained from the best fitted isotherm tabulated in CR = Corrosion rate (mm/yr), N = Avogadro's number (6.02 × 1023 mol−1), h = Plank's constant (6.63 × 10−34 m2 kg·s−1), R = Gas constant (8314 J mol−1 K−1), T = Absolute temperature (°K), Ea = Activation energy (kJ mol−1) and A = Pre-exponential factor.The consideration of thermodynamics studies in the OGCIs adsorption reveals the significance of Gibbs free energy of adsorption 33.11.Many of the greeners considered for extraction of OGCIs are edible and are very useful for human need in many areas such as medicinal, pharmaceutical, food consumption and so on thus, making them to be very competitive in terms of functionality.2.It has been established that many silicates also possess corrosion inhibitory attributes due to their capability to block corrosion active sites on metals in acidic medium.3.None of the examined studies presented chain reaction mechanisms and reaction pathways showing present of intermediates in the course of corrosion reaction inhibitory effects of used OGCIs and examined metals.4.as the case may be) in pure form and the spent OGCIs.Also, there is complexity of adequate separation techniques to be employed in obtaining tested acidic or alkaline solution  as a result of corrosion over period of time.12.ginkgo biloba leave Diospyros Kaki L.f husk Some corrosion inhibitors extracted from 13.2SO4 were the most widely used because of their high corrosive nature to most metals and alloys even at low concentrations 3 and H3PO4 were explored in isolated cases Acid solutions have found industrial application in mill scales removal from metallic surfaces, acid descaling, acid pickling, industrial acid cleaning and oil well acidizing. An important stimulation technique for enhancing oil production is petroleum oil well acidization. However, studies of metal corrosion using organic acid solutions were rare as compared to similar studies with mineral acids as corrosive media 14.Previous researchers have been using days and months to critically figure out adsorption corrosion process of examined materials, media  and inhibitors from greeners to truly determine percent inhibition efficiency of inhibitors. Though very few researchers have been working on methodologies and technologies to employ to reduce corrosion examination period as presented in 15.uniquely bridged the gap among the stated sources of OGCIs by providing useful information for prospective researchers in the field of corrosion engineering. Also, many researchers rarely present various isotherm and thermodynamic models used in validating their corrosion experimental data. Only results are usually presented and discussed. All applicable isotherm and thermodynamics equations that any researcher in this field could think of are well-presented for future usage. Nevertheless, various kinetics modelling corrosion equations for various corrosion types also make this review paper to be superb. Prospective researchers are implored to apply them in forcasting corrosion types and corrosion rate of materials in order to cut wasteful corrosion costs.Recently, researchers have written separate comprehensive review papers on plant extracts The following few points were concluded after comprehensive scrutiny of literatures on this subject matter:3.21.There is need for future research works to focus more on wastes from greeners constituting environmental nuisance causing harmful effects on both human and aquatic natures. Recently, Al-Zubaidi et al. 2.Researchers should focus on synthesizing composite OGCIs from greeners extracts and silicates (such as rice husk waste) to improve their efficiency. This phenomenon is refered to as synergism effect which can be quantified by applying 3.Prospective researchers should show how intermediates produced during corrosion reaction inhibitory effects of OGCIs and metals contribute or affect the inhibition via adsorption of OGCIs on metals. It is an established fact that when extracts from greeners react, intermediates are formed in the course.4.The reusability of spent OGCIs and decrease in their efficiency with time is a great challenge for prospective researchers in the field of Corrosion Engineering.5.2SO4 solutions for some period of time. In support of these, existing optimization tools such as response surface methodology and central composite design of design expert coupled with predictive tools such artificial neural network based Monte Carlo simulation, sum of square errors and so on will be of help Cost effective modern techniques that will maximize OGCIs extraction in pure form from their sources are required. Detailed financial implication from pilot scale to full industrial plant is needed to expose general public to see this as a key means of internal revenue generation. Previous studies focussed majorly on refluxing greeners in HCl, ethanol and H6.Flavonoid is a good candidate to explain the corrosion inhibition