Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses . Using Google COVID-19 Community Mobility Reports, we assessed the relative effects of absolute humidity and changes in individual movement patterns on daily cases while accounting for regional differences in climatological regimes. Our results indicate that increasing humidity was associated with declining cases in the spring and summer of 2020, while decreasing humidity and increase in residential mobility during winter months likely caused increases in COVID-19 cases. The effects of humidity were generally greater in regions with lower humidity levels. Given the possibility that COVID-19 will be endemic, understanding the behavioral and environmental drivers of COVID-19 seasonality in the United States will be paramount as policymakers, healthcare systems, and researchers forecast and plan accordingly. Current evidence suggests that the primary mode of transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is close contact with infected individuals3. Aerosols5, which are particulates less than 5 µm in diameter7, likely play a significant role in transmission8. After the initial rise of cases in the early winter of 2020, cases remained severe through the spring before dropping in the summer. Given the shelter-in-place order in most states and the rise in humidity, cases generally decreased in May and stayed in lower ranges through the summer until the fall months. In most areas of the northern hemisphere, as fall turns to winter, the weather becomes colder and drier. Lower absolute humidity has been shown to be associated with increased transmission rates of other respiratory viruses 9, posing significant concerns regarding potential increases in the number of COVID-19 cases in the fall and winter. The surge in cases through the end of 2020 further supports the seasonal effects of COVID-19.As of October 14, 2021, the coronavirus disease 2019 (COVID-19) pandemic has claimed over 720,000 lives in the United States alone, with more than 44.7 million confirmed cases18, the exact environmental and biological mechanism behind airborne and droplet transmission and viral survival of SARS-CoV-219 is not yet clear. In influenza, lower atmospheric moisture has been shown to increase the production of aerosol nuclei and viral survival time9, which translates to higher risks of airborne and droplet transmission. Other climatic factors that may impact transmission include temperature and air quality21; nevertheless, absolute humidity can still provide a surrogate measure for indoor air moisture and temperature22.While several studies have suggested a relationship between climatic factors  and COVID-1923. However, as the initial lockdowns were lifted and the movement of individuals increased, the correlation between mobility and case growth rates weakened overall24, though upticks in cases were associated with increased mobility during national holidays25. During the months of 2020 and 2021 some counties and states saw increases in cases, while others observed decreases without corresponding increases in movement by any metric. Thus, other factors, including environmental factors, must also be considered as important transmission drivers.Initial efforts to slow the spread of COVID-19 focused on reducing contacts between individuals through social-distancing measures such as large-scale lockdowns, which were significantly associated with reductions in cases28 or human mobility data23, but no study to our knowledge has considered changes in both climate and human mobility on COVID-19 outbreaks in the United States. Preliminary studies have investigated these effects in China but did not consider varying sensitivities to humidity for different climatological regimes, leading to a weaker detection of humidity impacts on transmission risks in areas with higher variations of humidity29. Understanding the potential for climatic factors to increase transmission in the fall and winter is crucial for developing policies to combat the spread of SARS-CoV-2. While the interaction between environmental factors and human encounters is complex, accounting for this relationship is necessary for determining appropriate policies that will be effective at reducing transmissions. Furthermore, indoor gatherings typically increase in frequency and size in the winter and are one of the largest risk factors for transmission30. Therefore, greater understanding regarding the added risk of weather changes is needed to aid future decisions on restricting gatherings or implementing mandates for protective face coverings. In this study, we assessed the relative impact of absolute humidity and human mobility in different climatological regimes on reported cases of COVID-19 in the US.Analyses of the factors influencing COVID-19 have used either climate data3  had a decrease of 4 percent in cases. The largest associations were seen in counties predominantly in the Rocky Mountains , Upper Midwest/Northwest , West Coast/Texas/Northeast , and a region stretching along the western edge of the Midwest down to Texas . Small but significant effects were detected in two high humidity clusters, both located in the southern region of the US (High 1 and High 2), with respective reductions of 6% and 1% in daily cases with a 1 g/m3 increase in AH.We conducted a regression on counties with more than 50,000 people using a generalized linear model (GLM) and controlling for individual movement and behavior with a metric from mobile phone data of visits to non-essential businesses (see Methods), we found that increases in AH were significantly negatively associated with cases per 100,000 of COVID-19 in all the non-high humidity regions  and SARS-CoV-2. While SARS-CoV-2 is a novel human virus, other pandemic coronaviruses The effects of behavior and nonpharmaceutical interventions (NPI) are observed in our analysis when we disaggregate the analysis between the early and later phases of the pandemic. In