Microglia, the brain-resident immune cells, are highly ramified with dynamic processes transiently contacting synapses. These contacts have been reported to be activity-dependent, but this has not been thoroughly studied yet, especially in physiological conditions. Here we investigate neuron-microglia contacts and microglia morphodynamics in mice in an activity-dependent context such as the vigilance states. We report that microglial morphodynamics and microglia-spine contacts are regulated by spontaneous and evoked neuronal activity. We also found that sleep modulates microglial morphodynamics through Cx3cr1 signaling. At the synaptic level, microglial processes are attracted towards active spines during wake, and this relationship is hindered during sleep. Finally, microglial contact increases spine activity, mainly during NREM sleep. Altogether, these results indicate that microglial function at synapses is dependent on neuronal activity and the vigilance states, providing evidence that microglia could be important for synaptic homeostasis and plasticity. Microglia survey the parenchyma, which leads to morphology changes over time. Here the authors show using 2 photon imaging of microglia in vivo that sleep modulates microglial morphodynamics through Cx3cr1 signaling. In the adult brain, microglia play a central role in learning-induced synapse formation by the secretion of brain-derived neurotrophic factor6. Moreover, they may enhance synaptic activity, maintain brain network synchronization within a physiological range and prevent hyperexcitability9. These synaptic functions are critically dependent on microglial morphodynamics, which refers to the changes in microglial morphology and motility. The regulation and functional properties of microglia-synapse interactions are a subject of intense research. Several studies have reported that global and local increase in neuronal activity leads to enhanced process motility, with process extension towards active sites and increased contact duration with neuronal elements13. The best-described mechanism involves microglial P2Y12R-mediated process extension, driven by activity-dependent ATP/ADP release at sites of increased activity14. Other mechanisms modulating microglial dynamics may include changes in potassium conductance, Cx3cr1/fractalkine signaling, as well as neuromodulator release, in particular norepinephrine and serotonin19. However, decreases in neuronal activity by sensory deprivation, optogenetic and pharmacological inhibition of neuronal activity have yielded inconsistent results on microglial dynamics and microglia-neuron interactions potentially due to the model and methods used and the activation state of microglia23. Nevertheless, stimulating or inhibiting neurons results in distinct changes in microglial gene expression and Ca2+ activity in microglial processes, both in favor of microglial sensing of neuronal activity24.Microglial cells are the resident immune cells of the central nervous system. In the healthy brain, they perform many physiological tasks essential for neuronal homeostasis and synaptic functions. During development, they participate in shaping neuronal connectivity by phagocytic and non-phagocytic refinement of neuronal circuits, selective synaptic strengthening, and filopodia formation27. Despite evidence for microglial sensing of neuronal activity and synaptic pruning in physiological conditions, the impact of the sleep-wake cycle on microglial dynamics and functional properties has not been elucidated yet. In awake mice, noradrenergic tone was recently found to suppress microglial process area and surveillance territory when compared to anesthesia18, but a clear description of the microglia process dynamics during sleep and the underlying regulation is lacking. Although the exact function of sleep is still elusive, there is a strong consensus as to its role in synaptic homeostasis and plasticity. Indeed, sleep and wake are important for learning and memory consolidation and are associated with molecular mechanisms of synaptic plasticity that may involve microglial cells29.Major changes in neuronal activity in physiological conditions occur during the sleep-wake cycles. In addition, vigilance states are associated with distinct neuromodulation and ion/purine concentrations that may impact microglial morphodynamicsTo better understand microglial morphodynamics in physiological conditions at the network and synaptic level, we combined electrophysiological recordings in vigil head-restrained mice with two-photon in vivo imaging of microglial cells and neuronal activity. Our results show that global microglial motility and complexity are overall correlated with changes in neuronal activity and that change in activity at the spine level impacts the proximity and contacts of microglia processes with synapses. These relationships between neuronal activity and microglia morphodynamics were also found during the alternance of wake and sleep and are dependent on Cx3cr1 signaling. At a single-spine level, microglial processes are attracted towards active spines during wake, whereas microglial proximity and contact with spines are downregulated during NREM sleep, in a state-dependent manner. Finally, we found that microglial contact with spines resulted in an increase in spine activity which was mainly observed during NREM sleep.Cx3cr1+/− mice using two-photon microscopy while simultaneously monitoring brain activity with electroencephalogram (EEG) recordings . To test whether this profile may be observed by a random distribution of values for contact duration and spine activity, we performed the same analysis with scrambled original data. We did not find any correlation for this random data set , indicating that microglial contact with spines is activity-dependent.To assess whether local spine activity affects microglia-spine interaction, we studied the correlation between the activity of the spine and the duration of microglia-spine contact. We found that this correlation was positive and that microglial processes stayed longer in contact with highly active spines . Because of the polysynaptic nature of the network, we found that stimulations of the whisker induced three different types of response , 24.9% of spines by a decrease of activity  while 35.8% of spines did not respond significantly to whisker stimulations . We found that the contact duration between microglial processes and spines was longer for the responding spines compared to decreased activity spines during stimulations  while not being different during baseline . For the 147 spines that responded by an increased activity we found that the proportion of spines contacted by a microglial process at a given time increased by 7.9 ± 3.2 % after 10-15 min stimulation when compared to baseline  and the spines spent on average 12.7 ± 4.4% more time after 10–15 min of whisker stimulations . On the contrary, for the 93 spines that exhibited a decreased activity during simulations, we found that the proportion of spines in contact during stimulation  as well as the percentage of time that spines spent in contact with microglial processes decreased by 11.2 ± 4.8% and 13.3 ± 5.8%, respectively 10–15 min following the onset of the stimulation . Finally, when measuring the relative distance of the closest microglial process with respect to spines, we found that microglial processes were closer during stimulation for spines that responded by an increase of activity and further away during stimulation for spines that responded by a decrease of activity , while no difference was observed between the groups during the baseline period . It is interesting to note that for spines which did not exhibit a response to the stimulations, no change was observed in the proportion of spines being contacted, the percentage of time spent in contact, and the distance between the closest microglial process and the spine . Altogether these results indicate that modulation of neuronal activity induces changes in the proximity of microglial processes with respect to spines, the proportion of spines being contacted by a microglial process, and the contact duration.To strengthen our observations and provide a causal evidence of the relationship between microglial dynamics and neuronal activity, we decided to modulate neuronal activity by stimulating the C2 whisker Fig.  while mop < 0.05) and motility  were significantly reduced during NREM sleep compared to wake. Of note, the average proportions of NREM sleep and wake were comparable for the recordings chosen for analysis , and one episode of a given vigilance state lasted on average 5.86 min ± 0.46. Further details about the characteristics of the sleep-wake episodes in our recordings can be found in Supplementary Figs. The vigilance states are physiological conditions during which activity is drastically changing. To examine whether microglial complexity and motility are influenced by the change of neuronal activity associated with the vigilance states, we performed two-photon imaging of microglial cells while simultaneously scoring the vigilance states through EEG and EMG recordings. Each session included several episodes of wake and NREM sleep Fig.  during wThese results indicate that microglia are sensing the vigilance states, possibly through the change of neuronal activity associated with a given vigilance state, and adapt their morphodynamics accordingly.30, we next investigated whether the invalidation of the Cx3cr1 gene could affect the morphodynamic changes associated with the vigilance states. To do this, we performed in vivo two-photon imaging experiments using Cx3cr1eGFP/eGFP mice in which Cx3cr1 was knock out on both alleles (Cx3cr1−/−) mice that we compared to Cx3cr1+/eGFP that we will refer to later on as Cx3cr1+/− in the text. When monitoring microglial motility and complexity during the alternation of the vigilance states, we found that microglia from Cx3cr1−/− mice did not exhibit changes of complexity nor motility associated with the shift of vigilance states as it was the case for Cx3cr1+/− mice . This result suggests that Cx3cr1 is involved in the regulation of microglial morphodynamics associated with the vigilance states. Because the deletion of the Cx3cr1 gene in microglia could alter the quality of sleep, we analyzed and compared sleep episodes between Cx3cr1−/− and Cx3cr1+/− mice. Overall, the percentage of time spent in NREM sleep, the duration, and the number of NREM sleep episodes were not significantly different between groups . Another possibility explaining the absence of motility change in homozygous mice could be that the neuronal activity differs from the one monitored in heterozygous mice. To test that hypothesis, we compared the EEG power spectrum of Cx3cr1−/− and Cx3cr1+/− mice and found no differences in the amplitude-frequency plots , indicating that global cortical activity was similar between Cx3cr1+/− and Cx3cr1−/− mice. Finally, we looked at the cross-correlation between EEG power and microglial complexity or motility and we found that the correlation was weaker in Cx3cr1−/− when compared to Cx3cr1+/− mice . This indicates that the relationship between neuronal activity and microglial morphodynamics is lost when the