effects observed in greeners. Its detailed chemistry must be studied to enhance further works on contribution of flavonoid in OGCIs on corrosion inhibition of metals.7.Phytochemical analysis provides information on active ingredients present in plants extracts acting as corrosion inhibition of OGCIs. Therefore, it is most likely that a mixture of constituents are acting as corrosion inhibitors 8.Additional advanced characterization techniques coupled with fundamental studies are required to further differentiate OGCIs mechanism and investigate relationship binding their structure with experienced corrosion inhibition. This knowledge will help tailoring OGCIs structure in obtaining necessary corrosion inhibition properties 9.Further research works are required on using additives such as iron control agents, water wetting agents, anti-sludge agents, non-emulsifiers, stabilizers and viscoelastic surfactants with the green corrosion inhibitors. They do not have corrosion inhibition potential but can enhance corrosion inhibition performance of OGCIs and significantly reduce corrosion rate.10.Deep studies are required on kinetics of corrosion inhibition of materials by OGCIs via earlier presented modelling equations. Also, more studies are needed in the aspect of developing mathematical models with reduced assumptions involving kinetics and mechanistic studies in predicting corrosion inhibitory effects of OGCIs from plant extracts. Computer software that will enhance quick prediction and other applications pertaining to corrosion inhibitory exhibits of these OGCIs can be developed.11.Novel research works on quantitative models development to bridge chemical structure to properties using existing machine learning or statistical approaches are required. Though quantitative structure-activity relationships (QSPR) and quantitative structure-property relationships (QSPR) modelling had been presented , 202, th12.There is need to work assiduously on other metallic components such as copper, alloys, aluminium, stainless steel and so on. All these work in unison and play specific roles in material selection for domestic/industrial purposes. They also corrode when subjected to certain environmental conditions. They are also major components of automobiles whose corrosion is of major concern.13.Thorough research is required on the use of OGCIs from greeners that have strong affinity for concrete cements and constituents (pastes) to increase the life span of reinforced concrete structures damaged as a result of high alkalinity and tackle rusting of these iron rods used in building construction.14.2, H2S and NaCl besides MIC type having great impact on metals used in oil and gas industries. Nevertheless, identification of corrosion type inhibited by OGCIs based on the solution examined for the materials is necessary.More researches should be carried out on other corrosive environments like COCurrent researchers whose research works center on this subject matter and prospective researchers who may develop interest after indepth understanding of this article could develop and execute novel research works in Corrosion Engineering field by considering the following stated recommendations:All authors listed have significantly contributed to the development and the writing of this article.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.The authors declare no conflict of interest.No additional information is available for this paper.


The Kocide applications were performed alongside three different mineral fertilization levels  to assess the environmental impacts of this nanopesticide under low-input or conventional farming scenarios. The effects of Kocide over time were monitored on forage biomass, plant mineral nutrient content, plant-associated non-target microorganisms  and six soil microbial enzyme activities. We observed that three sequential Kocide applications had no negative effects on forage biomass, root mycorrhizal colonization or soil nitrogen fixation rates. In the Low and High fertilization treatments, we observed a significant increase in aboveground plant biomass after the second Kocide exposure . Soil microbial enzyme activities were significantly reduced in the short-term after the first exposure (day 15) in the Ambient (-28% to -82%) and Low fertilization (-25% to -47%) but not in the High fertilization treatment. However, 2 months later, enzyme activities were similar across treatments and were either unresponsive or responded positively to subsequent Kocide additions. There appeared to be some long-term effects of Kocide exposure, as 6 months after the last Kocide exposure (day 365), both beta-glucosidase (-57% in Ambient and -40% in High fertilization) and phosphatase activities (-47% in Ambient fertilization) were significantly reduced in the mesocosms exposed to the nanopesticide. These results suggest that when used in conventional farming with high fertilization