the early phase of the pandemic, we see that an increase in mobility trends for retail & recreation resulted in an increase in daily cases, which measures visits to restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. While in the later stages during the fall and winter of 2020, retail & recreation mobility had a lesser effect since many of those establishments were closed due to NPI policies. Furthermore, increases in residential mobility played a larger role in transmission, especially during the winter holidays when travel between residential homes occurred at a higher incidence.38. Our findings support the hypothesis of a nonlinear relationship since the log-linear effects between humidity and case growth varied between climatological regimes. Our stratified regression and Fig. 39. As AH falls, relative humidity indoors also decreases, which may increase susceptibility to airborne diseases40. This association suggests that increased humidification of indoor air in high transmission settings may help decrease the burden of COVID-19.The relationship between humidity and transmission is not fully clear, but several studies have shown that as absolute humidity decreases, survival times for enveloped viruses increase nonlinearly, including other coronavirusesGiven that our results suggest COVID-19 cases will increase significantly during winters, areas where humidity typically falls earlier in the fall  are likely to see cases increase earlier. In contrast, more humid regions  will likely observe outbreaks later in the winter. However, the results demonstrate that mobility had a larger and significant impact on cases, particularly when humidity was unchanging in the summer. Consequently, falling temperatures and holiday celebrations are likely to increase the risk of people gathering in indoor spaces for longer durations, resulting in a surge of COVID-19 cases through the winter, given that there are no substantial changes in population immunity and behavior.43, who remain largely unvaccinated, suggests that proper precautions related to opening schools is warranted as the potential for transmission increases. While studies linking schools to outbreaks to date have been limited, few have occurred during the winter when transmission is higher.The prior influenza pandemic in 2009 is instructive here, as increased contact patterns that occurred in the fall likely combined with falling humidity to drive transmission, which resulted in the peak of infections occurring significantly earlier than other years. Given the uncertainty and nonlinear effects of humidity on transmission, increasing vaccination, proper social distancing, and improving healthcare capacities can potentially reduce the toll of the COVID-19 pandemic. In addition, the uncertainty regarding the role of children in transmissionWe suspected that a relationship between human behavior and climate might exist which can cause variations in encounters. During winter months, the likelihood of being indoors increases especially in colder climates. To investigate this potential interaction, we conducted a collinear analysis. We can interpret this collinear analysis as residential and workplace movement patterns not being collinear with meteorological conditions (absolute humidity) and epidemiological factors ). Retail/recreation and grocery/pharmacies are moderately collinear, while transit stations and parks were the most collinearly related to meteorological and epidemiological variables.44 may bias the analysis. Also, as with many COVID-19 analyses on retrospective data, the differences in testing rates at the county-level will result in varying detection rates of actual cases. Potential variations around vaccination efficacy for variants and within-host changes will impact the magnitude and exact timing of outbreaks45.One limitation of this study includes changing social distancing dynamics and masking adherence between counties. We attempted to account for county-level heterogeneities using fixed effects for each county, but these are static effects. Furthermore, it is difficult to disentangle the epidemiological dynamics that cause exponential growth of cases. Events related to evacuation in natural disasters or mass-gatherings during the summer of 2020 that were not reflected in the Google Mobility DataTransmission of SARS-CoV-2 will likely increase during the winters in the United States and other temperate regions in the northern hemisphere due in part to falling humidity. Studies of prior viruses and preliminary studies of the SARS-CoV-2 virus underpin the theoretical connection between humidity and transmission of droplet and aerosols. Nevertheless, mobility is still a significant driver of transmission.46 to classify the absolute humidity temporal profile for all observed counties into six exclusive clusters that are ranked based on average humidity. The clustering algorithm was implemented using the dtw package in R47. These clusters are ranked from lowest to highest as Low 1, Low 2, Mid 1, Mid 2, High 1, and High 2. Clustering allowed us to designate groups of counties based on temporal, climatological regimes and to stratify different absolute humidity patterns, which reduces group-level effects and enhances the independence of the data points. The DTW clustering of absolute humidity was conducted on a larger set of 3,137 counties. In the regression analysis, we included data from a subset of counties that had more than twenty cumulative confirmed cases and a population of more than 50,000 people. We excluded any days with fewer than 20 cumulative confirmed cases within each county because early transmission dynamics had a high rate of undetected cases48, making the data unreliable for this analysis. The final dataset used in the regression analysis included 497 counties, where separate panel data GLM was conducted on counties in each cluster . We assessed the results of the model over the entirety of the dataset and two time