Cx3cr1 receptor is absent. Altogether these results indicate that the Cx3cr1 signaling is important in the regulation of microglial cells’ morphodynamics with regards to neuronal activity.Since Cx3cr1 has been shown to be involved in the motility of microglial cellsp > 0.05, Supplementary Fig. p > 0.05). However, by investigating the distribution of microglial processes around spines during the different vigilance states, we found that microglial processes were closer to spines during wake . Furthermore, microglial processes spent slightly more time in contact with spines during wake when compared to NREM sleep . This observation holds true whatever the vigilance state during which the contact was monitored . Furthermore, microglial processes were closer during wake both for spines mostly active during wake and spines mostly active during NREM sleep, suggesting that this relationship is impeded during NREM sleep . Spine activity was well correlated with microglia-spine contact duration for spines active during wake, but not as well for spines active during NREM sleep . In addition, the contact duration was reduced during NREM sleep for spines active during wake , suggesting a global impact of neuronal activity on microglial morphology and its degree of ramification. On the other hand, motility changes were not as well correlated between microglial cells, indicating that motility could be regulated at the local level. Indeed, spontaneously active neurons, HFS, or trains of AP in a single neuron may recruit microglial processes potentially at the synaptic level32. In our study, we found that microglial processes spent more time in contact with spines displaying high Ca2+ transient frequencies  fluctuate at the cortical level during the vigilance states38. Increase in ionotropic GABA transmission, found during sleep, resulted in significant reduction of microglial motility and morphology in retinal explants, which is in line with our results39. During wake, among other NTs, cortical norepinephrine and serotonin are increased41, but these have divergent effects on microglial cells. Norepinephrine caused microglial process retraction42, whereas serotonin has chemotactic effect on microglial processes44. Thus, the effect of the complex neuromodulatory environment of the sleep-wake cycles on microglial morphodynamics needs to be further assessed. Besides NTs, several ions exhibit state-dependent changes of concentration26; for instance arousal is associated with a rapid rise in [K+]e, along with a slower decrease in [Ca2+]e and [Mg2+]e26. Baseline microglial morphodynamics is regulated by changes in [K+]e15, however, the [K+]e change required to induce microglial dynamics15 is far beyond the slight [K+]e shift26 observed during the vigilance states. Therefore, changes occurring in the extracellular space during the vigilance states are multiple and the exact mechanisms influencing microglial morphodynamics remain to be determined.Our study is the first to examine microglial morphodynamics during physiological sleep. Two recent studies have suggested that microglial cells exhibit a relatively low process surveillance in awake conditions, mediated by a high noradrenergic tone45, while in the hippocampus Cx3cr1 was shown to be involved in synaptic functions46. In our study, we explored the involvement of Cx3cr1 signaling in microglia-neuron communication  that we used for our experiments, the quantity of Cx3cr1 is lower than in wild-type mice, which could impact the physiology of the mouse. Second, in Cx3cr1−/− mice, the expression of eGFP is twofold when compared to heterozygous mice. This has been taken into account when performing imaging as we reduced the laser power to reach an eGFP signal equivalent to the one measured in heterozygous mice. This increased expression of eGFP should not interfere with the effects we are reporting as we compared morphodynamics between wake and sleep within the same mouse.Several studies have investigated the role of Cx3cr1 and various results were found depending on the area investigated. In the visual cortex, Cx3cr1 did not seem to be required for activity-dependent plasticity12. Elucidating neuronal activity during sleep has been an open question for many years. Contrary to Ca2+ transients in the soma, when calcium dynamics are monitored at the dendritic level, the decrease in calcium activity was not observed during NREM sleep47. In a similar manner, our monitoring of calcium activity in spines indicates that the frequency of Ca2+ transients does not change between wake and NREM sleep at the spine level. Furthermore, NREM sleep is composed of different EEG rhythms: slow-wave (0.5–4 Hz) and spindle-rich activity (9–16 Hz), that have profoundly different activity and possible impact on microglial cells. Even though we report decreased microglial morphodynamics during NREM sleep, the impact of the different stages of sleep may not be straightforward and needs to be assessed more closely in future studies using a better temporal resolution. Nonetheless, we found that microglial processes were overall closer to spines and spent more time in contact with spines active during wake  under the control of the Cx3cr1 promoter53, while Cx3cr1eGFP/eGFP mice (Cx3cr1−/− in the text), in which Cx3cr1 was knocked out on both alleles, were used to image microglial morphodynamics when Cx3cr1 signaling is invalidated. For microglial dynamics and spine activity analysis, Cx3cr1CreERT2+/− mice6 were crossed with ROSA26-STOP-tdTomato−/− mice, to generate Cx3cr1CreERT2+/−ROSA26-STOP-tdTomato−/− mice. All transgenic mice were 6 to 10-week-old males, derived from the C57BL/6J strain. Mice were housed in enriched and ventilated cages with bedding and running wheels, at 22 ± 2 °C under 12/12 h light/dark cycle (light onset at 7:00 a.m.) and were given access to food and water ad libitum at ALECS, SCAR, and ANIPHY Facilities. All animal procedures were conducted in accordance with the Guidelines of the Animal Care Facility of University Claude Bernard Lyon 1 and were approved by French Ministry of Agriculture  and Local Ethics Committee CE2A55.Microglial motility and complexity imaging was performed in six to ten-week-old male oC) at a concentration of 20 mg/ml. Six to seven-week-old Cx3cr1CreERT2+/−ROSA26-STOP-tdTomato−/− mice were injected subcutaneously in the thigh at 0.4 mg/g, twice, 48 h apart.Tamoxifen  was dissolved in warm sterile olive oil  and injected 500–700 nl of AAV1.Syn.GCaMP6m.WPRE.SV40  at a speed of 0.1 µl/min. For injection, we used a glass pipette with a 20 µm diameter tip which was maintained in the brain for 10 additional minutes to avoid backflow. A cover glass was finally set on top of a thin layer of cyanoacrylate glue over the thinned skull.Mice were deeply anesthetized with isoflurane  and mounted in a stereotaxic frame (D. Kopf Instruments). Isoflurane anesthesia was maintained at concentrations of 1–3% during the surgical procedure. To reduce postoperative pain and inflammation, we administered Carprofen (5 mg/kg s.c.) before surgery, and for two consecutive days following surgery. After the skull was thoroughly cleaned and exposed, two EEG screws were inserted in the frontal and parietal cortex of the right hemisphere and two EMG electrodes were inserted in the dorsal neck muscles. A custom-designed 0.5-mm diameter cranial implant was firmly glued on the left hemisphere using acrylic-based dental adhesive resin cement . The skull was carefully thinned over the somatosensory cortex using a high-speed dental drill until reaching 20–30 µm bone thickness. Most of the drilling was performed in cold, sterile saline solution which allowed continuous cooling and humidification of the bone to avoid heat-induced tissue injury, desiccation of the bone, and inflammation. To visualize CaFollowing surgery, mice were left to recover for one week. Mice were then subjected to daily head-restrained and experimental environment habituation sessions for imaging. The duration of the training sessions increased progressively from 10 min to 4 h. At the beginning and end of each session, mice were rewarded with several drops of sweetened concentrated milk to associate the head restraint condition with a positive experience.The EEG/EMG was monitored and continuously recorded using a differential amplifier . EEG and EMG signals were sampled at 1 kHz and band-pass filtered with 0.5–300 Hz and 10–500 Hz, respectively, and recorded via Prairie View . EEG/EMG data were analyzed using a custom Matlab  script. EEG power as well as EMG power spectra were calculated over a 4s-width sliding time window. Wake state criteria were: Increased EMG and high theta/delta ratio (>2). NREM sleep criteria were defined by low EMG activity power and high delta/theta ratio (>2), while REM sleep criteria consisted of high theta/delta power ratio (>4) and muscle atonia, characterized by an almost null EMG power. Sleep and wake episodes were defined using a manually established EMG threshold to fit with expert scoring. Episodes containing >90% and <25% of wake time were recognized as wake and sleep respectively.Ultima two-photon laser scanning microscope  with a mode-locked Ti:Sapphire laser  and 20× water-immersion objective (0.95 N.A. Olympus) were used. Fluorescence was detected using a 560 nm dichroic mirror coupled to 525/50 nm and 650/40 nm emission filters for eGFP and tdTomato, respectively. Laser power during imaging was maintained below 20 mW.For 3D imaging of microglial cells only, the laser was tuned to 900 nm due to the excitation wavelength for eGFP. Microglial cells were imaged in the somatosensory cortex at a depth of 60–150 µm from the cortical surface. The imaging area was 200 × 200 µm sampled on 512 × 512 pixels i.e., pixel size of 0.38 µm. Z-stacks containing 25–35 consecutive images were acquired every 30 s, with a step size of 1 µm/optical section. A typical recording lasted 30–35 min. All imaging sessions were performed at the beginning of the light phase, corresponding to the maximum sleep pressure.3D imaging of microglial dynamics and spine calcium activity was performed using a laser tuned to 980 nm for simultaneous excitation of both fluorescent proteins eGFP and tdTomato. Imaging was performed in the L1 of the somatosensory cortex at a depth of 60–120 µm from the cortical surface because of higher spine density. The imaging area was 100 × 100 µm sampled on 256 × 256 pixels i.e., pixel size of 0.4 µm. Z-stacks containing 14–16 consecutive images were acquired every 7 s, with a step size of 1 µm/optical section. A typical recording lasted 35 min (300 Z-stacks).For Microglial dynamics and calcium imaging experiments during whisker stimulation, 3D imaging of microglial dynamics was performed using two laser lines: a first laser line tuned to 1040 nm for excitation of the fluorescent protein tdTomato, and a second laser line tuned to 950 nm for excitation of the fluorescent protein eGFP. This configuration was chosen to minimize tdTomato bleaching. Imaging was performed in the L1 of the C2 barrel cortex at a depth of 60–120 µm from the cortical surface because of higher spine density. The imaging area was 100 × 100 µm sampled on 256 × 256 pixels i.e., pixel size of 0.4 µm. A resonant scanner and a 400 µm piezo