rates, Kocide applications did not lead to marked adverse effects on forage biomass production and key plant–microorganism interactions over a growing season. However, in the context of low-input organic farming for which this nanopesticide is approved, Kocide applications may have some unintended detrimental effects on microbially mediated soil processes involved in carbon and phosphorus cycling.The environmental fate and potential impacts of nanopesticides on agroecosystems under realistic agricultural conditions are poorly understood. As a result, the benefits and risks of these novel formulations compared to the conventional products are currently unclear. Here, we examined the effects of repeated realistic exposures of the Cu(OH) Novel applications of nanotechnology for plant protection and nutrition is leading to the development of so-called “nanopesticides” and “nanofertilizers.” The emergence of these nano-enabled agrochemicals may be a promising avenue for reducing agricultural impacts on the environment and on human health . BecauseThe direct, intentional, and repeated application of nano-agrochemicals could potentially become a pathway by which large masses of nanomaterials may be introduced into agroecosystems. Previous ecotoxicological studies raised concerns about the impact of nanomaterials on plant health and soil organisms 2 and thus this agrochemical product containing nanomaterials as the active ingredient is considered as a nanopesticide  nanopesticides used as a fungicide and bactericide. The commercial pesticide Kocide 3000esticide . It is aarticles . While tarticles . Moreovearticles . If so, Nanopesticides are typically added to agricultural soils or crops alongside other agrochemicals that can interact to increase  or decrease  the potential effects on organisms. In particular, agro-ecosystems are exposed to different fertilization regimes depending on the farming type , tillage practices, and the type of crop grown. Fertilization could indirectly influence Cu bioavailability as a consequence of the changes in soil pH and OM . Additio2 nanopesticide impact plant biomass, plant-associated non-target microorganisms , and soil microorganisms involved in OM degradation in an agro-ecosystem?, (2) How does fertilization influence the Cu(OH)2 nanopesticide effects on the biotic endpoints?, and (3) What are the legacy effects of repeated application of a Cu(OH)2 nanopesticide on agro-ecosystem functioning? To address these questions, we conducted a 1-year outdoor mesocosm experiment where we exposed forage crop cover to a series of repeated applications of the commercially available Cu(OH)2 nanopesticide (Kocide 3000), simulating realistic agricultural application rates. This experiment was performed under three fertilization levels  to test for interactive effects between the nanopesticide and enhanced resource availability of fertilization additions and simulate relevant farming practices with varying levels of nutrient inputs . The mesocosms were sampled on short- (15 days) and long-term (2.5 months) timescales after the nanopesticide exposures to assess the resistance and resilience of the agro-ecosystem to this disturbance. A final sampling was performed 6 months after the last Kocide exposure (1 year after the initiation of the treatments) to assess the legacy effects of this nanopesticide after a growing season.In this context, we asked three main questions: (1) Do repeated applications of a Cu(OH)The outdoor terrestrial mesocosms were set up in the Duke Forest . Each mesocosm [51 cm (l) × 25 cm (w) × 5 cm (h)] was filled with ∼81 kg of a sandy-clay-loam soil  comprised of: 57.7% sand, 20.5% clay, 21.9% silt, and 4% organic matter (pH = 5.8). The weather conditions at the mesocosm site were monitored during the entire experiment , Chamaecrista fasciculata , Brassica napus , Cichorium intybus , Sorghastrum nutans , and Urochloa ramosa . In the spring of 2016, each box was mowed and additionally seeded with Medicago sativa . The different plant species were grouped in three main plant functional groups: forbs, graminoids, and legumes.The mesocosms were seeded with seven forage crops plant species, selected based on growth performance and common agricultural use in pasture regions of the surrounding North Carolina Piedmont : TrifoliTo control water availability within the mesocosms, a sprinkler system was installed in the summer, to water each mesocosm every 3 days for 15 min unless a rain event occurred.