Our findings provide the experimental and theoretical foundations necessary for employing SIF mechanistically to estimate plant photosynthetic activity during periods of drought stress.Solar-induced chlorophyll fluorescence (SIF) has a high correlation with Gross Primary Production (GPP). However, studies focusing on the impact of drought on the SIF-GPP relationship have had mixed results at various scales, and the mechanisms controlling the dynamics between photosynthesis and fluorescence emission under water stress are not well understood. We developed a leaf-scale measurement system to perform concurrent measurements of active and passive fluorescence, and gas-exchange rates for winter wheat experiencing a one-month progressive drought. Our results confirmed that: (1) shifts in light energy allocation towards decreasing photochemistry  and increasing fluorescence emissions (the quantum yields of fluorescence increased to 0.062 from 0.024) as drought progressed enhance the degree of nonlinearity of the SIF-GPP relationship, and (2) SIF alone has a limited capacity to track changes in the photosynthetic status of plants under drought conditions. However, by incorporating the water stress factor into a SIF-based mechanistic photosynthesis model, we show that drought-induced variations in a variety of key photosynthetic parameters, including stomatal conductance and photosynthetic CO ChlF emng (NPQ) . NPQ is ng (NPQ) ,4. Thus,ng (NPQ) , have beng (NPQ) ,7. Howevng (NPQ) . In recelatforms ,9,10. Malatforms ,12,13. Platforms ,15. Marrlatforms  suggeste2 assimilation under drought conditions.As a consequence of climate change, droughts are expected to increase in frequency, duration and severity in many parts of the world, most notably in Africa, Asia and Central and South America , and the2 assimilation under a water deficit, one should not only measure passive ChlF radiance for drought plants, but also, simultaneously, gas exchange and active PAM fluorescence. In this study, we developed a concurrent leaf-level measurement system consisting of a portable gas-exchange system, a PAM instrument, and four high-resolution spectrometers. Using this measurement system, we obtained a variety of key photosynthetic parameters, actively and passively induced ChlF, and the light–response curves of gas exchange for both drought-affected and control plants during a month-long progressive drought experiment. We first show the cascade of decline in these photosynthetic parameters, which include stomatal conductance, net photosynthetic carbon assimilation, electron transport rate, and ChlF emission, in response to water stress. We present the variations in the quantum yields of photochemical quenching in PSII (ФP), fluorescence (ФF), and regulated and basal heat dissipation (ФN and ФD), namely, the probability of an absorbed photon being used in a given pathway, under different drought and light conditions. We pay particular attention to the response of the phase-shift in the ФF-ФP relationship to water stress and its implications for interpreting the SIF-GPP relationship. By reformulating the mechanistic light–response (MLR) model [P, ФF, ФN, and ФD under different degrees of water stress, (2) the reason why the drought response of satellite SIF observations is more pronounced than that obtained from leaf-level measurements, and (3) practical considerations regarding the application of the proposed model to large scales.To obtain a complete picture of the mechanisms regulating ChlF/SIF emission and photosynthetic COR) model , we are θSWC remained at 19.1% in the control pots but declined in drought-treated pots as the drought intensified. The θSWC reduction rate was high during the early stages of drought, decreasing by nearly half from 19.1% on WS0 (no stress) to 9.2% on WS9 (θSWC decreasing at a slower rate to 7.3% on day 16 of the drought cycle (WS16) as the drought stress progressed , resulting in the curling (WS7 and 10), yellowing (WS14), drying (WS20) and, ultimately, the death of stems/leaves (WS26) . The θSW% on WS9 , with thogressed . Subsequ by WS28 . The varAnet, GS, Ja_PAM, and ChlFP_F with progressive drought are shown in Anet. Increasing water stress diminished both the maximum Anet value achieved and the irradiance at which this maximum was observed (Anet decreased from 24.3 μmol m−2 s−1 at day 0 (prior to drought treatment) to 19.1 μmol m−2 s−1 at day 5, during which time SWC dropped from 19.1% to 12%. Anet decreased at a greater magnitude subsequently. Anet in the water-stressed plants fell to near-zero by day ten of the drought. Compared with Anet, GS showed an even more pronounced drought response . Drought stress had a relatively smaller effect on the electron transport rate , moderate (θSWC = 12.0%), and high (θSWC = 9.7%), which occurred at WS0, WS5, and WS8, respectively , when  to 0.36 . ФN shownditions . Both ФDse curve .−2 s−1. However, ФP showed a significant decline at high PAR values: at WS8, ФP decreased to 0.13, 0.10, and 0.07 at 1300, 1700, and 2100 μmol m−2 s−1, respectively , the variations in these four yields were still relatively limited when PAR ≤ 700 μmol mectively . NPQ sti regimes . Their nng trend .θSWC dropped below 9.0%, ФP decreased rapidly with increasing PAR, and this drop grew steeper as water stress developed , after withholding water, when eveloped . In othee curves . As thesreatment .F and ФP physiologically regulates the asymptotic behavior of the link between fluorescence and photosynthesis, that is, a positive or negative SIF-GPP relationship [F and ФP, and the breakpoint was identified as the value of ФP where the slope of polynomial shifted from positive to negative , and (2) ФF is inversely proportional to ФP under high ФP .The nonlinear relationship between Фtionship ,33. Simitionship , we fittnegative . This brP = 0.46. ФF was positively correlated with ФP when ФP ≤ 0.46, and they were negatively correlated when ФP > 0.46 , and the limited number of light regimes in the middle of the light–response curves. The breakpoints were again observed to decline markedly after 10 days of progressive water stress. For example, the breakpoint at WS12 occurred at ФP = 0.12  and the root mean squared error (RMSE) between the simulated and measured values.The trained parameters were applied to the testing dataset, and the resulting model performance in simulating Vcmax and Jmax between WS1 and WS6  for the period between WS0 and WS12  of the variability in Anet from WS0 to WS12  is one possible explanation for the overestimation. A decreased ФPmax has been suggested to occur under water stress [Pmax in drought would lead to an overestimation in χ (Equation (12)) and then in NPQ (Equation (10)), and would consequently lead to a higher simulated Anet than that observed ).The simulated response of nditions . The rML to WS12 . However drought , with th drought . The conr stress ,36. An oS can explain 91.7% (RMSE = 0.044 μmol m−2 s−1) of the variability in the measurements of GS collected between WS0 and WS12 (S for non-stressed plants (WS0) by about 15%. However, the overestimation was suppressed from the beginning of the drought-stress period: the RMSE between simulated and measured GS decreased to 0.040 μmol m−2 s−1 (WS1–WS12) for the water-stressed plants (2R = 0.89 and RMSE = 0.035 μmol m−2 s−1) in the early stages of water stress (WS1–WS4). The overestimation of GS at WS0 can be explained by the overestimation in Anet (βS as drought became more severe tended to cancel out the negative effect of overestimation in Anet (Equation (21)), resulting in an improvement in the simulation of GS.Overall, the simulated Gand WS12 . The rMLd plants . In part in Anet . The dec2 assimilation but with a lower magnitude  remained virtually unchanged under low or intermediate light levels  tended to increase as a main photoprotective mechanism, and ФD remained fairly constant (F.At moderate-to-severe drought (WS7–WS9), Gvanished  and the ynthesis . Given tresponse ,38,39. At levels . To compconstant . The engP continued to decrease, and the stressed plants kept increasing their use of thermal energy dissipation and fluorescence emission (ФF) to cope with excess light energy (D and decline of ФN (Under extreme drought conditions (WS10–WS12), Фt energy . Howeverne of ФN . This reP, and increase in ФF, pushes the stressed plant from the ‘NPQ phase’ to the ‘PQ phase’, resulting in a more nonlinear SIF-GPP relationship when considering both the drought and non-drought periods together.The results confirm that water stress increases nonlinearity in the overall relationship between photosynthesis and fluorescence . Under tWhen the stomata are closed, Marrs et al. (2020)  found thKDF, Kmc, Kmc, Г* and ΦPmax). These additional parameters are often assumed to be constant in the literature but may actually vary with water stress. The actual KDF value is currently unknown. Gu et al. (2019) [KDF to 19, while Liu et al. (2022) [KDF to be 9. According to Equation (3), using these two KDF values will directly cause Anet to change by a factor of two. KDF remains unchanged at the same temperature, but there is currently no relevant research on KDF under water stress. Kmc, Kmc and Г* depend on the partial pressure of oxygen and temperature, which are related to the specificity factor of Rubisco [Pmax decreases under water stress conditions. Considering these potential effects of water stress on the parameters involved in our theoretical equations, more future measurements are needed to quantitatively examine their relationships under water conditions. We performed a sensitivity analysis