controlling device allowed us to perform Z-stacks (14 planes) acquisitions every 3.4 s, with a step size of 1 µm/optical section. To partially compensate for the difference in temporal resolution, we acquired four Z-stacks of the activity channel for every Z-stack of the microglia channel. A typical recording lasted ~35 min (650 Z-stacks). Baseline was imaged for 10 min, followed by a period of 25 min during which whisker stimulations were performed.54. The C2 whisker was inserted into a glass pipette fixed to the piezo blade, and we made sure that the whisker was still in the glass pipette at the end of each imaging session. Contact durations were measured on contacts happening during the stimulation period. To take into account the contacts which lasted longer than the 20 min period of stimulation we kept the contacts which lasted during all the stimulation period and started before the onset of the stimulations.Whisker stimulations were performed using a piezoelectric blade (PL140.11 PI Ceramic®) driven by a home-made microcontroller device. Stimulations were triggered each 30 s and lasted for 1-s at a frequency of 90 Hzx, y, and z planes during time-lapse image acquisition. Each volume was registered to the first volume chosen as reference. The shift measurement was obtained from volumes’ cross-correlation calculated by fast Fourier transform. When animal movements were too important for automated registration, the time points were removed from the analysis.Image processing and analysis were performed using custom-written Matlab scripts and LabVIEW. Brightness and contrast, as well as intensity normalization and noise filtering were performed before 3D co-registration calculated by triple Fourier Transform. The microglial cell Z-stacks were then corrected for drift and movements in the 55 for each time point. The Hausdorff dimension is a measure of roughness also called fractal dimension D. This measure corresponds to the slope of the log-log characteristic function, drawing the number of tiles of size ε needed to cover all the surface of the object of interest. To analyze microglial motility, we performed subtractions between two consecutive Z-stack projections. For visualization purposes, the lost and gained pixels between two consecutive time steps were pseudo-colored in magenta and cyan respectively, resulting in images where magenta and cyan points represented lost and newly formed processes, respectively. The total number of pixels of the absolute difference of two consecutive images determined the global motility coefficient (arbitrary unit).For analysis of microglial motility and complexity during wake and sleep experiments, we manually delimited and cropped regions of interest containing the totality of one microglial cell from the 200 × 200 µm field of view using ImageJ . Standard deviation intensity projections from the Z-stacks were performed to generate 2D time-lapse movies. To assess the complexity of microglial cells during wake and sleep, we converted the projections into binary and calculated the Hausdorff fractal dimensionDue to the arbitrary range of values for these parameters, after subtracting the median of the data for each cell, the cells were normalized for each parameter to fit a scale from +100 to −100. Thus, the data are all in the same range and their dynamics are comparable. Microglial complexity and motility index during wake and sleep were obtained by averaging the normalized values during wake and sleep for each imaging session.2+ level for each spine. We found the Z position containing the three main planes of the spine volume (3 µm volume), and performed a Z maximum intensity projection (MIP) time-series of these planes. On the MIP time-series, we delimited a 15 × 15 µm region of interest (ROI) centered on the spine, and generated a new time-series containing only the ROI. Then, we constructed a microglia time-series with 7–9 planes with the same ROI size encapsulating the spine of interest. Finally, standard deviation intensity (STD) projections of the microglial cell images were performed. The maximum intensity and standard deviation projections were used for further analysis of spine activity and microglial dynamics respectively. For most of the microglia-spine analyses, we used 11–22 spines/mouse from five mice. For spine activity during sleep and wake, with and before/after contact, we used 10–18 spines/mouse from five mice. Spines that were not in contact with microglial processes during sleep or wake were excluded from analyses (n = 16). For microglial proximity with dendritic spines, we used 7-15 spines/mouse from 5 mice, including spines that were never contacted by microglial processes. All spines that were very close to microglial cell bodies or primary processes were excluded from the microglia-spine distance analysis.For analysis of microglial dynamics and spine calcium imaging during wake and sleep experiments, we first localized the spines in the xy plane using a maximum intensity projection in time (MIPT). Then, we localized the time point corresponding to the highest intracellular Ca2+ events were defined when the fluorescence intensity exceeded 0.85*std above the baseline. This threshold was determined by visual inspection for several spines allowing us a good detection of the Ca2+ transients.To quantify calcium activity in spines, we filtered the signal to remove background noise and measured the fluorescence intensity at the center of the spine and calculated the mean (baseline) and the standard deviation of the fluorescence intensity over time. Significant CaTo measure the distance and contact duration between microglial processes and dendritic spines, we used a custom Matlab script that plots the distance between the center of the spine and the moving front of the closest microglial process  for spines mostly active during sleep, the ratio of Ca2+ events between sleep and wake was >1.3 and 3) intermediary spines, active both during wake and sleep, with a ratio of Ca2+ events <1.3. For illustration purposes, brightness and contrast of the images were adjusted.To study microglial dynamics with regards to activity during sleep or wake, we classified spines in three categories depending on their level of activity during different vigilance states  and the standard deviation of the fluorescence intensity over time. Significant Ca2+ events were defined when the fluorescence intensity exceeded 0.3*std above the baseline. This threshold was determined by visual inspection for several spines allowing us a good detection of the Ca2+ transients. The Ca2+ transient response to the stimulations was expressed as the ratio of the area under the curve  prior to and after the stimulus onset over 10 min. Individual calcium transients occurring within 6 s following the stimulation were considered as successful evoked responses. The stimulation success rate has been calculated as the ratio of the number of successful stimulations vs the total number of stimulations. A custom Matlab script that calculates the distance between the center of the spine and the moving front of the closest microglial process was used to measure the distance and contact duration between microglial processes and dendritic spines. Since the size of the spine is variable, the threshold for microglia-spine contact was defined by visual inspection. Physical contacts were defined as a distance lower than 1 pixel between microglial processes and dendritic spines in more than 2–3 consecutive focal planes. The total contact duration was the cumulative time step during which contact was observed.For analysis of microglial dynamics and spine calcium imaging during whisker stimulation the image stacks were processed as described above. From three mice in which nine acquisitions were performed, a total of 20–70 spines/acquisition were analyzed, representing a total of 374 spines. All spines that were very close to microglial cell bodies or primary processes were excluded from the microglia-spine distance analysis as it would affect measurements. Likewise, spines selected for the analysis had to be round-shaped of 1–2 µm in 2+ activity (∫ΔF/F0dt) during the 10 min following the stimulation onset is >1.5 fold the baseline activity was considered as spines responding by an increased activity; 2) Spines displaying a ratio <0.6 were considered as decreased activity spines; 3) Spines exhibiting a ratio in [0.6–1.5] range were considered as stable spines. The thresholds for this classification were established in order to have clear responses to stimulation, homogeneous groups, and blind with respect to microglial contacts and durations of contacts.Spines were categorized into three classes based on the change of activity during the stimulation period: 1) Spines for which the CaCx3cr1+/− and homozygous Cx3cr1−/− mice was obtained using a Mann-Whitney test. For EEG spectra comparison between Cx3cr1+/− and Cx3cr1−/− mice, two-tailed unpaired t-test was performed. For cross-correlation analysis between Cx3cr1+/− and Cx3cr1−/− mice, the peak amplitude P  was compared to baseline B (considered from −1000 to −500 s and from 500 to 1000 s as these were considered too far to induce any effects) with Wilcoxon test. For experiments assessing the relationship between microglial dynamics and spine activity, n-values represent individual spines, and we used two-tailed paired t-test or unpaired t-test where appropriate, one-way ANOVA for repeated measures over time, Kolmogorov-Smirnov test for cumulative distributions and Pearson’s correlation test for correlation analyses. Grubbs’ test was applied to identify outliers, and single outliers were removed from the analysis. All values reported are mean ± SEM. A significance p value threshold p < 0.05 was chosen for all analyses.All statistical analyses were performed using Matlab and the Prism V statistical analysis software . Microglial complexity and motility during the vigilance states were compared by using the non-parametric Wilcoxon test where n-values represent individual animals. Sleep/wake cycles comparison between heterozygous Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Cardiorespiratory fitness (CRF) is rarely measured in population studies. Most studies of CRF do not examine differences by population subgroups or seasonal trends. We examined how estimated CRF levels vary by anthropometric, sociodemographic, and behavioral characteristics in a population-based cohort of UK adults (the Fenland Study).estimated) in 5976 women and 5316 men, residing in the East of England. CRFestimated was defined as estimated maximal oxygen consumption per kilogram total body mass (V̇O2maxtbm) and fat-free mass (V̇O2maxffm). Descriptive statistics were computed across anthropometric and sociodemographic characteristics, and across the year. Progressive multivariable analyses were performed to examine associations with physical activity energy expenditure (PAEE) and body mass index (BMI).We used a validated submaximal exercise test to obtain CRF estimates (CRF2maxtbm was lower in women (35.2 ± 7.5 mL·min−1·kg−1) than men (41.7 ± 7.3 mL·min−1·kg−1) but V̇O2maxffm was similar . CRFestimated was inversely associated with age but not after adjustment for PAEE. People in more physically