® fertilizer  following the NCDA recommended rates for pasture crops1. The Ambient mesocosms received no supplemental fertilizer; the Low mesocosms received 1.72 g N, 0.77 g P, and 1.29 g K; and the High mesocosms received 3 × Low . Before the Kocide exposures started, the soil chemical characteristics in the different fertilization levels were determined (Table 1). The soil NH4+ and NO3- concentrations were determined after a KCl extraction (2M) on a Lachat QuikChem 8000 . Soil pH was measured according to ISO 10390 in pure water and soil OM matter was determined by loss on ignition.Three fertilization treatments were initiated on September 4, 2015, 8 months before the beginning of the nanopesticide applications. Fertilization levels  were adjusted using the Osmocote® 3000  nanopesticide treatment regimes started. Kocide contains Cu(OH)2 nanoparticles . The control mesocosms were sprayed with the same volume of deionized water to hold constant across mesocosms the changes in soil moisture availability and temporary cooling from the spraying. Following the recommendations of the North Carolina Cooperative Extension Service for forage crops, three plant biomass harvests were conducted at a 2.5-month interval . The six treatments combinations were replicated across six independent mesocosms for a total of 36 mesocosms. In summary, the mesocosms were exposed to 3 consecutive nanopesticide exposures or to control exposures during the experiment under three different fertilization conditions: 2 nanopesticide conditions (including control) × 3 fertilization treatments × 6 replicates = 36 mesocosms.On June 8th, 2016, the Kocidearticles  with an articles . The Cu Figure 1). To avoid resampling the same spot or unevenly sampling the mesocosms, each soil core was extracted at different locations each time following a randomized sampling strategy established at the beginning of the experiment. The soil core samples were immediately stored at 4°C before analysis (less than a week). The soil samples were homogenized and sieved to <2-mm mesh (USDA standard). Soil moisture was determined by drying a ∼10 g subset of each soil core for 48 h at 105°C. Soils collected after the third, final exposure (day 170) and on the long-term sampling (day 365) were oven-dried and ground for microwave assisted acid digestion using 10:1 HNO3:H2O2, following US EPA Method 3052. Total Cu concentrations were then measured using ICP-MS  following US EPA method 6020A.Small soil cores  were collected before each new nanopesticide exposure  and 15 days after the exposure right before the plant harvest . An additional soil collection was performed 365 days after the first nanopesticide exposure .On these two sampling dates (day 170 and 365), an additional larger soil core  was collected to perform measurements of NN-degrading enzyme (chitinase – N-acetylglucosaminidase), a P-degrading enzyme  and a S-degrading enzyme (arylsulfatase). Fresh soil (2.75 g) was blended with 91 mL of a sodium acetate buffer  to obtain a homogeneous soil slurry. The soil slurry was then pipetted in 96-well deepwell plates with one of the six fluorescently labeled substrates or with fluorescent standards [4-methylumbelliferone (MUB)]. The plates were incubated for 3 h in the dark at 20°C, then centrifuged (2 min at 1000 × g) to pellet light-interfering soil particles, and the supernatant was transferred in black optical 96-well plates for fluorescence measurements on a plate reader . Enzyme activities were quantified based on the fluorescence measured in each sample (indirect assessment of substrate degradation and fluorophore release due to enzyme activity) and the conversion of the data based on the standard dilution curves of each substrate from the MUB wells.The potential activity of six microbial extracellular enzymes were measured using the protocol described by 2 fixation rates (includes both free-living and plant-associated N2 fixers) were measured on intact soil cores extracted from the same location in all the mesocosms. We determined N2 fixation rates by measuring acetylene reduction to ethylene using the standardized method of Acetylene Reduction Assays by Cavity ring-down laser Absorption Spectroscopy , soil N ARACAS; . Each son factor . We recon factor , but it Figure 1): 15 days after each of the three Kocide exposures; and at the end of the experiment (day 365). At each harvest, the plant biomass was mowed with cordless grass shears, and the different plant species were sorted before measuring plant dry biomass (72 h at 60°C). Representative composite samples of the aboveground plant biomass collected on day 170 and 365 were digested using HNO3:H2O2 and total Cu, iron (Fe), manganese (Mn), and zinc (Zn) concentrations were then measured using ICP-MS as described above.The aboveground plant biomass was harvested four times during the experiment (2 fixation (day 170 and 365), root mycorrhizal colonization was determined by carefully separating the roots and mycorrhizal hyphae from the soil cores, the roots collected were stored in 50% ethanol. Later, roots were removed from the 50% ethanol, rinsed with tap water and covered with 10% (w/v) potassium hydroxide. Then, roots were placed into an autoclave where they were heated for 3 min at 121°C. The potassium hydroxide solution was removed and the roots were rinsed with tap water. Roots were then acidified by soaking them in 