on the MLR model. The input variables are Ci, Kco, Γ*, ΦPmax, qL, and ChlFP_F, respectively. The sensitivity analysis results show that the main parameters affecting Anet variation are leaf physiological parameters, qL and ChlFP_F. These two parameters explain more than 80% of the Anet variation, while other leaf physiological parameters with greater influence such as Ci, Kco, and ΦPmax account for more than 15% of the total variation. However, the parameter Γ* plays a small role in explaining the Anet variation. Han et al. (2022) [qL is sensitive to light intensity and can be expressed by the exponential equation of two parameters (aqL and bqL) between the qL and PAR. However, we found that in the uniform plant functional types, the parameters aqL and bqL vary greatly. Therefore, we use the NPQ/ΦP version of the Ja-ChlFP_F equation. The NPQ/ΦP version mixes photochemistry and non-photochemistry. Further, this mixing is superficial because information on non-photochemistry is canceled out in the product of (1 + NPQ) and ΦP/(1 − ΦP) as ΦP contains both photochemical and non-photochemical information. We need to consider both the energy-dependent and energy-independent components of NPQ. This is particularly important for this study because it focuses on water stress, which likely induces energy-independent NPQ.Equation (3) also contains other parameters   set KDF . (2022)  assumed  Rubisco . Further. (2022)  studied toc as an input variable for Equation (3), several more steps will need to be performed. First, the contribution of PSII fluorescence to SIFtoc (fPSII) should be determined. Although Bacour et al. [fPSII can be also estimated from airT and PAR, more research is needed to assess how fPSII varies with species and environmental conditions [fesc_L-C) must be quantified, in addition to fesc_P-L (Equation (8)). In the near-infrared (NIR) region, fesc_L-C can be estimated from directional reflectance (RNIR) [toc and RNIR can be concurrently obtained from measurements of irradiance/radiance [fesc_L-C requires no additional observations. Third, a full-band SIF emission should be reconstructed from narrowband SIF. The full SIF spectrum at TOC can be approximated by a linear combination of basis spectra [βS and βB. Since satellite soil moisture satellite observations are sensitive to surface soil moisture, typically within the first few centimeters, current RZSM datasets are mostly produced by assimilating observations into model simulations [The weak response of fluorescence to a water deficit suggests that SIF alone, at least its physiological component, is not able to track drought-induced changes in plant physiology. However, the development of the rMLR model allows for a mechanistic understanding of the drought impact on photosynthetic characteristics using SIF, soil moisture, and two measurable meteorological variables as the inputs. To apply the rMLR model at the canopy scale, that is, by using narrowband SIFr et al.  showed tnditions . Second,e (RNIR) . Note thradiance . Thus, t spectra ,51,52. T spectra  model is spectra . Finallyulations ,56.Pro spectrometers , a PAM system , a short-pass filter, an external LED light source and fiber optics (connecting the PAM and spectrometers to the leaf chamber) , two HR2000+ spectrometers and two QE chamber) . With thWe used the LI-6800 gas-exchange system to control the environmental conditions of a modified leaf chamber. The original film of the LI-6800 transparent leaf chamber was replaced with high-transparency plexiglass. An external LED light source (see below) was fixed to the high-transparency plexiglass via a cylindrical plastic tube. The optical fibers of the PAM fluorometer and spectrometer were inserted into the leaf chamber via two fiber adapter bulkheads added to the plastic tube. These optical fibers were fixed to the diagonal of the fiber adapter bulkhead. In order to measure downward chlorophyll fluorescence spectral radiant energy fluxes, a metal plate covering the base of the leaf chamber was first added, and a fiber adapter bulkhead for inserting optical fibers was then fixed in the middle of this plate. All the fiber adapter bulkheads were sealed with clay to ensure the leaf chamber remained air-tight. To reduce light scattering in the leaf chamber, the inside of the light-source plastic tube and the upper surface of the metal plate connected to the base of the leaf chamber were painted with black acrylic paint  as a light trap b. The mo−2 s−1 for 0.8 s. The Dual-PAM-100 induced the maximum fluorescence quantum yield using the saturation pulse under dark-adapted conditions. The PAM was connected to the leaf chamber via a 1 m long single fiber of 0.8 cm diameter routed through the entrance hole  was used to measure fluorescence parameters. The Dual-PAM-100 featured a single red (625 nm) power LED for excitation of chlorophyll fluorescence. The saturating pulse was also driven by the red LED, which emitted a consistent 10,000 μmol mnce hole a. The anPro  high-sensitivity spectrometers, we simultaneously measured reflection, transmission, and upward and downward chlorophyll fluorescence spectral radiant energy fluxes of the leaf  calibrated using a NIST traceable integration sphere. Dark current and non-linear spectrometer calibrations were completed before each measurement.Using two HR2000+ and two QE the leaf c. The le−2 s−1 with a 400–700 nm wavelength range. To attenuate this LED light source, a customized 625 nm short-pass, fused silica filter with a diameter of 12.5 mm was placed between it and the O-ring fiber optic  with a ring-shaped fiber which provided a homogeneous light distribution across the leaf chamber. The actinic light source was capable of delivering 0–3000 μmol mer optic . After sTriticum aestivum L. cultivar Xi Nong 979) was sown in plastic pots (27 cm height × 21 cm diameter) filled with 9 kg of sieved, air-dried, loess topsoil and 1.95 g of urea. The field capacity of the soil was 24% and the wilting point was 9%. The plants were grown under a rainproof shed until the turning-green stage, and then moved to a climate chamber where CO2 concentration, air temperature and relative humidity were controlled at 400 µmol mol−1, 12 °C and 60%. The light intensity above the canopy during the experiment was kept at 600 μmol m−2 s−1 for 12 h per day, from 8 a.m. to 8 p.m. Beginning in late March, eight representative wheat plants in the heading stage were chosen for the experiment.The experiment was conducted between 15 October 2020 and 30 April 2021 in the Northwest A&F University, Yangling, China. Winter wheat  was continuously monitored using the weighing method [θSWC was maintained at 19.1%  [θSWC was gradually decreased to a final value of 6.3%. This steady decline in θSWC ensured that plants under the WS treatment were subjected to progressive drought stress [Throughout the 28-day experiment, the gravimetric soil water content  ,61 throut stress .o) and maximal fluorescence in the dark (Fm) of dark-adapted leaves. Next, the light–response curves and CO2 response curves of gas exchange and fluorescence were measured. The CO2 flow rate, air relative humidity and leaf temperature were kept constant at 500 μmol s−1, 50% and 12 °C, respectively. To obtain light–response curves, measurements were conducted at a CO2 concentration of 400 µmol mol−1. Light–response measurements were made with photochemically active radiation (PAR) light intensities of 0, 40, 90, 180, 350, 700, 1300, 1700, and 2100 µmol m−2 s−1. Next, we measured the CO2 response curve under saturated light intensity (1500 µmol m−2 s−1) using values of the CO2 concentration gradient of 30, 50, 100, 200, 300, 400, 600, 900, 1200, 1500 μmol mol−1.Measurements were made on attached leaves of the wheat plants under the two water treatments. The wheat leaf had to be placed in the 3 cm× 3 cm clear chamber along the diagonal of the square and positioned in the chamber with its adaxial surface facing the LED light source. Since the leaves did not fill the leaf chamber, the area of each leaf had to be accurately measured. After 1 h of dark adaptation, a saturation flash from the PAM fluorometer was used to determine minimal fluorescence  were continually recorded at 1 s intervals. Steady-state fluorescence emission (Ft), induced by the measuring beam of the PAM fluorometer, was also included. Maximal fluorescence emission in the light-adapted state (Fm’) from the PAM was recorded under each light intensity and CO2 concentration. From these measurements , we also obtained NPQ, a fraction of open PSII reaction centers (qL) [Ja_PAM, μmol m−2 s−1). The details for estimating them are provided in 2 assimilation rate  and stomatal conductance to water vapor  provided by the gas-exchange system were automatically stored every 5 s. To ensure that the gas-exchange conditions were stable, the measurements for light curves and CO2 curves were made after waiting at least 5 min, and up to 20 min, between each light intensity or CO2 concentration change. The filtered incident radiation of the LED light source at each light intensity were measured with a standard reflectance panel . The above measurements were taken at day 0  and at days 2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28 after withholding water . At WS0, the unstressed maximum carboxylation capacity of Rubisco  and the unstressed maximum electron transport rate  were estimated by fitting the FvCB model [2 response curves. According to the photosynthetic light–response curve of winter wheat at WS0, Anet increased steeply with PAR when PAR ≤ 350 μmol m−2 s−1, and increased slowly with