demanding jobs were fitter compared with those in sedentary jobs, but this association was attenuated in women and reversed in men after adjustment for total PAEE. Physical activity energy expenditure and BMI were positively associated with CRFestimated at all levels of adjustment when expressed relative to fat-free mass. CRFestimated was 4% higher in summer than in winter among women, but did not differ by season among men.Mean ± SD V̇Oestimated was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generations. Physical activity energy expenditure and BMI were stronger determinants of the variance in CRFestimated than other characteristic including age. This emphasizes the importance of modifiable physical activity behaviors in public health interventions.CRF The Health Research Authority NRES Committee East of England-Cambridge Central approved the study in accordance with the Declaration of Helsinki. All participants gave written informed consent. The Fenland Study has a dedicated Patient and Public Involvement panel, who provided input on the acceptability of the study protocols and participant data confidentiality. This study complied with the items listed in the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.The Fenland Study included 12,435 participants born between 1950 and 1975 and recruited from general practice lists around Cambridgeshire, UK from January 2005 until April 2015, as described in more detail elsewhere . ExclusiParticipants arrived at a clinical testing facility to complete baseline assessments after an overnight fast and after abstaining from smoking and vigorous physical activity the morning of their visit. During the clinic visit, height was measured with a rigid stadiometer , total body mass was measured in light clothing with calibrated scales , and fat-free mass was measured using dual-energy x-ray absorptiometry  . Self-reestimated, where CRFestimated was defined as estimated maximal oxygen consumption (V̇O2max). A diagram and description of the testing procedure is provided in Supplemental Figure 1 . Heart rate was monitored and recorded during testing using the combined heart rate and movement sensor   . The tesamNtech) ; 3) exer2max per kg total-body mass (V̇O2maxtbm) from exercise test performance, we extrapolated the linear relationship between heart rate and work rate . V̇O2max per kg fat-free mass (V̇O2maxffm) was estimated by multiplying V̇O2maxtbm values by total body mass and dividing by fat-free mass.To estimate V̇Oork rate  to age-pork rate , which work rate ,19, addeork rate , and the·ml O2−1 . The met·ml O2−1  using thestimated by age, BMI groups  and PAEE groups  were visualized using boxplots. Univariate associations of CRFestimated with age, BMI, and PAEE were computed as Pearson’s r; bivariate relationships were investigated using linear regression.Descriptive statistics were computed across BMI groups and sociodemographic characteristics by sex- and age-stratified groups. Cuzick’s test  was perfestimated and sociodemographic characteristics (model 1) with additional adjustment for PAEE (model 2) and BMI (model 3). The season of the year when CRFestimated values were obtained was considered in these analyses by including two orthogonal sine functions in the regression model: “Winter” reaching a value of “1” on January 1 and “−1” on July 1st, and “Spring” reaching a value of “1” on April 1 and “−1” on October 1. Seasonal trends were further described on a monthly basis using a binned regression procedure, controlling for seasonal variation in the measurement of sociodemographic characteristics . The association of estimated V̇O2maxtbm with PAEE  was higher than associations with BMI  and age . The combination of BMI and PAEE explained more variance in estimated V̇O2maxtbm  than bivariate combinations with age. For estimated V̇O2maxffm, univariate and bivariate analyses had weaker associations and less explained variance than analogous results for estimated V̇O2maxtbm.Figure 2maxtbm estimates, stratified by sex and sociodemographic characteristics, are provided in Table 2maxtbm was generally higher in participants with higher educational attainment . Workers in more physically demanding jobs were fitter than those in sedentary jobs . Current smokers had similar V̇O2maxtbm estimates compared with nonsmokers . Estimated V̇O2maxtbm differed by ethnicity; however, sample sizes were disproportionate between Whites and other racial and ethnic groups. Differences in estimated V̇O2maxtbm were not apparent between income levels, marital status, and testing sites. Analogous results for estimated V̇O2maxffm are presented in Table Unadjusted mean V̇O2maxtbm and sociodemographic characteristics are reported in Table 2maxtbm but was attenuated to the null with adjustment for PAEE. Among women, occupation type and smoker status were not associated with differences in estimated V̇O2maxtbm after adjustment. Among men, estimated V̇O2maxtbm was higher in manual workers compared with sedentary workers when associations were adjusted for sociodemographic characteristics only. When additionally adjusted for PAEE and BMI, estimated V̇O2maxtbm was lower in manual workers , retirees , and the unemployed  relative to sedentary workers. Current male smokers had lower estimated V̇O2maxtbm relative to nonsmokers . In both women and men, estimated V̇O2maxtbm did not differ by education level, income, and marital status after adjustment. Table 2maxffm. The direction and magnitude of differences in estimated V̇O2maxffm across sociodemographic factors were similar to those found for estimated V̇O2maxtbm; however, Black men had lower V̇O2maxffm  relative to White men.Sequentially adjusted multivariable analyses of associations between estimated V̇O2maxtbm and V̇O2maxffm than women tested at other times of the year. Seasonal variation in estimated V̇O2maxtbm and V̇O2maxffm was not observed in men after adjustment for PAEE. To investigate this further, we analyzed seasonal variation in CRFestimated when stratified by higher (≥50 kJ·d−1·kg−1) and lower (<50 kJ·d−1·kg−1) PAEE levels  varies by anthropometric, sociodemographic, and behavioral characteristics in a population-based sample of UK adults (The Fenland Study). The direct relationship between physical activity and CRFestimated was stronger than the inverse relationship between age and CRFestimated. Although CRF and CRF trainability are known to have a strong genetic component  was less than values published in some CRF registries from different countries. Cardiorespiratory fitness declined by approximately 2 mL O2·kg−1·min−1 per 5 yr in the US-based FRIEND registry  as being 25 yr younger. Similarly, a one SD higher BMI was associated with lower CRF (1.2 to 1.6 mL O2·kg−1·min−1), adjusted for age, physical activity and other sociodemographic characteristics. Thus, physical activity and BMI were stronger determinants of CRF than age and other factors that were examined. Physical activity is known to improve and explain a majority of the variance in CRF among adults  and fat-free mass (V̇O2maxffm). In multivariable analysis, estimated V̇O2maxtbm was negatively associated with BMI, however estimated V̇O2maxffm was positively associated. Other studies demonstrate that CRF is independent of adiposity when scaled by fat-free mass and suggest V̇O2maxffm can be considered an indirect measure of musculoskeletal tissue metabolic quality (2maxffm values. Indeed, ectopic fat infiltration of skeletal muscle may be more related to impaired muscle oxidative capacity and reduced force production than overall adiposity (We present CRF quality ,46. More quality , could bdiposity ,49.estimated values were generally higher in the summer compared to the spring and winter. Physical activity behaviors displayed a similar pattern as reported in previous analyses (CRFanalyses . PAEE adanalyses .estimated in a large participant sample, enabling the investigation of differences by sociodemographic characteristics in the UK. We also quantified and compared the influence of objectively measured PAEE and BMI on associations between CRFestimated and these characteristics, which allow judgment of their relative importance. A limitation of our study includes using heart rate response to a submaximal exercise test, rather than directly measured maximal oxygen consumption. We show in the validation substudy that the CRFestimated from this method agree with direct measurements of CRF, which provides reassurance of our findings. Other sources of CRF estimation error do still exist, however, including the conversion of the energetic cost of the treadmill test to oxygen cost and the estimation of resting energy expenditure. This could impact the classification of participants into CRF categories in clinical settings (Our study has strengths and limitations. We objectively assessed CRFsettings . Anothersettings ,52, suggestimated within a UK adult population by sociodemographic factors and lifestyle behaviors. CRFestimated was inversely associated with age but less steeply than anticipated, suggesting older generations are comparatively fitter than younger generations. Physical activity and body size were stronger determinants of the variance in CRFestimated than any other factor including age. A one SD difference in physical activity had the same impact on CRF as being 25 yr younger. This emphasizes the importance of maintaining physical activity across adulthood.We have described variation in CRF


The influence of dipeptidyl peptidase-4 (DPP4) inhibitors on glycemic variability compared to other oral antidiabetic drugs (OADs), measured based on the mean amplitude of glycemic excursions (MAGE), has not been comprehensively analyzed. The aim of the study was to perform a meta-analysis to compare the effects of DPP4 inhibitors on MAGE with other OADs in type 2 diabetes mellitus (T2DM) patients without concurrent insulin treatments.The Medline (PubMed), Embase (Ovid), and CENTER (Cochrane Library) databases were searched for relevant randomized controlled trials (RCTs). Study characteristics and outcome data were independently extracted by two authors. A random-effect model was used to combine the results.2 = 28%). Predefined subgroup analyses suggested that DPP4 inhibitors were more effective in reducing MAGE compared to insulin secretagogues  and non-secretagogues , as well as compared to sulfonylureas  and sodium glucose cotransporter 2 inhibitors .Fourteen studies with 855 patients were included. Compared to other OADs, DPP4 inhibitors significantly reduced MAGE  with mild heterogeneity (IDPP4 inhibitors may significantly reduce glycemic variability compared to other oral anti-diabetic drugs, as evidenced by MAGE in T2DM patients with no concurrent insulin treatment.INPLASY, registration number: INPLASY2021120113. Vascular complications are considered the main determinants for increased disability and mortality in patients with type 2 diabetes mellitus (T2DM) , 2. The via glucose-dependent insulin secretion and inhibiting glucagon production without increasing the risk of hypoglycemia , dipeptidyl-peptidase 4 (DPP4) inhibitors have shown promising therapeutic efficacy for T2DM patients on the basis of numerous benefits besides hypoglycemic efficacy such as the preservation