periods in 2020–2021: (1) the entire duration of the dataset , (2) spring and summer when humidity increases , and (3) the fall and winter months when humidity decreases to its lowest point .The United States is geographically large, and the timing and magnitude of changes in absolute humidity can vary widely across regions. In order to account for regional differences in humidity, we utilized a partitional clustering algorithm with dynamic time warping (DTW) similarity measurements1 for each county. Population data were obtained from the US Census Bureau49 for 3,137 counties from March 10, 2020 to March 1, 2021. Daily cases were obtained from the confirmed case count by taking a simple difference between the days. Any data incongruencies, such as negative case counts, were omitted in our analysis.Confirmed case data were extracted from the Johns Hopkins Center for Systems Science and Engineering50 at the National Oceanic and Atmospheric Administration (NOAA). Time series data for the year 2020 from US weather stations were acquired from the NOAA Global Summary of the Day Index51. Weather stations were mapped using latitude and longitude to corresponding counties using the Federal Communications Commission (FCC) Census Block API52. For counties without a weather station, we used data from the nearest station, which was calculated based on distance from the county’s spatial centroid using the haversine formula. In cases where counties contained multiple stations, data were averaged across all stations in a county. Absolute humidity was calculated using average daily temperature and average daily dew point (see Alduchov and Eskridge53).Daily average absolute humidity for each US county, excluding territories, was calculated using temperature and dewpoint data from the National Centers for Environmental Information54. We specifically utilized the metric that measures visits to grocery stores & pharmacies, parks, transit stations, retail & recreation, residential, and workplaces by comparing the median rate on the county-level to a 5-week period Jan 3–Feb 6, 2020. The measure was calculated as the percent difference from before policy interventions  began to impact movement. This temporal measure allowed us to compare movement differences across counties.Data on mobility from March 10, 2020 to March 1, 2021 was obtained from the Google COVID-19 Community Mobility ReportsYit, is the number of daily COVID-19 cases for county i at time t, log(N) is an offset term to control for population-size, and α is the intercept. In order to account for population immunity and exponential growth dynamics, we added the independent variables cumulative cases per 100,000, IMt, and lagged daily cases per 100,000, yi(t-δ) to the regression models. Absolute humidity, AHi(t-δ) is smoothed using a 7-day moving average and lagged by δ days. Google mobility trends to retail and recreation, RRi(t-δ), grocery and pharmacies, GPi(t-δ), parks, PKi(t-δ), transit stations, TSi(t-δ), workplaces, WPi(t-δ), residential places, RDi(t-δ), are smoothed using a 7-day moving average, lagged by δ days, and rescaled and centered on the mean. Fixed effects γi for each county were added to capture unobserved heterogeneities between counties. For our study, we assumed that the lag length δ was equal to 14 days, which is based on previous studies investigating lagged effects due to the incubation period of COVID-1955. As our outcome variable was daily cases, we modeled the variable as a Poisson distributed random variable with a log-transformed link function. Standard errors were calculated for the estimated linear coefficients β.For each humidity cluster that was classified using the DTW algorithm, we conducted three multivariate regressions using a generalized linear model (GLM) that assessed the time-weighted association between absolute humidity and non-essential visits with the number of new coronavirus cases Eqs. –3. The GWe conducted additional regressions on the absolute humidity and mobility measures as predictors individually to test for robustness. Specifically, we fit a GLM with absolute humidity for each humidity cluster and one measure from rescaled Google COVID-19 Community Mobility as linear predictors for new daily cases, as described in Eqs.  to 8).2.28).2\dostats package in R (Version 4.0.2). All untransformed coefficient estimates are located in (Tables 3) of absolute humidity. Given the log-linear relationship in a Poisson regression between the covariates and response variable, we can calculate the percent change in daily cases for a unit increase of a covariate to be equal to exp (β) − 1. For example, if β = − 0.112 for absolute humidity, we would state that there is a 9% (= exp (− 0.112) − 1) reduction for 1 g/m3 increase in absolute humidity. To verify that mulicollinearity is not a major issue, we conducted a collinearity analysis by calculating the Generalized Variational Inflation Factor (GVIF) for all regressions, which are listed in Table To demonstrate robustness in the coefficient estimates, the coefficients in the combined regression analyses with absolute humidity and all mobility trends (Eq. ) were con Tables , 2 and 3In addition to running a GLM regression, we also discretized the data based on months for each humidity cluster and calculated the Pearson correlation coefficient for absolute humidity and Google Mobility Trends against new cases Fig. . StationWe tested for robustness and externally validated our regressions by conducting additional analysis using K-folds cross-validation. We validated the coefficient estimation of all the GLMs mentioned previously by showing that the relative effect size for each regression was similar. The analysis was conducted over 100 folds or iterations with separate training and test sets derived from a subset of the county-level data. We used test sets for each fold where the mean square error (MSE) was calculated for each fit and shown in Table Supplementary Information.