increasing PAR when PAR ≥ 1000 μmol m−2 s−1 (see below). Thus, a PAR level between 350 and 1000 μmol m−2 s−1 was assumed to be the intermediate light condition, and PAR levels lower than 350 or higher than 1000 μmol m−2 s−1 were taken to be low and high light conditions, respectively.Throughout the course of each light–response curve and COers (qL) , and theCB model  to the CJa can be mechanistically estimated from qL, ФPmax, and the chlorophyll fluorescence flux density emitted from the photosystem II (PSII) across the full ChlF emission spectrum . At the photosystem level,KDF is the ratio between the rate constants for constitutive heat loss (KD) and fluorescence (KF) and is assumed to be 9 [qL is much less studied than the other PAM parameters [qL and ФPmax in the original MLR model can be replaced by ФP and NPQ:The MLR model  shows th to be 9 . Howeverrameters . A previrameters  shows thP and NPQ (dimensionless) can be estimated from air temperature  and PAR (see the section on the estimation of ФP and NPQ). Note that Ja in Equations (1) and (2) is already balanced by carboxylation and photorespiration [Ja_PAM, in this study, Ja was calculated from ChlF emission using Equation (2).The practical advantage of Equation (2) is that both Фpiration ,47. To dAnet for C3 and C4 species:Ag represents gross photosynthesis (μmol m−2 s−1); Rd is the daytime respiration (μmol m−2 s−1); Cc is the chloroplastic CO2 partial pressure (µmol mol−1); Γ* is the chloroplastic compensation point of CO2 µmol mol−1 [ζ is the fraction of total electron transport of mesophyll and bundle sheath allocated to mesophyll, assumed to be 0.4 [Anet from the observed SIF is illustrated in One can obtain ol mol−1 ,65; ζ iso be 0.4 . We refe1) detected by the PAM fluorometer for C3 species can be estimated as:The rMLR model is only valid for fluorescence emissions from PSII ; the con1 from the fluorescence yields  directly measured by the PAM fluorometer, we were able to correct them for PSI fluorescence. Accordingly, all other yields/parameters derived from these four yields, ФP, ФF, ФN, ФD, and NPQ, were also corrected for PSI fluorescence. See the details in By subtracting FL(λ), mW m−2 nm−1 sr−1):L_U(λ) and ChlFL_D(λ) represent the fluorescence radiance emitted from adaxial and abaxial leaf surfaces (μW cm−2 nm−1 sr−1), respectively, and λ is the wavelength (nm).The leaf-scale concurrent measurement system  providest yields [t yields before and after correction for PSI fluorescence can also allow for an approximate separation of the PSI and PSII spectral fluorescence yields. Further, Pfündel [1 to Fo was 14% and 45% in the spectral ranges below 700 nm (SW) and above 700 nm (LW), respectively. Considering the wavelength-dependent relationships among spectral and PAM fluorescence yields [L_PSII(λ)) to measurements of ChlFL(λ) can be estimated as:t is the steady-state fluorescence yield, and F1_SW (F1_SW = 0.14 × Fo) and F1_LW (F1_LW = 0.45 × Fo) represent the PSI contribution at SW wavelengths and LW wavelengths, respectively. Note that Ft and Fo used in Equation (6) were directly measured by the PAM fluorometer and thus contain contributions from both PSI and PSII.Due to the strong linear relationships between spectral fluorescence yields and the original PAM Ft yields ,69, deri Pfündel  showed tL_PSII(λ) must be further downscaled to the photosystem level (ChlFP(λ)) by accounting for the probability that a fluorescence photon escapes from the PSII light reactions inside the leaves to the surface of the leaf (fesc_P-L):To apply the rMLR model, ChlFfesc_P-L is approximately equal to the sum of leaf reflectance (R) and transmittance (T) [ance (T) :(8)fesc_P(λ) in Equation (7) has units of mW m−2 nm−1 sr−1. To obtain ChlFP_F as required by Equation (3), we need to integrate ChlFP(λ) between 640 and 850 nm and perform a unit conversion:h is the Planck constant (6.62607015 × 10−34 J·s), c is the light speed (3 × 108 m s−1), NA is the Avogadro constant (6.02 × 1023 mol−1), 106 is used to convert moles (mol) to micromoles (μmol) in NA, 103 is used to convert milliwatts (mW) to Watts (W), and 109 is used to convert nanometers (nm) to meters (m) in λ. For the application of the rMLR model at the canopy scale or beyond, see the see the Note that ChlFKD and KF are assumed to be 0.9 and 0.1, respectively [KN) because NPQ = KN/(KF + KD), and KF + KD = 1. KN can be estimated as [θSWC:C = (M − S)2, where M is the NPQ measured with the PAM, and S is the modelled NPQ. A Trust Region algorithm was used to update the values of the parameters after each iteration step, with the iteration terminating when the improvement in the cost function was less than 10−3. In this study, ectively . Note thmated as :(10)KN=aastrikov , b, c, dχ is defined as [Pmax is assumed to be 0.8 and is similar among healthy plants; ФP is estimated as [C and AJ represent Rubisco-limited and RuBP-limited gross CO2 assimilations (μmol m−2 s−1), respectively. αgrn represents the absorption efficiency of PAR by green leaves, and the value is usually fixed at 0.84. βPSІІ is the fraction of absorbed energy allocated to PSII, and the value is set to 0.5 [C and AJ are given by [KmC is the Michaelis–Menten constants of Rubisco for CO2 270 µbar, [KmO is the Michaelis–Menten constants of Rubisco for O2 16,500 µbar, [Oc is the chloroplastic O2 partial pressure, assumed to equal to the oxygen partial pressure 230,000 µbar, [Jp is the potential electron transport rate [Pmax. σ is set to 0.3 [θ is an empirical curvature parameter, which is also modelled as a function of θSWC:P χmated as :(13)ΦP=mt to 0.5 . AC and given by :(14a)AC=70 µbar, ; KmO is 00 µbar, ; Oc is t00 µbar, ; Jp is tort rate :(15)Jp=σt to 0.3 . θ is anes of ФP .Anet is limited by biochemical processes under water stress, such that a soil-moisture-dependent stress function  below) should be applied to regulate the parameters Jmax and Vcmax of the photosynthesis model [Jmax,0 and Vcmax,0 represent the unstressed values of Jmax and Vcmax, respectively, at the beginning of the experiment.is model :(17a)JmaTair:Katul et al.  and Liu Rd is described as [Here, we used air temperature as measured in the LI-6800 leaf chamber. ribed as :(19)Rd=02 was assumed to be infinite and thus Cc was considered to be equal to intercellular CO2 partial pressure Ci, [Ci is estimated as [Ca is the ambient air CO2 partial pressure (µmol mol−1), and Gc is the stomatal conductance for CO2 (mol m−2 s−1). Anet is the minimum of AC and AJ (Equation (14)). Because Ci, Anet, and Gc are coupled to each other, the estimation of Anet and Gc has to be resolved iteratively over Ci given an initial value, which is Ci = 0.7 × Ca for C3 winter wheat [Ci between two successive iterations is less than 0.1 µmol mol−1. The biochemical model of photosynthesis proposed by Farquhar, von Caemmerer and Berry [Anet as the minimum of the Rubisco-limited CO2 assimilation rate and the electron-transport-limited CO2 assimilation rate.Mesophyll conductance to COsure Ci, . Ci is emated as :(20)Ci=Cer wheat . The itend Berry  was usedc is estimated using a modified Ball–Woodrow–Berry model BWB [0 is the residual conductance (mol m−2 s−1), assumed to be 0.01 [sC is the CO2 concentration at the leaf surface (µmol mol−1), assumed to be the product of a/(a − 1) and Ci; VPD is the vapor pressure deficit (kPa); D0 (kPa) is an empirical parameter related to stomatal sensitivity to VPD, assumed to be 1.5 [a is a parameter related to Ci, assumed to be 11.0 [2 [βS is the normalized soil-moisture-dependent stress function which accounts for the reduction in GS under water stress. βS and βB (Equation (17)) can be defined as:βi ranges between 1 (for plants not suffering from drought) and 0 (transpiration is zero); the subscripts i = B and S are for biochemical and stomatal limitations, respectively [θFC and θWP represent θSWC at the field capacity (24%) and wilting point (9%); the fitting parameter qj is a measure of the nonlinearity of the effects of water stress on the biochemical and stomatal limitations, and j takes the values B and S for i = B and S, respectively [B (Equation (17)) and qS in (Equation (21)) were determined by minimizing a cost function, C = (M − S)2, where M represents the measurement and S is the corresponding simulated value .Godel BWB :(21a)Gs= be 0.01 ; Cs is to be 1.5 ; a is a  be 11.0 ; 0.64 is 11.0 [2 ; βS is tectively ; θFC andectively ,82. qB (Our results show that the response of fluorescence emissions to drought is smaller than those of either stomatal conductance or net photosynthetic carbon assimilation. At the canopy scale and beyond, however, structural dynamics dominate the spatial variation of canopy SIF in response to water stress, explaining the strong drought response of SIF retrieved from space. As drought becomes more severe, the shifts in energy allocation towards decreasing photochemistry and increasing fluorescence emission tend to push plants into the PQ phase, enhancing the nonlinearity in the overall relationship between photochemistry and fluorescence. We confirm that SIF alone has a limited ability to predict drought-induced declines in photosynthetic parameters. Alternatively, the rMLR model, using SIF as one important input variable, demonstrates a satisfactory performance in reproducing declines in stomatal conductance and net photosynthetic carbon assimilation. The rMLR model has good potential for applications at regional and global scales, and thus provides the basis for using SIF mechanistically to estimate GPP under the scenario of increasing intensity and the extent of droughts in the twenty-first century.