of islet β-cell function , improveglycemia , 19.via continuous glucose monitoring (CGM) or flash glucose monitoring (FGM)  ng (FGM)  and is ang (FGM) –34, the ng (FGM)  includinng (FGM) –34 have The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement  and the Medline (PubMed), Embase (Ovid), and CENTER (Cochrane Library) databases were searched for relevant studies with the following keywords: (1) “DPP4” OR “DPP-4” OR “dipeptidyl peptidase-4 inhibitors” OR “sitagliptin” OR “vildagliptin” OR “linagliptin” OR “saxagliptin” OR “alogliptin” OR “dutogliptin” OR “aemigliptin” OR “anagliptin” OR “teneligliptin” OR “trelagliptin” OR “omarigliptin” OR “gemigliptin” OR “evogliptin”; (2) “continuous glucose monitoring” OR “glycemic variability” OR “glyceamic variability” OR “glucose variability” OR “glucose fluctuation” OR “glycemic fluctuation” OR “mean amplitude of glycemic excursion” OR “MAGE” OR “standard deviation” OR “SD” OR “SDBG” OR “largest amplitude of glycemic excursion” OR “LAGE” OR “Coefficient of variation” OR “CV”; and (3) “random” OR “randomized” OR “randomised” OR “randomly”. Only clinical studies were considered. The references of related reviews and original articles were also searched as a complement. The final database search was conducted on July 23, 2021.Studies that fulfilled the following criteria were included: (1) full-length articles published in English; (2) designed as parallel-group or crossover RCTs; (3) included adult patients with T2DM who were not treated with insulin; (4) patients were allocated to a treatment group with DPP4 inhibitors or a control group with other OADs; and (5) reported the between-group difference of changes in MAGE using CGM or FGM from the baseline for patients in each arm of the study. Studies with drug-naïve patients or T2DM patients on background OAD therapy were included. Studies including T2DM patients with concurrent insulin therapy were excluded because the influence of insulin on blood glucose fluctuations is related to various factors such as dosage and categories, which may conceal the effect of combined OADs on blood glucose fluctuations. We did not consider studies including patients treated with single-dose/single-day DPP4 inhibitors because we did not plan to evaluate the acute effect of DPP4 inhibitors on glycemic variability. In addition, non-randomized studies, studies with non-T2DM patients, and those without MAGE measurement using CGM or FGM were excluded.Database search, data extraction, and quality evaluation were conducted by two independent authors. Disagreements were resolved by discussion with the corresponding author. We extracted data regarding the study information , study design , patient information , details of background antidiabetic treatments, drugs and doses of DPP4 inhibitors and control OADs, treatment durations, and methods used for MAGE measurement. Quality evaluation was achieved using Cochrane’s Risk of Bias Tool  accordin2 statistic was also calculated, and an I2 > 50% reflected significant heterogeneity (The influences of DPP4 inhibitors on MAGE compared to controls in T2DM patients were presented as the mean difference (MD) and 95% confidence interval (CI). We used Cochrane’s Q test to detect the heterogeneity . The I2 ogeneity . Pooled ogeneity . Sensitiogeneity . Predefiogeneity . P valueThe process of database search and study identification is shown in 2 = 28%). Pooled results showed that DPP4 inhibitors significantly reduced MAGE compared to other OADs in T2DM patients with no concurrent insulin treatment . Predefined subgroup analyses suggested that DPP4 inhibitors were more effective at reducing MAGE compared to insulin secretagogues  and non-secretagogues  is a new vital index of glycemic control that was recently proposed to describe the percentage of time when glucose values fall within the target range (the common default range of 3.9–10 mmol/L) in 24 h . AlthougTo the best of our knowledge, there is only one meta-analytic study that evaluated the influences of DPP4 inhibitors on glycemic variability compare to other OADs . This stThe specific multidimensional mechanisms of DPP4 inhibitors for glycemic regulation, such as enhancing incretin preservation and inhibiting glucagon production , 19, maySubgroup analysis also suggested that the reduction of MAGE was more remarkable in patients with T2DM duration ≤ 5 years as compared to those with disease duration > 5 years. Patients with longer T2DM duration are likely to have increased glycemic fluctuation severity, which is likely correlated with impaired pancreatic beta-cell function in these patients . SubsequThis study also has limitations that should be considered when the results are interpreted. Firstly, there remains no consensus regarding the optimal parameter for measuring glycemic variability in T2DM patients. The influence of DPP4 inhibitors on other glycemic variability parameters in T2DM patients, such as TIR, should be evaluated. Besides, GV was analyzed based on CGM with relatively short durations among the included studies (24~72 hours). Parameters of GV calculated on the basis of CGM with longer duration  may more accurately reflect the severity of glucose fluctuation. In addition, this meta-analysis was based on data at the study level rather than individual patients. Therefore, the results of subgroup analysis should be interpreted with caution. Large-scale RCTs or meta-analysis based on individual patient data may be considered to confirm whether patient characteristics or concurrent medications influence the potential effect of DPP4 inhibitors on MAGE. Finally, nine of the 14 RCTs included in the meta-analysis used insulin secretagogues (mostly SUs) as controls. None of the included studies compared DPP-4 inhibitors with the oral formulation of the GLP-1 receptor agonist semaglutide on GV. Studies are warranted for further investigation and the conclusion that DPP-4 inhibitors may be more effective than other OADs in reducing GV should be interpreted with caution in this regard.In conclusion, the results of the meta-analysis showed that DPP4 inhibitors may be more effective than other OADs in attenuating glycemic variability in T2DM patients receiving no concurrent insulin therapy.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.SC, RZ, RC, YZ and MY conceived and designed research; SC and RZ collected data and conducted research; SC and MY performed or supervised analyses; SC, RZ, YMZ, SR and MY interpreted data; SC wrote the initial paper. All authors provided substantive suggestions for revision, reviewed and approved final version of the paper, and for all aspects of the work in ensuring that questions related to the accuracy.This study received funding from MSD China Holding Co. Ltd. for funding editorial assistance, which assistance was provided by Medjaden Inc.Jingya Chen of MSD China Holding Co., Ltd., Shanghai, China, assisted literature research of the manuscript. Administrative assistance was provided by Li Qi of MSD China Holding Co., Ltd., Shanghai, China. Medical writing and editorial assistance was provided by Medjaden Inc. This assistance was funded by MSD China.SC, RZ, YZ and YMZ are employees of MSD China; SR is employee of Merck Sharp & Dohme LLC., a subsidiary of Merck & Co., Inc., Rahway NJ, US. This study received funding from MSD China. The funder had the following involvement with the study: study design, data collection and analysis, and preparation of the manuscript. All authors declare no other competing interests.The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The proposed cleaning procedures might be generally applied to various kinds of Au electrodes fabricated with the same conditions comparable with those are introduced in this study.Surface cleaning of the working electrode has a key role in improved electrochemical and physicochemical properties of the biosensors. Herein, chemical oxidation in piranha, chemical cleaning in potassium hydroxide-hydrogen peroxide, combined (electro-) chemical alkaline treatment, and potential cycling in sulfuric acid were applied to gold finish electrode surfaces deposited onto three different substrates; low temperature co-fired ceramics (LTCC), polyethylene naphthalate (PEN), and polyimide (PI), using three different deposition technologies; screen printing, inkjet printing, and electroplating  accordingly. The effects of the (electro-) chemical treatments on the gold content and electrochemical responses of LTCC, PEN, and PCB applicable for aptamer-based sensors are discussed. In order to assess the gold surface and to compare the efficiency of the respective cleaning procedures; cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were employed. LTCC sensors electrochemically cycled in sulfuric acid resulted in the most gold content on the electrode surface, the lowest peak potential difference, and the highest charge transfer ability. While, for PEN, the highest elemental gold and the lowest peak-to-peak separation were achieved by a combined (electro-) chemical alkaline treatment. Gold content and electrochemical characteristics on the PCB surface with extremely thin gold layer could be slightly optimized with the chemical cleaning in KOH + H Furthermore, a strong interaction between gold and thiol groups has attracted great attention towards the development of thiol-functionalized aptamers in biosensor application. Aptamers can be easily modified chemically to thiol-terminated molecules and immobilized on the gold surfaces via self-assembled monolayers (SAMs) of thiol-gold5. All these prominent features have highlighted the importance of Au electrodes and surface pretreatments prior to the immobilization and coupled with electrochemical sensing methods paved the way for development of a powerful tool for point-of-care (POC) biological diagnosis6. Not only the quantity and quality of thiol–gold interactions on the surface8, but also electrochemical detection performance10 are strongly affected by the surface cleanliness.Gold has been widely used as a transducer element in the field of electrochemical biosensor owing to its fascinating properties such as excellent electrical conductivity, chemical inertness, and superior biocompatibility11.Commercially fabricated Au electrodes are subjected to adsorb a wide range of adventitious contaminants from the laboratory environment or during the manufacturing process. Contaminants on the gold surface can act as a barrier that block the covalent bonding between gold and sulfur group in thiolated aptamers, in addition to a resistance against charge transfer ability on the electrode surface; hence cleaning of the transducer substrate has been considered as the first and foremost step in fabrication of a highly sensitive electrochemical aptasensor and achieving reproducible results13 and the results were characterized using electrochemical and physicochemical methods14. UV-Ozone cleaning procedure has been known as a simple, dry, and effective method to remove a wide variety of organic impurities from various substrates. UV irradiation oxidizes the organic molecules either via O3 formation or direct dissociation of C–C and C–H bonds in the adsorbed organic compounds which can be much easier oxidized and removed from the