1.0% HCl for 5 min. After removing the HCl, roots were covered with 0.05% tryptan blue (C34H28N6O14S4) and stained overnight at room temperature. Tryptan blue was then removed and the roots transferred to 50% ethanol after rinsing with tap water. Mycorrhizal colonization of stained roots was then assessed via the root slide technique  on the data collected on day 170 and 365 for which we had the highest number of plant and soil variables measured. Using the envfit function in the vegan package in R, we tested if these plant and soil variables were significantly correlated to the enzyme activity profiles on the ordination.All the endpoints were analyzed using generalized linear mixed-effects to model the effects of nanopesticide exposure , fertilization  and the interaction between nanopesticide exposure and fertilization by day of the experiment . In these models, main effects and interactions were nested by day, and mesocosm was treated as a random effect to account for serial correlation among observations from the same mesocosms over time . The mod R 3.2.3 . Post hop < 0.001); while the addition of Kocide did not significantly affect biomass production at any level of fertilization . Although there was no overall effect, post hoc tests indicated that Kocide application increased the aboveground plant biomass after the second exposure (day 90) in both the Low  and High fertilization treatments . For the two occasions on which destructive sampling allowed us to estimate belowground root biomass (days 160 and 365), we found no effect of Kocide on belowground biomass .Aboveground plant biomass increased significantly between each level of fertilization . Graminoids tended to dominate in the Ambient and Low fertilization treatments, while forbs tended to dominate in the High fertilization treatment at most dates. The single date on which we observed a Kocide treatment effect on a plant functional group was on day 90, when forb biomass was significantly increased in the mesocosms treated with Kocide in the Low fertilization condition .While our fertilization treatments led to shifts in plant communities, there was no consistent effect of Kocide on the relative dominance of the three-different plant functional groups . This endpoint was also unresponsive to either fertilization (p = 0.95) or the interactive effects between Kocide and fertilization (p = 0.55). Similarly, no effect of the treatments was observed on soil N2 fixation rates . Note that on day 365, N2 fixation rates were low enough in some samples to be below detection limit .There was no effect of Kocide exposure on the extent of mycorrhizal colonization of plant roots . In the Ambient fertilization conditions, large reductions in microbial enzyme activities were observed 15 days after the first Kocide exposure . The chitinase activity exhibited the highest reduction , followed by cellulase , phosphatase , beta-glucosidase , sulfatase , and alpha-glucosidase activity . By day 75, there were no treatment effects on enzyme activities, and enzyme activities did not decline in response to the second and third Kocide exposures. Prior to the Kocide addition on day 155 three C- degrading enzymes and sulfatase activity were substantially higher in the Kocide treated soils . At the end of the experiment (day 365), we measured significant reductions in both beta-glucosidase and phosphatase activities in mesocosms exposed to the nanopesticides .The effects of the Kocide on potential microbial extracellular enzyme activities were strongly influenced by the fertilization level. Significant interactive effects of Kocide exposure and fertilization were observed in all six extracellular enzymes activities measured . On day 15, three enzyme activities were significantly decreased  and the chitinase activity was stimulated by Kocide (+111%). On day 75, all the enzyme activities recovered from this initial exposure and no significant effects of the Kocide additions were observed for the remainder of the experiment at this fertilization level.In the Low fertilization treatment, microbial enzyme activities were also altered by the first Kocide exposure (day 15), albeit to a lesser extent than in the Ambient fertilization treatment  and beta-glucosidase  were observed on day 155 and alpha-glucosidase was increased on day 170 . Under High fertilization, only beta-glucosidase activity was negatively impacted by Kocide after 6 months .In the High fertilization treatment, Kocide treatments had limited negative effects on microbial enzyme activities and mainly generated significant augmentations after the second and third exposures. On day 90 and 170, the sulfatase activity was increased following Kocide exposures . Stimulation of the chitinase activity . This visualization did not reveal any clear shift in the enzyme activity profiles induced by our treatments but a Permanova analysis indicated a significant effect of the date of collection (p = 0.008) and a significant interaction between the Kocide and Fertilization treatments (p = 0.03). The soil NH4+, the plant aboveground biomass and forb biomass were found to be significantly correlated to the enzyme profiles on the ordination .Using a principal component analysis, we explored how the Kocide and fertilization treatments affected the soil enzyme activity profiles on day 170 and 365 and which environmental parameters influenced these microbial activities . Higher Cu concentration was found in the plant biomass of the High fertilization treatment compared to the Ambient fertilization in the mesocosms exposed to the Kocide . Overall, plant biomass Cu concentrations on day 170 averaged at 14.3 ± 0.3 mg/kg in the Kocide-treated mesocosms, while the concentration was 6.75 ± 0.18 mg/kg in the Control mesocosms. Based on the known concentration of Cu applied as Kocide per mesocosm, we calculated the percentage of Cu recovered on the plants on day 170, and found that only a small fraction of the Cu applied to the mesocosms (3.7–7.4%) was associated to the plants 15 days after the last Kocide application .Two weeks after the third and final Kocide exposure (day 170), plant biomass Cu concentrations increased twofold among the three fertilization treatments  though it should be noted that the maximum Cu concentrations in biomass increased with increasing fertilization level. Six months after the last exposure, we found that the plant biomass Cu concentrations were no longer significantly different between control and Kocide-exposed mesocosms . Moreover, on both final days (170 and 365), there were no significant changes in mineral nutrient content  of the plant biomass exposed to the Kocide exposures .There was no effect of the fertilization treatment on the amount of Cu associated to aboveground plant biomass . None of the soil Cu concentrations were significantly different from the high natural Cu background of this soil (90.5 ± 4.4 mg Cu/kg).The soil Cu concentrations were not significantly different between the control and Kocide treatments, even immediately following the third Kocide exposure , as well as a report of lettuce leaf biomass stimulation (Table 2). By adhering to realistic Kocide concentrations and soil conditions, reflective of agricultural conditions, our results show that Kocide 3000 applications do not lead to any decrease in plant yields of a mixed forage plant community. In fact, we found that plant growth is stimulated when Kocide treatments are in tandem with fertilization. The fertilization may have alleviated stress such that any direct, negative impacts of Kocide may have been mediated. We hypothesize that the stimulations observed were probably related to direct effects of the nanopesticide on plant health or nutrition and not to an indirect positive effect driven by the microbial endpoints measured. When the plant biomass stimulations occurred (day 90), the six microbial enzyme activities tested were not significantly altered by the treatments, with the exception of a positive increase of the sulfatase activity in the High fertilization treatment.Other than a 14 and 27% increase on day 90 after the second exposure in the Low and High fertilization conditions, we found limited effects of the three Kocide applications on aboveground plant biomass. Previous work examining the effect of Kocide 3000 on plant biomass include reports of significant decreases in lettuce , elegantmulation . However2(OH)3Cl, 2 3Cl, 2 , and copH)3Cl, 2 , do not H)3Cl, 2 . ConversH)3Cl, 2 . The lacThe most sensitive endpoints to the Kocide exposures were the extracellular enzyme activities involved in OM degradation  that are performed by a diverse group of soil microorganisms. Interestingly, the effects observed in the enzyme activities in the principal component analysis and when analyzing each enzyme separately were strongly influenced by the degree of fertilization and exposure duration. For instance, these significant interactions between the nanopesticide and fertilization treatments resulted in large decreases in all enzyme activities on the short-term after the first Kocide exposure in the Ambient fertilization, while three enzymes were decreased in the Low fertilization, and no effect was observed in the High fertilization condition. This result shows that—in a community of microorganisms not previously exposed to this nanopesticide—the resistance of microbial function to the nanopesticide increased with increasing soil nutrient availability. We hypothesize that the microbial communities involved in the enzyme synthesis in the Ambient or Low fertilization treatment were already stressed by nutrient limitation and thus had less energy to cope with the additional disturbance brought by the antimicrobial nanopesticide  and that the Cu added along with other micronutrients included in the Kocide formulation may have stimulated some microbial activities. Supporting