Fragaria × ananassa Duch) fruits, among others, is largely maintained by the presence of soluble sugars and organic acids. As the primary products of photosynthesis, they are energy depots in plants, necessary for the construction of cell constituents, but also serve as precursors of aromatic compounds and signaling molecules. (2) In this study, fruits of 25 strawberry cultivars were qualitatively and quantitatively characterized concerning individual sugars and organic acids by HPLC, FT-ICR-MS, and MS imaging analysis. In addition, the total quality index (TQI), as a novel mathematical model, was used to compare all individual parameters evaluated to obtain a quantitative single score, as an indicator of overall fruit quality. (3) Regardless of a large number of cultivars and monitored parameters that were studded, several cultivars stood out in terms of selected primary metabolites, such as ‘Rumba’, ‘Jeny’, and ‘Sandra’, while the latter had the best TQI score. (4) Intercultivar variations in sugars and organic acids profiles, along with other bioactive compounds, should be considered for selection of promising cultivars with improved naturally occurring nutraceutical traits. Besides the search for a pleasant taste, increased awareness of healthy nutrition resulted in heightening consumer demand for high-quality fruit.(1) The nutritional quality of strawberry ( Fragaria × ananassa, Duch.) is the most outspread and consumed fruit worldwide, the production of which has increased steadily in recent decades, reaching 9,175,384 t + ions of these compounds were also found in all analyses, but to a significantly lower extent.Pictures of the tissue sections of both cultivars are presented in wberries . NonetheThe general distribution trend is similar for all visualized molecules, as ions with comparable higher intensities were found for all four biomarkers in the skin, cortical tissue, and pith tissue region of the strawberry fruits, which is in good alignment with previously published data ,11,12,13O/C ratio as well as an H/C ratio is calculated for each assigned molecular formula. Thus, each molecule is presented in this plot as a single point. Specific O/C and H/C regions can be correlated to different compound classes  to reveal the composition of a complex sample. In a van Krevelen plot, lignin structures can be found between O/C = 0.3–0.8 and H/C = 0.5–1.5. Condensed hydrocarbons occupy a similar H/C region, but typically possess lower values in the O/C region (0–0.3). Additionally, lipids , peptides/proteins , amino sugars , and carbohydrates  can be distinguished with the help of this plotting option [In a further step to characterize biomarker molecules of higher molecular weight in the two representative strawberry cultivars ‘Aprika’ and ‘Sandra’, fruit extracts were generated and analyzed by ESI(−)- and ESI(+)-FT-ICR-MS. In order to study the general composition of these extracts, van Krevelen plots are utilized to visualize the variety of supposably contained compounds and compound classes in both extracts . For thig option .O/C = 0.8–1.3, H/C = 1.4–2.4). Thus, from these van Krevelen plots, we can derive that a high amount of organic acids, sugars, and polyphenolic compounds are present in both cultivars, as most of the ions were assigned to lipids, carbohydrates, lignin-like structures, and condensed hydrocarbons.According to 1O4–N1O8, S1, S1O5–S1O6 and O1–O16 are illustrated in In order to learn more about the molecules and compound classes that are present in the extracts from ‘Aprika’ and ‘Sandra’, the mass spectral data were further evaluated by clustering the assigned molecular formulae into heteroatomic classes, based on the number of nitrogen, oxygen, and/or sulfur that was assigned to a molecular ion. These heteroatomic compound clusters were then evaluated according to the total number of assigned molecular formulae and the relative abundance of different heteroatomic classes. Visualizations for these two assessment parameters for compound classes N1–O16. Most likely, these compounds are organic acids, esters, sugars, and phenolic compounds, which are typical compounds contained in strawberries [1O5 and S1O6. Compounds of the heteroatomic classes N1O4–N1O8 were mainly assigned to the ESI(+)-FT-ICR-MS data sets of both cultivars. In accordance with the van Krevelen plots, these molecules could correspond to peptides/proteins and amino sugars.From these plots it becomes obvious that the highest numbers of assigned molecular formulae from the illustrated compound classes were assigned to the ESI(+)-FT-ICR-MS data set of ‘Sandra’, even though that the amounts for ‘Aprika’ are in a comparable range. According to the ESI(−)-FT-ICR-MS data sets, slightly higher numbers are observable for ‘Aprika’. The relative abundancies are, in comparison, similar for both strawberry extracts for each individual heteroatomic class. In addition, we can derive from these plots that most of the compounds are assigned to oxygen-containing heteroatomic classes Owberries ,29,43,44Cn) vs. the double bond equivalent (DBE) of each heteroatomic class were utilized. Both Cn and DBE are obtained using the molecular formula assigned to an ion. The number of assigned carbon atoms gives the Cn value, whereas DBE can be calculated according to the number of assigned carbon, hydrogen, and nitrogen atoms. The DBE corresponds to the number of double bonds and cycles, which are present in the molecular structure. In contrast, Cn values are used to assess the alkylation degree of a compound class [The evaluation of the ESI-FT-ICR-MS data sets of both extracts was focused, in the next step, on the structural characterization of the different molecules in the various heteroatomic classes. For this purpose, visualizations of the carbon number , dicarboxylic acids , tricarboxylic acids , tetracarboxylic acids , pentacarboxylic acids , and hexacarboxylic acids  with Cn values between 11 and 43 can be supposed in the extracts of both cultivars. Higher oxygen numbers and DBE values could correspond to analogous structures that contain additional oxygen-containing functional groups . Additionally, monosaccharides up to trisaccharides can be assumed according to the Cn-DBE plots of the oxygen-containing heteroatomic classes, as molecules with a relatively high oxygen number (o ≤ 18) but low Cn (Cn ≤ 18) and DBE values (DBE ≤ 3) are present. Aromatic molecules are also assumable, according to the Cn-DBE plots of compound classes O6–O16 (DBE ≥ 4). These molecules are, most likely, flavonoids or anthocyanins, which are part of the polyphenolic compounds that are typically found in strawberries [The  ≤ 5 see . Thus, oc acids O, DBE = 5c acids O, DBE = 6wberries ,29,43,45DBE values (DBE ≤ 17), which might correspond to aromatic and amino-functionalized molecules. In contrast, only small amounts of sulfur- as well as sulfur-/oxygen-containing compounds are present in the Cn-DBE plots of compound classes S1 and S1O5–S1O6 .Nitrogen- and oxygen-containing compounds were mainly assigned to the ESI(+)-FT-ICR-MS data sets of extracts from ‘Aprika’ and ‘Sandra’ see . Most ofS1O6 see . Most ofThe chemicals for the sample extractions and for the mobile phases were HPLC-MS grade, methanol and phosphoric acid were purchased from Sigma-Aldrich , while sodium hydroxide was purchased from J.T. Baker . HPLC grade carbohydrate standards , as well as citric and malic acid were procured from Fluka , while shikimic and fumaric acid were purchased from Sigma-Aldrich . All utilized solvents and chemicals for MS analysis were purchased from Merck Chemicals, Carl Roth or VWR Chemicals as HPLC (purity ≥ 99.9%) or analytical grade chemicals (purity ≥ 99%). Water for the mobile phase was double distilled and purified with the Milli-Q system .Out of a total of 25 newly introduced strawberry cultivars that were used in our study, 22 are June-bearing cultivars , while 3 are ever-bearing types . All cultivars were grown at the same plantation in Serbia located in the municipality of Šid , a region that is characterized by a temperate continental climate, with a mean annual air temperature of 10.7 °C and a mean annual precipitation of 650 mm. The soil was categorized as a fine sandy loam (pH 6.8) with medium to high levels of nutrients. Cold-stored plants of tested strawberry cultivars were planted on raised double beds covered with black polyethylene foil in July 2020 with a planting density of 44,440 plants per ha. Drip irrigation with two laterals per raised bed and emitters at a 10 cm distance were applied. Fertigation was performed at a frequency in accordance with crop requirements previously reported by Tomić et al. .Fully ripe fruit samples were collected in three repetitions per 20 fruits (60 fruits per cultivar) during the second harvest in the first year after planting (2021). After harvest, fruits were stored at −20 °C prior to chemical analysis to prevent the effect of postharvest factors.w/v). After centrifugation at 13,000× g for 10 min at 4 °C, supernatants were used for HPLC analysis of sugars and organic acids. Three extracts were prepared for each sample analyzed.The whole fruits were carefully thawed, measured, and homogenized using mortar and pestle. Sugar compounds and organic acids were extracted in 80% methanol at a ratio of 1:3  acetone and 1% (v/v) acetic acid, was added. This suspension was sonicated for 15 min in the ultrasonic bath to extract organic compounds from the fruits. After this extraction time, the suspensions were filtrated through a cellulose filter  and the filtrates were concentrated in a water bath at 37 °C until a final extract volume of 2 mL was reached. These final extracts were then diluted 20-fold using a solvent mixture consisting of 80% (v/v) methanol, 19% (v/v) dichloromethane, 0.9% (v/v) water, and 0.1% (v/v) triethylamine for ESI(−) analyses or with a solvent mixture that contained 60% (v/v) acetone, 30% (v/v) isopropanol, and 10% (v/v) water (+ 1 mM NH4COOCF3) for ESI(+) analyses.Extracts of selected cultivars were prepared using adapted literature-known routines for the preparation of strawberry extracts ,28. Brie3PO4 as the mobile phase at a flow rate of 0.5 mL min−1 and a column temperature of 40 °C. The data acquisition and spectral evaluation for peak confirmation were carried out by the Waters Empower 2 Software . Results were expressed as gram of specific organic acid per 100 g of fresh weight (g 100 g−1 FW). Separation of organic acids was performed on a Waters Breeze system  consisting of 1525 binary pump, thermostat, and 717+ autosampler connected to the Waters 2996 diode array detector adjusted at 210 nm. Supelco C-610H (300 × 7.8 mm) column  was used, connected to the appropriate guard column. Isocratic elution was employed with 0.1% H−1. Sugars were eluted with 200 mM sodium hydroxide, prepared by dissolving a 10.5 mL of 50% w/w, low carbonate sodium