surface17. Although piranha solution has been considered the most popular oxidizing agent to remove different kinds of impurities and organic materials from the gold surface21 some evidence of delamination of gold film from the substrate and topography damage have been reported23 in addition to the gold-oxide formation on the surface24 that requires to be removed with further cleaning procedures. Cyclic voltammetry (CV) in sulfuric acid solution is a commonly used method for gold surface cleaning26 to effectively polish the gold surfaces without changing the morphology27. Cleaning in alkaline (KOH + H2O2) solution to remove the organic impurities has also been reported elsewhere30. According to the literature, a potential sweep in KOH followed by chemical alkaline treatment (KOH + H2O2) is found to be the most effective method to leave a very clean gold surface while maintaining the electrochemical properties of the electrode31.Numerous cleaning approaches including chemical and electrochemical pretreatments for preparing reproducible gold surfaces have been proposed in previous worksIn this study, the most efficient cleaning procedure for LTCC, PEN, and PCB (-based) gold electrodes in terms of electrochemical responses and surface properties is explored. Various (electro-) chemical cleaning approaches have been tested on three types of Au electrodes fabricated with different technologies and processes to achieve the most electroactive surface area and the highest concentration of elemental gold. To this end, electroactivity and surface cleanliness were characterized using electrochemical methods: Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), as well as physicochemical analysis: X-ray photoelectron spectroscopy (XPS) and Scanning electron microcopy (SEM).2SO4) and potassium hydroxide (KOH) were purchased from Sigma-Aldrich  and Merck  respectively. Hydrogen peroxide (30% H2O2), phosphate-buffered saline (PBS), potassium ferricyanide K3[Fe(CN)6], and potassium ferrocyanide K4[Fe(CN)6] were purchased from Carl Roth . All solutions were prepared in ultra-pure water (18.2 MΩ.cm at 25 °C) produced by a Direct- UV Water Purification System purchased from Merck  and freshly utilized.Sulfuric acid (98% H2 surface area) utilizing thick-film technology. Second, PEN  printed gold layer with 1 µm thickness on Polyethylene naphthalate polymer film (34 mm by 10 mm with 0.28 cm2 surface area) using inkjet printing technology. Both LTCC and PEN (-based) electrodes were manufactured in Fraunhofer Institute for Ceramic Technologies and Systems IKTS , while PCB-based finish gold chips were fabricated via electroplating of the nickel-gold  layer on a polyimide (PI) substrate containing copper as a printed circuit board (34 mm by 10 mm with 0.28 cm2 surface area) supplied by LeitOn GmbH .Three types of Au electrodes fabricated with different printing technologies on different substrate were employed as the working electrode in this study as follows: First, LTCC (Low Temperature Co-fired Ceramic) printed gold paste on GT951-ceramic substrate  for 30 min. Shortened “UV-O3” in the results. Thereafter, UV-O3 cleaned sensors were cleaned chemically via chemical oxidation either in piranha or KOH + H2O2, and electrochemically via either a single potential sweep in KOH or potential cycling in H2SO4 following the UV-O3 pretreatment as shown in Fig. 3 cleaned sensors were dipped in piranha solution (Three parts concentrated 98% H2SO4 and one part 30% H2O2) for 5 min (CAUTION: extreme precaution is needed on handling and using piranha solution!). Potassium hydroxide and hydrogen peroxide cleaning, UV-O3 treated chips were immersed in a solution (50 mM KOH and 30% H2O2 in the ratio of 3:1) for 10 min. Shortened “KOH + H2O2” in the results. Potassium hydroxide and hydrogen peroxide/KOH Sweep, Gold chips were sequentially cleaned in UV-Ozone photoreactor, KOH + H2O2 solution, and rinsed with ultra-pure water. A single linear potential was swept between − 200 and 1200 mV [vs. Ag/AgCl (sat. 4 M KCl)] in 50 mM KOH at a sweep rate of 50 mV/s. Shortened “KOH + H2O2 /KOH Sweep” in the results and also known as combined (electro-) chemical alkaline treatment. Potential cycling in sulfuric acid, UV-O3 cleaned chips were placed in a three-electrode electrochemical cell and the potential was cycled between − 500 and 1700 mV [vs. Ag/AgCl (sat. 4 M KCl)] at 100 mV/s scan rate in 0.5 M H2SO4 until a reproducible cyclic voltammogram of gold was achieved. The sulfuric acid solution was already deoxygenated using nitrogen flushing into the solution for 5 min prior to the experiments. The procedure is known as “H2SO4 CV” in the results. After each cleaning procedure, gold sensors were rinsed thoroughly with ultra-pure water, dried under N2, kept in the vacuum, and analyzed over the next day at the latest time. Each cleaning protocol was tested over multiple samples and studied individually for statistical analysis. All experiments and measurements were carried out at 25 °C.Initially, Au electrodes were cleaned using ultrasound in ethanol for two minutes, rinsed thoroughly with ultra-pure water, and dried under nitrogen gas. To remove the organic contaminants, Au chips were exposed to UV irradiation in a UV Ozone Cleaner—ProCleaner purchased from BioForce Nanosciences  was used to perform electrochemical measurements as well as electrochemical cleaning using a three-compartment electrochemical cell consisted of a Au electrode as the working electrode, a platinum rod as the auxiliary electrode, and Ag/AgCl reference electrode in saturated 4 M KCl solution at room temperature. Before and after treatment following suggested cleaning procedures, each Au chip was characterized with CV  by sweeping the potential from − 100 to + 600 mV [vs. Ag/AgCl (sat. 4 M KCl)] at 20 mV/s scan rate in PBS solution (pH 7.4) containing 1 mM [Fe (CN)6]3−/4− as a redox agent. Impedance values were recorded at an amplitude of 10 mV on a DC potential of 0.2 V, 60 data points logarithmically distributed within the frequency range of 0.1–100 kHz and fitted to the Randles equivalent circuit model14 using IviumSoft 4.1066 (https://www.ivium.com/software/). XPS measurements were conducted using a SPECS system developed by SPECSGROUP  consisting of a SPECS XR 50 M X-ray source combined with a SPECS Focus 500 crystal monochromator, which provides Al K-alpha radiation, and a SPECS PHOIBOS 150 MCD-9 hemispherical analyzer with a nine channeltron detector. Survey spectra were acquired at a pass energy of 50 eV with the chamber operating at a pressure of 3 × 10–10 mbar. For analysis of the surface composition, core level intensities were normalized to their respective photoionization cross sections as calculated by Scofield32, as well as the instrument specific product of electron inelastic mean free path and analyzer transmission function determined by Speck33. SEM images were recorded by a Philips XL30ESEM-FEG purchased from ThermoFisher Scientific  with a standard setup: secondary electron detector, 3 keV acceleration voltage, and 6.5 mm working distance within a wide range of magnification .An IviumStat.h purchased from Ivium Technologies B.V. (p) and charge transfer resistance (Rct) values obtained from cyclic voltammogram and Nyquist plot of EIS for different Au sensors before and after cleaning procedures and rounded according to the rules of DIN 1333. The uncleaned LTCC sensors show the highest ∆Ep value (410 mV) without any specific oxidation and reduction peaks  that confirms almost full coverage of the surface with impurities. The considerable deviation from theoretical value of ∆Ep (58 mV at 25 °C) for single-electron transfer reactions such as in the [Fe (CN)6]3−/4− couple on a perfectly clean gold surface14 can be due to the long-term storage in the laboratory environment and much more exposition to contaminants rather than other recently fabricated sensors. Accordingly, any changes on the electrode surface after each cleaning procedure strongly influence both the Peak-current potential-differences (∆Ep) and charge transfer resistance (Rct) values. So that, ∆Ep from CV and Rct from EIS can be used as a measure of surface cleanliness14. After UV-O3, the electrochemical values remarkably decreased to  because of organic removal. Further decreases were subsequently found after different chemical and electrochemical treatments as summarized in Table Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were employed to investigate the electroactivity of the gold surfaces before and after treatment with different cleaning procedures. Table aks Fig. a and a waks Fig. b correspp and Rct values will be expected, which is indicative of a clean and conductive surface area12. The lowest potential difference (83 mV) with sharp redox peaks and much lowest resistance (65 Ω.cm2) was achieved for LTCC cleaned with H2SO4 CV. In a perfect gold surface or bare gold electrode, the diameter of the semicircle portion in the Nyquist plot corresponding to the Rct value is getting lost that only appeared as a straight line31. Figure 2SO4, indicating noticeable removal of particles on the surface thereby reducing the charge transfer resistance of the LTCC sensors.Basically, for a reversible and fast electron transfer across the interface, the lowest ∆Ep value of 271 mV could be evidently decreased with UV-O3 pretreatment (104 mV) and further improved following with other cleaning methods as seen in Fig. 2O2/KOH Sweep (88 mV) as shown in Table p value (104 mV) for both LTCC and PEN after UV-O3 exposure could be achieved. While a little differences in ∆Ep value after chemical and electrochemical treatment confirms the fact that different kinds of Au electrodes are not affected by the cleaning chemicals to the same extent. A relatively low Rct value (225 Ω cm2) for uncleaned PEN sensors indicated a highly conductive PEN surface even in the presence of impurities on it. Impedance measurements for all cleaned PEN sensors didn’t distinguish any semicircle portion in the EIS spectrum indicating a highly conductive PEN surface (diffusion-controlled)31. That’s the reason for missing the Rct values of PEN electrodes in Table For PEN sensors before cleaning, a relatively high ∆E2), not as much contaminated as LTCC. They were analyzed as soon as received before being too exposed to the laboratory environment. Further improvement in electrochemical response  could be achieved with UV-O3 cleaning as clearly seen in Fig. 2O2 and to avoid formation of nickel-oxide on the PCB surface, the concentration of hydrogen peroxide was minimized to 50 mM and the cleaning solution was modified to 50 mM KOH and 50 mM 30% H2O2 in the ratio of 3:1 for this kind of sensor chips. After cleaning in KOH + H2O2, electrochemical values  could be slightly improved and a well-distinguished pair of redox peaks appeared in the cyclic voltammogram of PCB sensors as seen in Fig. p and Rct values was observed indicating oxide formation on the electrode surface following with a potential sweep. Although PCB surface could not be improved as much as LTCC and PEN, KOH + H2O2 is chosen as the most effective procedure to clean the PCB surface according to the electrochemical characterization results.Electrochemical measurements for uncleaned PCB sensors