this hypothesis, we observed on day 170 and 365 that soil NH4+ concentration along with plant biomass were significantly correlated to the soil enzyme activity profiles on a principal component analysis. Additionally, the microbial community could have shifted to become more Cu tolerant after the two previous nanopesticide exposures through the exclusion of sensitive microbial taxa over time . On subsequent sampling dates, the Kocide treatment generated no effects or stimulations of enzyme activities simultaneously in the Ambient and High fertilizations. Especially on the third exposure (day 155 and 170), the nanopesticide application was associated with significant increases in C, N, and S-degrading enzyme activities in both Ambient and High fertilization but not in the Low fertilization treatment. These treatment effects occurred at the end of the growing season in North Carolina (November). We hypothesize here that soil resources were more depleted than at previous dates  on day 365, 6 months after the last Kocide application in Ambient fertilization conditions. These decreases occurred at the same period of the season that the initial enzyme activity inhibitions previously described on day 15 and may be related to microbial responses to Kocide driven by seasonal effects  or to legacy effects of the nanopesticide on the microbial community function. Our experiment was not able to confirm either of these hypotheses. Additional research on the long-term effects of chronic nanopesticide applications and the influence of season on plants and microorganisms’ responses to this agrochemical is needed.Overall, the Kocide applications caused most of the inhibition on enzyme activities in the Ambient fertilization treatment and the majority of the positive effects on plant biomass and enzyme activities in the High fertilization treatment. These results suggest that when used in conventional farming with high fertilization rates, repeated Kocide 3000 application had limited negative consequences and induced positive effects on forage production and soil microbial processes over a growing season. However, in the context of lower-intensity fertilization where this nanopesticide is often used , Kocide 3000 applications may have some unintended detrimental effects on microbially mediated soil processes involved in C and P cycling and on forage production.Table 2). Assessing the fate in soil and long-term non-target effects of the new generation of Cu nanopesticides under realistic conditions is necessary to determine if they should replace conventional pesticide formulations.Long-term contamination of agroecosystems with Cu fungicides used in both conventional and organic farming is of great environmental and toxicological concerns, as Cu has a low mobility in soil and can accumulate over time . HoweverTable 2) (Following the last nanopesticide exposure performed in our experiment (day 170), we observed that the Cu concentration associated with aboveground plant biomass was double that of the control plants. This Cu accumulation was observed in all fertilization treatments and the Cu residue concentrations observed compared to the control plants were always lower than the recommended maximum residue levels authorized in the European Union market . Copper concentrations in plant biomass observed in our study were 100 times lower than in previous publications simulating foliar applications of Kocide 3000 (Table 2) ,b, 2017.In terms of biological endpoints, the microbial enzyme activities were the only parameters showing significant declines 6 months after the last Kocide 3000 exposure. These legacy effects on beta-glucosidase and phosphatase activities are particularly interesting because these processes were not affected by Kocide additions shortly after the second and third exposures. This observation indicates that despite the high resilience of microbial processes on the short-term (day 75), longer term declines can occur and these phenomena are hard to predict based on our pre- and post-exposure monitoring during the growing season. These results call for long-term assessment of nanopesticide impact on soil fertility mediated by microbial processes to uncover the abiotic and microbial factors driving these declines. Additionally, in a parallel mesocosm experiment simulating a wetland ecosystem exposed chronically to Kocide 3000, we observed that this nanopesticide caused large ecosystem-scale impacts, including an intensification of eutrophication and major algal blooms . Our fin2 fixation assays. JJ performed the mycorrhizal colonization assays. JU performed the ICP-MS measurements. MS, SA, and EB wrote the paper and all the authors edited the manuscript.MS, BC, SA, and EB designed the experiments. MS, SA, CB, and JR conducted the field work and the plant biomass and microbial enzyme analyses. WT and NC performed the NThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.