hydroxide solution, , with vacuum degassed deionized water, to the final volume of 1 L. Signals were detected in the pulse mode within 150 ms of integration time using following waveform: E1 = +0.15 V for 400 ms; E2 = +0.75 V for 200 ms; E3 = −0.8 V for 200 ms. Filter timescale was 0.2 s, and the range was 1 μA for the full mV scale. The data acquisition and spectral evaluation for peak confirmation were carried out by Waters Empower 2 Software . Results were expressed as gram of specific sugar per 100 g of fresh weight (g 100 g−1 FW).Sugar analyses were performed on the same Waters system connected to Waters 2465 electrochemical detector with 3 mm gold working electrode and hydrogen referent electrode. Separation of sugars was performed on CarboPac PA1  250 × 4 mm column, equipped with corresponding CarboPac PA1 guard column at a constant temperature of 30 °C and a flow rate of 1.0 mL minAll quality characteristics have been divided into two groups, in line with the works of Finotti et al.  and DjekThree other parameters  were processed in line with rule #2—“the higher the value, the better the quality”, Equation (2):QI—quality index for a specific quality characteristic; ix—measured value in the subset of values; T—target value ; maxx—maximal value in the subset of values; minx—minimal value in the subset of values. For both equations, the following applies: QIs and considering all indices as vectors QI =  ϵ RN [TQI) was calculated as per Equation (3) [Upon calculation of all IN) ϵ RN , the tottion (3) :(3)TQI=∑TQI is “the lower the value, the better total quality index” [The rule of thumb for interpreting achieved y index” .The sweetness index was calculated by multiplying the sweetness coefficient of each sugar class  with individual sugar concentrations obtained by HPLC analysis, as described by Crespo et al. . In thisFruits of the strawberry cultivars ‘Aprika’ and ‘Sandra’ were characterized, as representative cultivars, by means of FT-ICR-MS. In a first step, cross and longitudinal sections of both cultivars were prepared to analyze the biomarker distribution with the help of MSI. For this purpose, the calyx of the strawberries was carefully removed and the samples were attached to a sample holder by embedding either the bottom part (cross sections) or a side  of the fruit with tissue freezing medium (Leica). Tissue sections with a thickness of 80 µm were generated using a Leica CM 1950 cryomicrotome. After every cut, the blade was cleaned with ethanol to avoid sample contaminations with the embedding medium. The sections were directly attached to glass slides, which were coated with indium tin oxide (ITO).c = 30 g/L in acetone) using a self-made matrix sprayer. This sprayer consisted of an ICP nebulizer (Thermo Scientific Burgener Peek Mira Mist), which was linked to the nebulizer gas inlet and syringe port of the ESI source of the mass spectrometer. Thus, nebulizer gas pressure and matrix flow could be adjusted via the MS software. The distance between the tip of the sprayer and the sample was kept at 5 cm and the matrix was applied in five several spray cycles. Furthermore, a spraying pressure of 1.0 bar, 300 µL/min flow of matrix solution and 5 s spraying time per matrix layer were used. After each spray cycle, the coated tissue sample was oven-dried for 10 min at 30 °C [In a second step, the freshly prepared tissue sections were coated with 2,5-dihydroxybenzoic acid (DHB)  for MALDI-MS analyses. For the characterization of the fruit extracts of ‘Aprika’ and ‘Sandra’ cultivars, the ESI source was operated in positive and negative ion mode, using the following parameters: capillary voltage +2800 V (ESI(−))/−3000 V (ESI(+)), end plate offset −500 V, nebulizer pressure 1.0 bar, dry gas flow 4.0 L/min, dry gas temperature 300 °C, and syringe flow 10 µL/min. Mass spectra were acquired in a m/z range of 153.52–2000.00 Da by accumulating 256 scans. Resulting data sets had a size of 8 M, the resolving power was R = 800,000 at m/z = 400 Da, and a Q1 mass of 170 Da was used [MS experiments were conducted utilizing a 15 T solariX FT-ICR-MS from Bruker Daltonics, equipped with an ESI source and a Smart Beam II laser  was ≥5. Mass calibration of the ESI-FT-ICR-MS experiments was conducted in a two-step process. In the first step, existing calibration lists were used for a first internal calibration. From these mass spectra, molecular formulae were calculated, and the resulting molecular formula lists were used to create an evolved calibration list, which contained molecular ions that are typical for the analyzed strawberry samples. Molecular formulae were assigned to peaks with s/n ≥ 10. The deviation from the theoretical masses should not exceed 0.3 ppm and the molecular formulae should show a predefined elemental composition . Exported peak and molecular formula lists were imported into MATLAB R2022b (Mathworks) and further processed and visualized using in-house programs for blank correction and molecular formula filtering. Filtering of the molecular formula lists was conducted by applying the rules established by Herzsprung et al. [DBE) ≥ 0, 0.3 ≤ H/C ≤ 2.5, O/C ≤ 1.0, N/C ≤ 1.0, S/C ≤ 1.0). The MSI experiments were also evaluated with the help of self-developed MATLAB programs using picture files (.tif) of the sections, spot lists (.csv files), and .imzML files, which were exported from Bruker Daltonics software flexImaging 5.0 (Build 80).Peak picking, calibration, and molecular formula assignment were accomplished using Bruker Daltonics software DataAnalysis 5.0 (SR 1). Mass spectral signals were considered peaks if their signal-to-noise-ratio . To examine significant differences in mean values of analyzed parameters between strawberry cultivars, analysis of variance (ANOVA) was used, while the Duncan test provided post hoc analysis. The level of statistical significance was set at 0.05.The chemical composition of fruit becomes an important selection criterion in fruit production and one of the main marketing factors defined by consumer preferences. This comparative study revealed that sugars and organic acids profiles in fruits of different strawberry cultivars varied among them, which could be of the utmost importance in the selection and promotion of the most suitable cultivars regarding fruit quality. Irrespective of the large number of cultivars and parameters that were monitored in this study, several cultivars were distinguished from others in terms of the selected primary metabolites: ‘Rumba’, ‘Jeny’, and ‘Sandra’. The latter was also the best ranking newly released cultivar regarding the overall fruit quality, according to TQI score. Since strawberry fruit quality is a complex concept that depends on both the physical and chemical attributes of fruit, the ‘Jeny’ cultivar has been suppressed due to the pink color of the fruit which is quite unacceptable to the consumers . On the Congregated, all of the presented data provide additional value to the “strawberry story”. Producers and breeders can be assured that in addition to the traditional detailed, quantitative analysis of primary metabolites, TQI can be used as a reliable tool for electing promising cultivars among newly introduced ones. On the other hand, detailed studies by FT-ICR-MS provide novel information regarding strawberry fruit metabolic distribution, which extremely important for understanding fruit physiology processes, going deep into the structures that define taste, acceptance, and hence the fruit quality.


From ambient pressure dielectric and mechanicalexperiments, we found that charge transport of all three examinedILs is viscosity-controlled at high temperatures. On the other hand,ion diffusion is much faster than structural dynamics in a nanostructuredsupercooled liquid (at T < 210 ± 3 K), whichconstitutes the first example of conductivity independent from viscosityin neat aprotic ILs. High-pressure measurements and MD simulationsreveal that the created nanostructures depend on the anion size andcan be modified by compression. For small anions, increasing pressureshapes immobile alkyl chains into lamellar-type phases, leading toincreased anisotropic diffusivity of anions through channels. Bulkyanions drive the formation of interconnected phases with continuous3D curvature, which render ion transport independent of pressure.This work offers insight into the design of high-density electrolyteswith percolating conductive phases providing efficient ion flow.Ionic liquids (ILs),revealing a tendency to form self-assemblednanostructures, have emerged as promising materials in various applications,especially in energy storage and conversion. Despite multiple reportsdiscussing the effect of structural factors and external thermodynamicvariables on ion organization in a liquid state, little is known aboutthe charge-transport mechanism through the self-assembled nanostructuresand how it changes at elevated pressure. To address these issues,we chose three amphiphilic ionic liquids containing the same tetraphosphoniumcation and anions differing in size and shape, i.e., thiocyanate [SCN] If the ionsare weakly amphiphilic, the bulk structure is determined mainly byCoulombic forces and simple packing.9 Hence,the degree of nanostructure in ILs usually scales with the elongationof a cation alkyl chain, while anions control local structure inthe polar domains. For instance, imidazolium and pyrrolidinium-basedILs develop amphiphilic nanostructure (like micelles) above n-butyl chains.11 However, the geometryof trialkylmethylammonium  cationis sufficient to facilitate the nanostructure formation even withshort ethyl tails.12 These studies emphasizethe importance of the volume ratio of charged and uncharged groups and the relative positions of the alkyl groups on the cation. Inprinciple, for a larger Valkyl:Vpolar ratio, stronger segregation of polar andapolar domains is observed.13 Ions forminghydrogen-bonded networks also support amphiphilic self-assembly.14 Thereby, nanostructures are observed in bothprotic and aprotic ILs.15The nanostructure of IL is driven by the spontaneous solvophobicsegregation of charged and uncharged groups into polar and apolardomains.18 It has been reported that thermal fluctuations makeILs homogeneous fluids without any self-assembly behavior at hightemperatures. On the other hand, cooling brings competition betweenhydrophobic and hydrophilic interactions accompanied by changes inions conformation. Various nanoscale phases arise as the moleculesrearrange in a supercooled liquid state.11 In this context, it should be noted that isobaric cooling decreasesthe kinetic energy and increases the ions’ packing (density),making the self-assembly complex and puzzling. Therefore, the combinedeffect of thermal and density fluctuations must be separated to thoroughlyunderstand IL (nano)structure formation. It can only be achieved byperforming high-pressure experiments.19 Since