result in  and Scanning electron microscopy (SEM) provide useful information about the elemental composition and morphological properties of the surface. XPS survey spectra were recorded for each sample type before and after applying the different cleaning procedures, and the elemental composition was calculated from the intensity of the respective core level signals. Representative spectra of selected samples are shown in Fig. 2O2 or KOH + H2O2/KOH Sweep cleaning. The highest concentrations of 70.1% of gold could be achieved for LTCC sensors cleaned with potential cycling in 0.5 M H2SO4 and with Piranha treatment. Both techniques lead to comparable surfaces compositions with 22.5% (23.1%) carbon and 5.9% (6.3%) oxygen, respectively.Quantitative XPS analysis for uncleaned LTCC chips Fig. a results2SO4 is chosen to be the most effective cleaning procedure for LTCC sensors, as it leaves the highest concentration of elemental gold and lowest content of carbon and oxygen on the surface in XPS measurements, while also showing the lowest peak potential difference and charge transfer resistance values in the electrochemical characterization.SEM results of uncleaned LTCC surfaces Fig. a show a 3 cleaning. However, UV-O3 cleaning removes unwanted elements such as iodine and fluorine. Due to the corrosive effect of Piranha solution, iodine and fluorine reappeared on the surface after Piranha treatment, while only slightly increasing the amount of elemental gold. The highest percentage of gold could be achieved with KOH + H2O2/KOH Sweep cleaning procedure, which shows excellent electrochemical characteristics and also confirms the results of12 for thin-film gold electrodes.For PEN sensors Fig. b, alread3 exposure  chemical alkaline pretreatment improves electrode cleanliness and increases gold content on a bare electrode like what we concluded as a most efficient cleaning procedure for PEN electrodes. The efficiency of the combined alkaline pretreatment was also confirmed by a recently published study11. In previous studies36, electrochemical treatment in 0.5 M H2SO4 was used to clean the screen printed gold electrodes what was found as a best cleaning method for LTCC screen printed electrode in this study. The XPS results in the current study shows a high percentage of elemental C and O on the uncleaned gold surface that is still a lot left even after cleaning procedures. The studies show that there are always a considerable amount of elemental C and O, even on the surface of Bulk gold12 that could be merely improved after surface cleaning37.The results obtained in the current study in light of previous works. In the current study, the cleaning procedures were explored on three different kinds of gold electrodes which were not reported elsewhere, while in previous works only one kind of electrode was explored. Findings by Fischer et al.p and Rct values which are known as a measure of surface cleanliness. Using a combined (electro-) chemical alkaline treatment known as KOH + H2O2/KOH Sweep in the results, the most desirable electrochemical and physicochemical properties of the PEN surface could be achieved. In general, a thick-film Au electrode like LTCC seems to be greatly clean with CV cycling in acid due to the removal of a thin gold layer during the electrochemical polishing, while in the case of PEN electrodes with only a thin layer of gold, a combined (electro-) chemical alkaline treatment seems to be the most effective method to clean the gold surface. Due to the presence of nickel under layer of ultra-thin Au layer of the electroplated electrodes like PCB and its substantial susceptibility to oxidation, harsh oxidizing cleaning methods like piranha or electrochemical techniques that sequentially oxidize and reduce the surface could not work. The most effective cleaning procedure for PCB was chemical cleaning in a low concentration of KOH + H2O2. Cleaning PEN, LTCC and PCB Au electrodes in piranha solution is not recommended due to its strong corrosive and oxidizing behavior resulting in the formation of metal oxide originating from the support material onto the gold surface which in turn significantly deteriorates the electrical conductivity. Importantly, the proposed cleaning procedures could be generalized to any kinds of Au electrodes depending on their gold thickness, substrates, and manufacturing technologies (In this study, the most effective cleaning procedure for Au electrodes fabricated with different materials and technologies was obtained. Potential cycling in sulfuric acid resulted in the highest gold content on LTCC surface, and the lowest ∆Enologies .Supplementary Information.


Furthermore, the corresponding adsorption mechanism was identified. Complete adsorption of hexavalent chromium was achieved with CNF hydrophobized with 1.5 mmol MTMS·g−1 CNF with the faster adsorption kinetic, which proved the initial hypothesis that hydrophobic CNF improves the adsorption capacity of hydrophilic CNF. The optimal adsorption conditions were pH 3 and the adsorbent dosage was over 500 mg·L−1. The maximum removal was found for the initial concentrations of hexavalent chromium below 1 mg·L−1 and a maximum adsorption capacity of 70.38 mg·g−1 was achieved. The kinetic study revealed that pseudo-second order kinetics was the best fitting model at a low concentration while the intraparticle diffusion model fit better for higher concentrations, describing a multi-step mechanism of hexavalent chromium onto the adsorbent surface. The Freundlich isotherm was the best adjustment model. Cellulose nanofibers (CNF) are sustainable nanomaterials, obtained by the mechanical disintegration of cellulose, whose properties make them an interesting adsorbent material due to their high specific area and active groups. CNF are easily functionalized to optimize the performance for different uses. The hypothesis of this work is that hydrophobization can be used to improve their ability as adsorbents. Therefore, hydrophobic CNF was applied to adsorb hexavalent chromium from wastewater. CNF was synthetized by TEMPO-mediated oxidation, followed by mechanical disintegration. Hydrophobization was performed using methyl trimetoxysilane (MTMS) as a hydrophobic coating agent. The adsorption treatment of hexavalent chromium with hydrophobic CNF was optimized by studying the influence of contact time, MTMS dosage (0–3 mmol·g Furthermore, this technique prevented electrostatic repulsion between the negative CNF-surface charges and the hexavalent chromate ions. Nevertheless, excessive coating of MTMS over the surface of CNF supposed a mass transfer limitation, minimizing the adsorption rate. For these reasons, the selected MTMS dosage for the resting adsorption tests was 1.5 mmol MTMS·gom water .−1 of initial chromium concentration, and 1000 mg·L−1 of CNF dosage. The trend of the Cr(VI) removal rate was similar under neutral (pH 7) and alkali (pH 9) conditions, while the adsorption was faster under acidic (pH 3) conditions, with a total removal of 80% after 1 h of operation , thus prioritizing the process kinetic over the adsorption capacity. Furthermore, this CNF hydrogel dosage was selected to prevent the CNF from extremely fast saturation. Hexavalent chromium concentration was modified to study the adsorption capacity of CNF when the initial chromium concentration was between 0.1 and 5 mg·LAll of the studied initial chromium concentrations showed similar trends, with an initial fast adsorption of chromium, a second stationary step, and a final slow adsorption step . This si−1 ·L−1 . At thision rate . The resulting curves of each initial chromium concentration experiment adjusted to the pseudo-second order kinetics a,b showe−1). The equilibrium time of the hydrophobic CNF was similar to the one of another silanized cellulosic material applied by Jamroz et al. [There are a large variety of adsorbents to remove hexavalent chromium and many results related to how the contact between water and adsorbent is made, and to the different surface modifications, which affects the mechanism of adsorption. The hydrophobic CNF hydrogel showed the slowest equilibrium time regarding other adsorbent materials based on nanocellulose or on activated carbon , but it z et al.   [T parameter, which is related to the heat of sorption, was 0.19 kJ·mol−1. Choudhary and Paul [T below 8 kJ·mol−1 revealed a weak interaction chromium-CNF surface. These low bT values are related to physisorption processes, where the values of adsorption enthalpy are in the order of physical processes such as intermolecular forces.The mean free energy of adsorption calculated through the Dubinin–Raduskevich model was 2.24 kJ·molJ·mol−1) . The valand Paul  indicate−1 as a hydrophobizing agent allowed for the increase in the Cr(VI) kinetic constant of adsorption k2 by 84.97%. The pseudo-second order and intraparticle diffusion kinetic models were the best fitting models. These models revealed that both the sorption rate and hexavalent chromium diffusion played a major role as rate-limiting steps. The adsorption mechanism is ruled by the multi-step adsorption of hexavalent chromium on the CNF hydrogel dominated by internal diffusion at low concentrations and external diffusion with concentrations above 5 mg·L−1. The optimized conditions were found to be pH 3 and a dosage over 500 mg·L−1. More than 97% of hexavalent chromium removal was reached, treating concentrations below 1 mg·L−1 and the maximum adsorption capacity of 70.38 mg·g−1 was achieved at 50 mg·L−1. The isotherm analysis showed that Freundlich was the best fitting model, meaning that multilayer adsorption and the heterogeneous dispersion of surface energy is the main adsorption mechanism of hexavalent chromium onto the surface of hydrophobized CNF. The Freundlich unfavorable isotherm predicts a multilayer adsorption and a weak interaction between hexavalent chromium and CNF, associated with a physical sorption mechanism. The relatively low values of mean free energy of adsorption (2.24 kJ·mol−1) and heat of sorption (0.19 kJ·mol−1) calculated through the Dubinin–Raduskevich and Temkin models are also indicators of a physical sorption process. In general terms, the hydrophobized CNF hydrogel reached relatively high adsorption capacities compared to previously developed cellulose nanomaterials and activated carbons, and the complete removal of chromium could be found, even at low adsorbent dosages. The modified CNF hydrogels successfully removed more than 97% of Cr(VI), opening promising applications for these nanomaterials as heavy metal adsorbents. The nanofibers showed a large amount of active carboxylic and anionic groups. Thus, it was necessary to develop a surface modification through hydrophobization treatment. The inclusion of 1.5 mmol MTMS·g