simple packing constraints determine the general arrangementof polar and nonpolar domains, the effect of pressure on ions rearrangementis naturally expected. Since the pressure-dependent experiments arefar more complex than their temperature counterpart, there are noreports on the behavior of ILs self-assembled nanostructures underpressure. A methodology that can address this challenge employs high-pressuredielectric spectroscopy.20 In additionto insight into dc-conductivity behavior and relaxation dynamics atvarious T–P conditions, isothermaldielectric measurements offer a unique possibility to recognize thedominating charge transport mechanisms in low-molecular ILs and theirpolymer counterparts.24 Thereby, high-pressure dielectric experiments can capture two missingcrucial aspects: ion dynamics through the self-assembled nanostructuresand how it changes at elevated pressure.Over theyears, numerous experimental techniques and theoreticalstudies have been focused on the effect of temperature on the nanoscalemorphology of ILs.666,14]+-based aprotic ionic liquids overa wide temperature and pressure range. Specifically, the + cation has been combined with three structurally differentanions: thiocyanate [SCN]−, dicyanamide [DCA]−, and tricyanomethanide [TCM]−. Accordingto X-ray diffraction and FT-IR spectroscopic studies of borohydrideIL [BH4], presented in ref (T > TLL), the chargetransport is fully controlled by viscosity ,while in a supercooled state revealing self-assembly behavior (T < TLL), ion diffusion ismuch faster than the structural dynamics. Furthermore, the conductingproperties of self-assembled glass differ from typical IL and dependon the thermal history of sample. To disclose the details of the chargetransport mechanism in + materials,high-pressure dielectric spectroscopy has been employed. High-pressureexperiments combined with MD simulations show that the nanostructureof +-ILs depends on the anion sizeand reveals different pressure sensitivities for each material.The current paper discussesthe conductivity mechanism as a functionof the structural organization of three trihexyl(tetradecyl)phosphonium[SCN] and [TCM]is provided in Supporting Information. [DCA] with a purity of 99%was supplied by Iolitec.The synthesisof examined -based IL was quenchedto 201 K and then compressed to the glassy state. Afterward, the temperaturewas increased, and the dielectric data were collected on isothermaldecompression. This procedure enables studies of ion dynamics in theglassy and supercooled liquid 2 state. Further decompression to liquid1 resulted in cold crystallization of [SCN] and[DCA] and required melting of the sample in thesubsequent step.The dielectric measurementsat ambient pressure for studied ILs were carried out over a frequencyrange from 10–1 (10 points per decade) with strainequal to 0.1% in the vicinity of the liquid glass transition. Thestrain was increased by 1 order of magnitude with every 10 K. Therelative uncertainty of the reported viscosity measurements from calibration,temperature, and pressure control, as well as sample purities, didnot exceed 7%.The viscositywas measured byemploying an ARES G2 rheometer. In the supercooled liquid region,aluminum parallel plates of diameter 4 mm were used. The rheologicalexperiments were performed in the frequency range from 0.1 to 100rad·sq range of 0.13–0.8Å, while wide-angle X-ray scattering (WAXS) measurements wereconducted in the q range of 0.6–4.9 Å.The measurements were performed on cooling and subsequent heating,and the two-dimensional data were acquired by using independent detectors.The one-dimensional data were obtained by azimuthal integration usingthe pyFAI program. The temperature of the system was controlled byLinkam, and the cooling/heating rate was set to 10 K/min.To investigate the microscopic structural changesduring LLT on alarger scale, we employed X-ray diffraction (XRD) characterizationwith an energy of 12 keV. Small-angle X-ray scattering (SAXS) measurementswere performed in the 1H spin–latticerelaxation data of [DCA] were collected by a STELARfast field cycling (FFC) relaxometer in the frequency range of 10kHz to 25 MHz at 204 and 201 K. Data analysis has been performed withthe following parameters: ρ = 0.90 7 g/cm3, M = 549.90 g/mol, NH = 6.75× 1028/m3.The Supporting Information.Molecular dynamics simulations of ionicliquids comprised of amphiphile molecules were performed using a bead–springmodel at coarse-grained resolution. Details on the adopted numericalmodel are included in 666,14]+ cation with long alkyl chains hasbeen combined with three anions of different sizes and shapes: a relativelysmall linear thiocyanate, [SCN]−, a larger dicyanamidewith bent geometry, [DCA]−, and the largest, trigonalplanar tricyanomethanide, [TCM]−. Due to the amphiphilicnature of the + cation, the chosenILs are expected to reveal self-assembly behavior. Recent X-ray diffractionand FT-IR spectroscopic studies of the borohydride salt [BH4] confirmed the enhanced ordering of long alkyl chainsin a supercooled liquid state accessible through the first-order liquid–liquidphase transition. Consequently, two liquid phases have been foundwithin single component material: first, at T > TLL (liquid 1) and the second with nanoscalestructuration at T < TLL (liquid 2).The LLT has also been reported for [SCN] and [TCM];26 however, the physicalproperties of [DCA] were studied only at roomtemperature conditions .27 To bridge this gap, first, differential scanningcalorimetry (DSC) studies of [DCA] have been carriedout, and collected thermograms were compared to data recorded for[SCN] and [TCM] in –1 results in a broadexotherm with onsets at 207.4, 209, and 213.3 K for [TCM], [DCA], and [SCN].Subsequent heating performed after the time-dependent isothermal stepat 187 K  revealed the step-like changeof heat capacity, followed by a well-resolved endothermic peak, thefirst indicating liquid-glass transition  and the latter, reversiblewith respect to cooling scan, denoting the first-order liquid–liquidtransition (LLT). More thermal effects were observed upon furtherheating of [DCA] and [SCN]:the onset of cold crystallization (Tc)and subsequent melting (Tm). However,due to the small enthalpy of these events, one can conclude that onlypartial crystallization of these two ILs occurred upon heating ata standard rate of 10 K·min–1. Extending thetime for nucleation and crystal growth by decreasing the heating ratemakes both Tc and Tm more detectable, while the onset of LLT remains the same. The sameexperimental protocol applied to [TCM] shows astrong resistance to crystallization.The materialsexamined hereinwere chosen as model systems to understand the effect of moleculararchitecture and external thermodynamic variables (temperature andpressure) on the charge transport mechanism in amphiphilic aproticionic liquids, revealing self-assembly behavior. Specifically, the[DCA]. As can be seen, the examinedIL shows three characteristic peaks at values of q below 2 Å–1. According to literature reports,28 the most intense diffraction peak near 1.4 Å–1 arises from a short-range separation between counterionscombined with a carbon–carbon interaction from cation alkylchains. The latter indicates that hydrophobic tails of + have significant contact. This intermolecular peakshifts toward higher q values with cooling due tothe increase in density and becomes noticeably narrow when IL entersthe liquid 2 state. At the same time, the intensity of the first diffractionpeak, the so-called prepeak (0.36 Å–1 at RT)identified with long-range anion–anion correlations, becomessmaller with decreasing temperature and the other peak starts to appearon its high-q side when the liquid 2 state is achieved.The latter one, observed finally at around 0.53 Å, can be dueto the separation of ions of the same charge. The subsequent heatingbrings opposite changes in the XRD pattern. However, when the temperaturerises above TLL, the Bragg peaks indicatingcold crystallization of [DCA] appear  were recorded upon heating at ΔT = 1 K intervals, i.e., with the rate of 1 K min–1. A complex electric modulus, M*(f) = ε*(f)−1 = M′(f) + iM″(f), was adopted to analyze the dielectric response of examinedsystems. Representative results for [DCA], inthe form of the imaginary part of the M*(f) function, over a broad range of temperatures, are depictedin fmax, is strongly temperature-dependentand shifts toward lower frequencies with cooling, which is a typicalbehavior of ILs. This indicates slower ion mobility and a longer timescale of charge transport in a given system at lower temperatures.At a specific temperature close to TLLDSC, the amplitude of the M″(f) function decreases slightly and then maintains a newlevel.In the next step, dielectric measurementswere performed to examine the charge transport mechanism across theLLT and near the liquid–glass transition. Two experimentalprotocols have been applied to realize this task. In the first one,the dielectric data were collected on cooling, which allowed for monitoringchanges in ion dynamics during the transition from a simple liquidstate to a nanostructured one. In the second procedure, the ionicliquids were quenched to 187 K (corresponding to a glassy state) inthe dielectric setup, and then frequency scans (10M″(f) peak becomes broader  peak, frequently called σ-relaxation, moves outof the experimental window. Then, a secondary mode characterizingthe dynamics of the glassy state appears. Analogous results have beenobserved on the heating scan ; however, an increase in temperature above TLLDSC caused cold crystallization. Using the sameexperimental protocol, that is, heating of quenched IL, we observedcold crystallization of [SCN] in phase 1, whereasphase 2 was stable. At the same time, both liquid states of [TCM] were thermodynamically stable . These results stay in agreement with previouslydescribed DSC data.At the same time, the see also 3b. Below666,14][DCA] thoroughly, the time-dependentdielectric scans within the T range 199–215K were taken after quench-cooling from room temperature. The procedurefor isothermal dielectric measurements is schematically presentedin 666,14][DCA] were performed. As shown in T in the liquid 1 state. Later on, a decrease of M″ over time takes place. Contrary, phase 2 of [DCA] is a stable liquid without crystallization tendency (see violetlines in Mnorm″(f) has been analyzed in terms of the Avrami–Avramovmodel. The representative Avram–-Avramov plot for crystallizationkinetics at T = 209 K is shown in To characterize the physical stability ofsupercooled liquid 1and liquid 2 states of , at TLLDSC. A substantialincrease in activation energy accompanies this behavior  continues the Arrheniusdependence but with much lower activation energy (see the zoom). Thecrossover of τσ(T–1) visible at Tg is an inherent part ofthe liquid–glass transition and reflects the slowing down ofcharge transport in