The photophysical behavior of these chromophores is centeredaround a rapid E → Z photoisomerizationby passage through a conical intersection seam. Here, we use photoisomerizationand photodissociation action spectroscopies with deprotonated 4-hydroxybenzalacetone (pCK–) to characterizea wavelength-dependent bifurcation between electron autodetachment(spontaneous ejection of an electron from the S1 statebecause it is situated in the detachment continuum) and E → Z photoisomerization. While autodetachmentoccurs across the entire S1(ππ*) band (370–480nm), E → Z photoisomerizationoccurs only over a blue portion of the band (370–430 nm). No E → Z photoisomerization is observedwhen the ketone functional group in pCK– is replaced with an ester or carboxylic acid. The wavelength-dependentbifurcation is consistent with potential energy surface calculationsshowing that a barrier separates the Franck–Condon region fromthe E → Z isomerizing conicalintersection. The barrier height, which is substantially higher inthe gas phase than in solution, depends on the functional group andgoverns whether E → Z photoisomerizationoccurs more rapidly than autodetachment.Molecules based onthe deprotonated  Although the excited-statedynamics in p-hydroxycinnamates has been studiedextensively in solution over the past two decades potential energy surfaces and conical intersection seams,29 although experimental strategies capable of directly observing photoisomerizationin the gas phase are now starting to emerge.32A desire to understand the photophysics of le,refs −22 and r35 frequency-resolved photoelectron spectroscopy tofingerprint internal conversion dynamics,36 and photoisomerization action (PISA) spectroscopy to select precursordeprotomers or geometric isomers and to probe photoisomerization orphototautomerization.37 However, these studies were unableto provide any evidence for E → Z photoisomerization across a series of around 20 hydroxycinnamateanions. The present study provides clear evidence for an E → Z photoisomerization response in deprotonated4-hydroxybenzal acetone . The spectrum spans the 390–480 nm range with maximumresponse at 440 nm. The spectrum is red-shifted by ≈10 nm comparedwith the photodetachment action spectrum for the methyl ester (pCEs–) from ref (pCK– is presumably due to differences in inductiveelectron donation. The absorption spectrum of pCK- in water is blue-shiftedby ≈80 nm relative to the photodissociation spectrum.A photodissociation action spectrum (black), whichserves as aproxy for the visible absorption spectrum of from ref  , correspCK– was investigated using the emerging technique of PISA spectroscopy.A detailed description and illustration of the PISA spectroscopy techniqueare available in refs (6).44 In an experiment, charged isomersthat are drifting under the influence of an electric field througha buffer gas  are separatedaccording to their drift speeds, which depend on their collision crosssections. The target isomer is selected in a primary drift stage andthen exposed to wavelength tunable light, with separation of photoisomersor photofragments in a second drift stage. By monitoring the yieldof photoisomers and Supporting Information.Photoisomerization of isolated  in refs  and 44. pCK– in any of the buffer gases considered in this study produced a singlearrival time distribution (ATD) peak, consistent with a single isomer. Thephotoaction ATD in N2 buffer gas doped with ≈1%SF6 and ≈1% propan-2-ol shows generation of a photoisomerat longer arrival time (assigned to Z) and electrondetachment as detected through Z isomer compared with the E isomer is given in the Supporting Information. Because the photodepletionsignal  in 45Electrospray ionization of  pure N2 1. SimilapCK– are shown in E – Z isomerizationwas observed only over the 360–430 nm range with maximum responseat ≈405 nm. These spectra span the same wavelength range asthe photodissociation spectrum (proxy for the absorption spectrum)in T = 300 K (0.3 eV). This situation is also true for pCEs–.37Photodetachment (red) and photoisomerization (blue) action spectrafor pCEs–,32 weused master equation simulations combining RRKM isomerization rateswith Langevin collisional energy quenching to explore the possibilityof process ii, where it was concluded to be unlikely because of theelectronic energy difference between the two isomers, although some Z → E thermal reversion may occurbefore collisions stabilize the isomers. To investigate thermal reversion(process ii) for pCK–, which mayskew the appearance of the photoisomerization action spectra, we consideredan experimental approach in which the ion mobility experiments wererepeated in CO2 buffer gas (see the Supporting Information). The rationale is that the vibrationalenergy quenching collision cross section for CO2 is anorder of magnitude larger than that for N2,49 providing more rapid thermalization and suppressionof ground-state statistical processes. Because the action spectrain CO2 buffer gas closely resemble those shown in Z → E thermal reversionprocesses have a significant bearing on the action spectra.In PISA spectroscopy, anisomerization signal can result from twomechanisms: (i) a rapid excited-state process associated with passagethrough a conical intersection and (ii) statistical isomerizationon the ground electronic state before collisions in the drift regionthermalize the activated ions. In an earlier study considering E → Z photoisomerizationfor pCK– inthe gas phase contrasts with pCEs– and derivatives such as the phenoxide deprotomer of p-coumaric acid and ring-substituted derivatives caffeic, ferulic,and sinapinic acid (and methyl esters of each), for which no E → Z isomerization was observed.37 Earlier molecular dynamics simulations and related studies havesuggested that there is a barrier on the S1 state potentialenergy surface for double-bond rotation  and 49.1°  at the transitionstate. Significantly, the computed barrier for pCK– is in good agreement with experiment (≈0.18eV) and is substantially lower than that for pCEs–.The occurrence of surfaces 3 using asurfaces 2 using tE → Z photoisomerization response for pCK– at T = 300 K, wenext consideredexcited-state lifetimes. Following rapid geometric relaxation fromthe Franck–Condon geometry (τ1 < 1 ps),gas phase lifetimes for pCK– havebeen determined to be τ2 = 52 ps when pumped at 400nm33 and ≈120 ps when pumped at444 nm,35 with the latter correspondingto exciting near the absorption band maximum. While the 400 nm studyobserved ground-state recovery (and assumed isomerization), the 444nm study observed only autodetachment (spontaneous ejection of anelectron from the S1 state because it is situated in thedetachment continuum) and predicted α-torsion dynamics basedon photoelectron angular distributions. The present action spectrashown in pCEs–, which, when pumped near the maximum in its absorptionband (438 nm), decays exclusively thorough autodetachment with a lifetimeof 45 ± 4 ps.34 Similar autodetachmentprocesses have been fingerprinted over the entire absorption bandfor pCEs– with no evidence forinternal conversion and thus the possibility of Z isomer formation.36 The longer excited-statelifetime for pCK– compared with pCEs– when pumped near the maximum inthe photodissociation action spectrum is presumably because, whilethe two anions have similar electron detachment thresholds, the absorptionprofile for pCK– is red-shiftedby ≈0.1 eV , producingan “upconverted” signal. By changing the delay betweenfemtosecond pump and probe pulses, and monitoring the upconvertedsignal, fluorescence lifetimes are measured. The present upconversionmeasurements refine an earlier solvent polarity and viscosity upconversionstudy on pCK– 18 and were performed because the earlier study(i) was limited by ≈500 fs time resolution, (ii) used an excitationwavelength (340 nm), which is far from the absorption maximum andlikely accesses a nπ* state that gains substantial intensitythrough Herzberg–Teller coupling,36 and (iii) assumed static samples that likely gave rise to photostationarystates. Steady-state fluorescence excitation and emission spectrafor pCK– in water are shown in –1 ; the large shift is inpart attributed to hydrogen-bond interactions between the phenoxidegroup and solvent molecules weakening upon excitation.41 The Stokes shift at T = 77 K is significantly lower at 2243 ± 20 cm–1 (ethanol), consistent with inhibition of nuclear and/or solventrelaxation to reach the lowest energy fluorescing geometry.To facilitate a comparison of gas phase excited-state lifetimeswith those in solution, we performed time-resolved fluorescence upconversion(≈50 fs time resolution) on pCK– with 400 nm light, which were measuredin a flow cell at T = 300 K, are shown in Supporting Information. The decay curves were fit with a two-component exponential decaymodel with lifetime τ1 dominated by rapid solventrearrangement (limited by the cross correlation), and τ2 is linked to the excited-state lifetime and associated solventmotion, i.e., convoluted with the ≈880 fs longest time scaledynamics for water rearrangement.53 Fittedlifetimes in selected solvents are given in Supporting Information). It is worth nothing that the ≈1ps lifetime of pCK– in water iscomparable with the time scale for E → Z photoisomerization of the chromophore in PYP.54 It is striking that the excited-state lifetimes for pCK– in solution are 1 or 2 orders of magnitudeshorter than in the gas phase, suggesting a considerable reductionof the isomerization barrier in solution or access of an alternativerelaxation pathway. This situation contrasts with anionic retinoidsin the gas phase that undergo barrier-controlled stereospecific E–Z photoisomerization and haveconsiderably shorter lifetimes than in solution.55 Studies of derivative hydroxycinnamate chromophores insolution have shown that the excited-state lifetime is sensitive tothe identity of the functional group on the carbonyl tail,19 with the ketone group for pCK– giving rise to the shortest lifetimes, althoughan overall picture is complicated because of solvent polarity effects,differences in charge-transfer character, and hydrogen bonding.20Fluorescence upconversiondata following excitation of pCK– isshown in Supporting Information for the solvent polarity effect. Following ref  to 0.09 eV. Values of α have been determinedat 0.64 for pCEs– 18 and 0.75 for the thioester anion,17 consistent with pCK– having the lowest isomerization barrier in solution. We concludethat solvation significantly stabilizes the barrier to isomerization.The influence of viscosity on theexcited-state lifetime of wing ref , excitedd in ref . AssuminE → Z photoisomerization of a p-hydroxycinnamate anion may occur in the gas phase, although a barrierto double-bond torsion on the S1(ππ*) potentialenergy surface is a key factor in defining if photoisomerization iscompetitive with electron autodetachment. Substitution on the carbonylgroup tunes the barrier height separating the Franck–Condongeometry and the E–Z isomerizing conical intersectionseam. Solvation of the chromophore significantly stabilizes the excited-statebarrier, leading to rapid nonradiative relaxation. The present experimentalstrategy is applicable to other charged systems that may photoisomerizeand possess excited-state barriers, and is particularly applicableto systems for which there are wavelength-dependent dynamics leadingto multiple isomeric products.55 Futurework on deprotonated hydroxycinnamate anions will seek to photogenerate,isolate, and apply frequency and time-resolved action spectroscopytechniques to Z isomers, as well as other unstableor intermediate isomers such as keto–enol tautomers,32 to map out excited-state potential energy surfaces.In summary, this study has demonstrated that