a disordered solid state.29 However, close inspection of 666,14][DCA], [SCN], and[TCM] is thousands of times faster when comparedto 1000s (log τσ(Tg) = 3) observed for all aprotic ILs examinedso far.30 Specifically, log τσ(Tg) =−0.14SCN, −0.36DCA, and −0.38TCM (for heating scans), and importantly, it is sensitive tothe thermal history of the sample. That is, different values of log τσ(Tg) areobtained for slowly cooled and quenched IL. As shown in the zoomedimage in 666,14][DCA] and [SCN] is cooled slowly, it enters a glassy state at τσ much longer than it is for quenched material. Furthermore,the slow cooling of these ILs brings a glass of much higher apparentactivation energy (σ(Tg) for quenched and slowly cooled [TCM].This suggests that the self-assembly of alkyl chains in -ILs depends on the anion and brings differences in conductivitymechanisms between examined systems. However, fast charge transport,to some extent independent of structural rearrangements, is expectedin supercooled liquid 2 and in the vicinity of the liquid-glass transitionof [DCA], [SCN], and [TCM]. To verify this hypothesis, there is a need fora direct comparison between the time scale of charge transport andstructural dynamics, τα, under the same conditions.Upon vitrification of supercooled liquid 2 taking place at n energy 5b. Thus,666,14][DCA],and the representative results of mechanical measurements are illustratedin G″ forms a well-resolved peak; the intersection of G′(f) and G″(f) gives the structural relaxation time τα (G″(f) peaks are well-identifiedonly within four decades from the liquid–glass transition,the Maxwell relation τα = η/G∞ was employed to convert η(T–1) data to τα(T–1) and thereby probe the structural dynamics ofliquid 1. From 666,14][DCA] follows the VFT law in liquid 1 and steeply increasesat the temperature of LLT: over 8 K, there is a six-decade changein the value of τα. Consequently, recallingcondensed matter physics terminology, one can state that liquid 2is much more fragile than liquid 1. Notably, an opposite conclusionwas drawn by H. Tanaka for molecular liquid TPP,31 who explicitly identified LLT as fragile-to-strong transition.Two experimental techniques were used to determine the time ofstructural motions: temperature-modulated DSC (TMDSC) and rheology.The experiments have been performed for [DCA]. In supercooledliquid 1, these two time scales are nearly identical, implying thatthe charge transport requires the diffusion of entire molecular units.Such a vehicle mechanism characterizes all aprotic ILs studied sofar.32 Upon cooling below TLLT, the time scales of τα andτσ start to diverge: the structural motionsbecome slower than the time scale of conductivity relaxation, andthe most significant difference (decoupling) between these two variables,noted as R, is seen at Tg, where R = log τα(Tg) – log τσ(Tg) = 3.1. Thus, when thestructural relaxation time of the glassy phase is on the order of1000 s, which gives [DCA] mechanical propertiesof a solid, fast charge transport still occurs and takes around 1s (log τσ(Tg) = 0). This decoupling occurs for all three +-based ILs and only slightly depends on the anion size. Sucha phenomenon has never been reported for aprotic ILs or any othermaterial revealing self-assembly or LLT, which raises an intriguingquestion about the mechanism behind this observation.An important conclusion is drawn from the direct comparison ofthe temperature dependencies of τ34 From thispoint of view, [DCA], [SCN],and [TCM] in their liquid 2 state seem to actas single-ion conductors. To verify this hypothesis, we used [DCA] as a reference system and determined the translationaldiffusion of cations in the liquid 2 state using 1H NMRrelaxometry. We found that the Dtrans of+ cations slows below TLL and practically does not change with a further temperaturedecrease. Dtrans at 204 K was found tobe equal to 7.32 × 10–14 m2/s andfluctuates within the error of 2.82 × 10–15 m2/s in the liquid 2 state.Takinginto account the chemical structure of examined ILs andtheir self-assembly behavior, one can expect that in liquid 2, longalkyl chains of cations form a skeleton that contributes substantiallyto structural dynamics. At the same time, anions are free to movethrough created channels and thus are responsible for charge transport.Such a scenario has been previously described for polymerized ionicliquids, in which anions travel easily within channels of the covalentlybonded, cationic polymer backbone.To provide more detailedinsight into charge transport in the studiedsystems, we have performed high-pressure experiments.666,14][DCA] in the pressure range 0.1–200 MPa are shown in Figure S4, while the isothermal conductivityrelaxation times plotted as a function of pressure are presented in τσ–P dependence obtained for [TCM], [SCN], and [DCA]reveals two kinks: first at τσ ≈ 3.5 ms separating two supercooled liquids and, second,being a manifestation of liquid-to-glass transition. Defining PLL and Pg as thepressures at which the log τσ rapidly changes behavior, the TLL(PLL) and Tg(Pg) dependences were obtained for the three studiedILs  for [DCA] has been determined .The experimental protocol of high-pressure measurementshas been described in the 666,14][TCM], both liquid–liquidand liquid–glass transitions occur at isochronal conditions:log τσ =−3.5 and log τσ =−0.5. In [DCA], there is a continuous shiftof τσ(Pg) toward shorter relaxation times with increasing pressure.Finally, in [SCN], there is a clear minimum inthe τσ(Pg) = f(P) dependence, around170 MPa. Furthermore, for every Pg value, τσ is larger than 1000 s. Note thatthe time scale of structural relaxation is isochronal (τα ≈ 1000 s) regardless of T–P thermodynamic conditions.35 Thus, the examined phosphonium ILs are characterized by pressure-tunablefast charge transport, decoupled from structural relaxation, and governedby anion size. It can be speculated that for relatively small anions,[SCN]− and [DCA]−, an increasein pressure results in better packing of cation alkyl chains and consequentlyprovides more channels for anion motions. In this scenario, the diffusionof anions becomes faster at elevated pressures, which is visualizedas a shorter log τσ.However, above the pressure limit of 180 MPa, the [SCN]− slows due to the reduced free volume, Vfree. The same effect is expected for [DCA]; however,it probably occurs above the experimentally available pressure range.Further increase in anion size, in turn, makes the alkyl chain arrangementsmore difficult, which results in irregular channels for anion transport.In this case, anions still move faster than cations, making the systemdecoupled; however, squeezing does not affect it much, making log τσ pressure independent.A closer inspection of Supporting Information for details).41 To mimic the architecture of the phosphonium ILs, a simple bead–springcoarse-grained model was employed, consisting of an amphiphilic moleculecontaining a positively charged head connected to a stiff, neutraltail and a negatively charged free counterion , while anion dynamics is strictly relatedto the IL phase and applied pressure. Larger anions favor isotropicdiffusivity, decreasing with pressure, whereas D– varies nonmonotonically with pressurefor smaller anions. Namely, the random distribution of small cationclusters leads to isotropic diffusivity of anions at low pressure,while lamellar-type phases, obtained at higher pressures, favor anisotropicdiffusivity, making the anions transport 5 times faster along thelamellar plane. A pressure increase brings a further decrease in D– .MDsimulations have been performed to verify the proposed mechanism ofcharge transport  and structural dynamics (τα) shows that charge transport is fully controlled by viscosity inliquid 1, that is, at T > TLL and P < PLL. In contrast, liquid 2 has a nanostructure that facilitates chargetransport decoupled from structural dynamics. Long alkyl chains ofthe cations are partially frozen in nonpolar domains while anionsmove swiftly through the created channels. From this point of view,the supercooled liquid 2 phase in + ILs seems to mimic single-ion conductors such as polymerized ionicliquids. The self-assembled nanostructures of liquid 2, allowing fastion transport, can be fine-tuned by sample thermal history, anionsize, and compression. When quenching and slow cooling are applied,two different glasses differing in the time scale of ion motions canbe obtained from the liquid 2 state. That is, in both disordered solids,the charge transport is independent of structural dynamics; however,in the one obtained by quenching, the decoupling is more pronouncedcompared to the slowly cooled system. This phenomenon, called polyamorphism,observed for the first time in ILs, gives a unique possibility totune the properties of disordered electrolytes. Furthermore, a decreasein anion size brings nonmonotonic behavior of decoupling at elevatedpressure. That is, τσ(Pg) reveals a minimum accompanied by diffusivity changes fromisotropic to anisotropic character, and the latter facilitates aniontransport along the lamellar-type plane. For bulky anions, interconnectedphases with 3D continuous curvature emerge regardless of P and make τσ(Pg) constant. These results pave the way for a better understandingof self-organization in ILs and hence control the charge transportmechanism in ion-containing systems. The self-assembly-based chargetransport mechanism discovered here offers a new approach for fine-tuningtransport properties of ILs and other fluids with ordered nanostructures,which could profoundly impact emerging technologies associated withionic liquids as conductive soft materials.Here, we focused on the charge transport mechanismof three phosphoniumionic liquids comprising the same large amphiphilic cation with long,intertwined nonpolar alkyl chains and much smaller anions, charge-balancedby the cationic phosphonium centers. Our studies reveal that uponisothermal compression and isobaric cooling, the examined phosphoniumILs [P


The remote borylation was directed by 1,5-HAT of the amidyl radical, which was generated by photocatalytic reduction of hydroxamic acid derivatives. The method accommodates substrates with primary, secondary and tertiary C(sp3)−H bonds, yielding moderate to good product yields (up to 92%) with tolerance for various functional groups. Mechanistic studies, including radical clock experiments and DFT calculations, provided detailed insight into the 1,5-HAT borylation process.The borylation of unreactive carbon-hydrogen bonds is a valuable method for transforming feedstock chemicals into versatile building blocks. Here, we describe a transition metal-free method for the photoredox-catalyzed borylation of unactivated C(sp 3)-H bonds, mediated by amidyl radicals to achieve C-H borylation at the λ position of carbonyl compounds.Alkyl boronic esters are commonly used building blocks in organic synthesis, causing efficient C-H borylation methods to be in high demand. Here, the authors develop a method for the photoredox-catalyzed borylation of unreactive C(sp C−H borylation is desirable and powerful because it directly transforms common C−H bonds into synthetically valuable C−B bonds. Borylation of nonactivated C(sp3)−H is one of the most challenging subjects in the area. In the past decades, a series of methods have been established4. and most of them require transition-metals as catalysts, including W5, Rh6, Pd10, Ir24 , and the yields based on diboron are relatively low (lower than 50% in most cases). Given that boronic esters are important in organic synthesis and medicinal chemistry, it is of great significance to develop substrate equivalent and efficient C−H borylation methods.Alkyl boronic esters are highly versatile building blocks in organic synthesis because C−B bonds can be easily transformed into various useful functional groups28. The earliest example of remote C−H functionalization by means of HAT was Hofmann–Löffler–Freytag (HLF) reaction  of N−H bond is higher than the C−H bond. In past decades, the nitrogen-centered radical-based HLF type reaction has shown its advances in regioselective functionalization of inert C−H bonds32, by trapping the carbon-centered radicals towards the formations of C−C34, C−O36, C−N40, and C−X 44 bonds. Particularly, amidyl radical is widely explored nitrogen-centered radical in above mentioned processes. In 2016, Knowles45 and Rovis46 independently developed oxidative photocatalytic generation of amidyl radicals by direct N−H bond cleavage, after 1,5-HAT process, followed by the Michael addition of the translocated carbon-centered radical, affording the remote functionalized amides phthalimides49, N-aminopyridinium salts53, and hydroxylamides58 give the corresponding amidyl radicals upon heterolytic cleavage of the respective leaving groups  is a general strategy in organic synthesis, and it represents an attractive approach toward remote C−H functionalization63. We therefore turned our interest towards the use of amidyl radicals for remote C−H bond (γ-position of a carbonyl compound) borylation reaction −H bonds initiated through 1,5-HAT.In our previous studies, we focused on developing transition metal-free radical borylation reactionsion Fig. d. With aN-radicals under mild conditions. With their weak N−O bond, hydroxylamine derivatives have recently been exploited as nitrogen-centered radical precursors in visible-light photocatalysis, given their higher oxidation state, it would be necessary to apply reductive conditions57. Thus, 4-trifluoromethylbenzoyl hydroxamic acid derivative (1a) was used as the model substrate to establish optimal reaction parameters, including the equivalency of bis(catecholato)diboron (B2cat2), the choice of photocatalysts , light sources, and additives (Table p-CF3ppy)3 (2%) as photocatalyst, 40 W 456 nm light source, Et3N as additive, the desired product was obtained in 45% GC yield. Other amines, such as DABCO, DIPEA or no additive (entry 2–4), were tested, and DIPEA gave improved yield (47%), almost same yield was obtained with a longer reaction time (30 h) (entry 5). Replacing photocatalyst to Ir(ppy)3, [Ru(bpy)3]Cl2, or 4CzIPN could not give the improved results (entry 6–8). Gratifyingly, the reaction gave 47% yield by using Eosin Y, which is a non transition metal photocatalyst, and importantly, much cheaper than Ir(p-CF3ppy)3 (entry 9). Furthermore, We examined the influence of the light source. 467 nm wavelength LED turned out to be better than 456 nm LED, leading a jump increasing yield . Higher equivalent photocatalyst (5%) resulted in 77% yield (entry 11). Reducing the B2cat2 loading from 3.0 to 2.5 equiv. led to a slightly improved yield , and 4.0 equiv. B2cat2 gave significantly lower yield (entry 13), which showed that the product yield is not always proportional to the equivalent of borylating reagent. Decreasing the loading of additive DIPEA, the yield dropped to 54% (entry 14). Control experiments revealed that no borylation product formed in the absence of photocatalyst, light or additive (entry 15–17). To rule out the potential for substrate thermal decomposition, we conducted a control experiment by replacing the light with heating at 50 °C for 12 h in the absence of light. However, no borylation product was detected under these conditions (entry 18). Continuous irradiation is necessary, as turning off the light after 30 min resulted in no product formation (entry 19). The standard reaction in air gave a 76% yield, indicating that the reaction is not sensitive to oxygen (entry 20).Recently, visible-light photoredox catalysis has been the leading efficient access to rivative a was userivative a was use3)-H borylation. As summarized in Fig. γ-position all gave borylation products in good to excellent yields (2a–2d), while the reactions of the substrate possessing secondary C−H bonds could also afford moderate yield . Substrates containing tertiary C−H bond in cyclic structure were also tested, and cyclopentyl (2f) and cyclohexyl (2g) products were formed in 83% and 58%, respectively. Specially, we found that this method can activate secondary C(sp3)-H bonds in cyclic substrates, producing the corresponding syn product 2h with excellent diastereoselectivity. Moreover, for bicyclic substrate which have more than one γ-positions, such as norbornyl derivative, high site-selectivity was obtained  , albeit the undesired hydrogenation by-products accompanied. It may cause by replacing the N-connected alkyl group from tertiary one to secondary one, which weakens the N−H bond, and the 1,5-HAT process was affected thermodynamically . Interestingly, we found that a benzene ring can be involved in this six-membered ring 1,5-HAT process, as exemplified in the cases of (2p-2u). For isolation of these products, we found the partial instability of the benzylic pinacol boronic esters in silica gel, and that in situ oxidation with H2O2 allowed isolation of the corresponding alcohols in high yields. Various functional groups such as methyl(2q), methoxy (2r), chlorine (2s) and bromine (2t) were well tolerated, and the yields were up to 92%. For secondary benzylic substrates(2u) or benzylic position on the carbon chain substrates(2v-2w), the products were obtained in good yields. α-hydroxy hydroxamic acids derivatives were also tested, including methoxy (2x), acetoxy (2y). Amino acid leucine-derived hydroxamic acid also underwent this remote borylation, giving the desired product in 45% (2z). Our method can also work on complex molecules, such as lithocholic acid and dehydroabietylamine derivatives (in the cases of 2aa and 2ab). It is worth noting that the last example (2ab) in the substrate scope is a different type other than the rest ones. The borylation of C−H bond took place on the alkyl chain of the amine site.With the optimal reaction conditions, we next sought to explore the substrate scope of this visible light photoredox-catalyzed remote Csp-H borylat yields a–2d, whi3 as a white solid, instead of the desired borylation product  from ultraviolet-visible absorption spectroscopy  was added to standard conditions, and the reaction yielded 16% of the corresponding TEMPO-trapped product uct Fig. a. Secondeld Fig. b. These 69. In this method, the radical trapping product is a bench-stable terminal alkene that can be analyzed by mass spectrometry (MS). To further testify the 1,5-HAT process, we chose CHANT as radical trapping reagent, and carried out the reaction in standard conditions  and F2 (m/z 181.1336) which contain two nitrogen atoms, could only be attributed to the trapped R1, the fragments showed that the acyl group RCO was dissociated. The most strong-intensity peaks F3 (m/z 264.1957) is a stable acylium ions, and F4 (m/z 236.2008) is F3 losing CO. They could only be attributed to the trapped R2, and many other strong peaks could also be attributed to it 2− to excited singlet state −1 72. Then this singlet state transforms to its more stable triplet counterpart 73. Next, this triplet state species is oxidatively quenched by 1a to give EY− and 1a−, which further undergoes N−O bond cleavage to form a nitrogen-centered radical, accompanied with the leaving of −1) to the six-member ring transition state HAT_TS, which allows the hydrogen atom to transfer from C atom to N atom to give the C_radical, this step is quite favorable thermodynamically. Subsequently, the C_radical is borylated by diboron to give the desired product59.To better understand the photoredox mechanism of this 1,5-HAT process, we carried out density functional theory (DFT) calculations , leading to the formation of a certain amount of hydrogenation product.Similar behavior was observed for secondary C−H bond substrates, where the activation energy increases from 7.7 to 10.4 kcal mol−1 (II). For the primary C−H bond substrates, the barrier is 13.1 kcal mol−1 (I), the reaction all gave hydrogenation products and no borylation products were obtained.As shown in substrate scope studies, it is noted that the borylation reactivity is in a order of tertiary C−H bond substrates > secondary ones > primary ones. For tertiary C−H bond substrates, the reactivity of the 1a was carried out under air -H borylation reaction, which enabled by 1,5-HAT process based on hydroxamic acid derivatives. A series of mechanism experiments, as well as DFT calculations resolved the radical mechanism in detail. This investigation afforded remote C−H bond functionalization of γ position of carbonyl compounds, thus provided scarce synthetic method of such type. We anticipate that this approach will find broad applications in synthetic community.In conclusion, we have developed a transition-metal free visible light photoredox-catalyzed remote C, the substrate , Eosin Y  and 3 mL DMA to a 10 mL Schlenk tube with a stir bar, followed by DIPEA . The capped Schlenk tube was removed from the glovebox, and the reaction mixture was irradiated by 467 nm Kessil 40 W LED at a 3 cm distance for 12 h, with a fan for cooling. As the catechol boronate esters are sensitive to hydrolysis, RBcat was transformed to RBpin for isolation. After cooling to room temperature, pinacol  and 1.0 mL triethylamine were added, and the mixture was stirred at room temperature for 1 h. Water was added to the reaction mixture, the aqueous layer was extracted with EtOAc (20 mL × 2). If phase separation was slow, brine was added. The organic phases were combined and washed with 50 mL of saturated brine, the organic phase was dried over anhydrous sodium sulfate and filtered, then concentrated under reduced pressure, purified by column chromatography to give the product. The product was monitored by thin-layer chromatography with phosphomolybdic acid stain.In a glovebox under a nitrogen atmosphere, sequentially added BSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2