Numerous studies have shown flexible electronics play important roles in health management. The way of power supply is always an essential factor of devices and self-powered ones are very attractive because of the fabrication easiness, usage comfort and aesthetics of the system. In this work, based on the metal-air redox reaction, which is usually used in designing metal-air batteries, we design a self-powered chemoelectric humidity sensor where a silk fibroin (SF) and LiBr gel matrix containing parallel aligned graphene oxide (GO) flakes serve as the electrolyte. The abundant hydrophilic groups in GO/SF and the hygroscopicity of LiBr lead to tight dependence of the output current on the humidity, enabling the sensor high sensitivity (0.09 μA/s/1%), fast response (1.05 s) and quick recovery (0.80 s). As proofs of concept, we design an all-in-one respiratory monitoring-diagnosing-treatment system and a non-contact human-machine interface, demonstrating the applications of the chemoelectric humidity sensor in health management. Self-powered sensors are of interest in wearable technology and other applications. Here, the authors report on the creation of a metal-air redox reaction humidity sensor where the conductance and charge generated is influenced by the amount of absorbed water and demonstrate application in respiration monitoring. For example, monitoring respiration, including the frequency, rhythm, and intensity of respiration, is very important for diagnosing respiratory diseases and preventing respiratory failure14. Currently, most of the reported flexible electronics are equipped with separate power supply devices, which need to be assembled through external circuits18. The complicated circuit design and wire generate high requirements for device structure and integration techniques, which inevitably affect the comfort and esthetics, and increase the probability of system failure. Therefore, it is highly desirable to develop flexible electronics with integrated energy supply functions20. In the last decade, self-powered electronics, including self-powered sensors, pacemakers, etc., have drawn great interest and been going forward obviously23. Such electronics can continuously monitor physiological signals or provide therapy without the need for external power devices and bulky wires.Numerous studies have shown flexible electronics can be applied in health management, and hold great potential to revolutionize people’s lifestyles30. Being different from the above phenomenon, the metal-air redox reaction, which is usually used in high-performance metal-air batteries, can convert chemical energy into electrical energy and does not require external stimulation, showing advantages in achieving high stability34. Previous works have demonstrated that the output performance of a metal-air battery can be affected by external stimuli 38, showing the potential that a self-powered humidity sensor may be realized if humidity can have an obvious impact on the redox reaction in a metal-air battery. Compared to other reported self-powered sensors, the sensor based on metal-air batteries derives its energy from the chemical energy of the anode. Thus, this kind of self-powered sensor may generate a stable signal response without any external energy input.Pioneer researchers have reported self-powered sensors based on various mechanisms, including triboelectric, piezoelectric, thermoelectric, and hydroelectric, which can be driven by friction, compression, temperature gradient, and humidity gradient and thus do not require other power supplyIn this work, we developed a high-performance self-powered chemoelectric humidity (CEH) sensor based on the metal-air redox reaction and demonstrated its potential application in remote health management and medical treatment. The sensor is composed of graphene oxide (GO)/silk fibroin (SF)/LiBr electrolyte gel layer sandwiched between graphite paper and aluminum foil. Since both GO and SF have abundant hydrophilic groups and LiBr has good hygroscopicity, the redox reaction in this structure is highly sensitive to humidity. The humidity can be monitored by measuring the short-circuit current of the sensor without the need for an additional power supply, which depends on the ion mobility of the electrolyte. The GO flakes are parallelly aligned in SF gels, hindering fast ion mobility and thus leading to a long lifetime of the sensor. The sensor showed good performances including quick response (1.05 s), fast recovery (0.80 s), and high sensitivity (0.09 μA/s/1%) in a wide relative humidity (RH) range (11–84%). Based on the excellent performance of the sensor, we designed an integrated respiratory monitoring-diagnosing-treatment system and non-contact human–machine interfaces, demonstrating its promising application in health management, including infection prevention, disease diagnosis, and medical treatment.39. The X-ray photoelectron spectrum (XPS) of GO shows that the contents of C and O are about 68.5 and 31.5%, respectively, and the GO is rich in oxygen-containing functional groups   of the Raman spectra of GO and GO/SF/LiBr composites and found it increased from 0.97 to 1.05  and non-contact human–machine interaction (such as non-contact controllers for light switches and elevators)  Fig. .I/dt) of the sensor under different RH. The CCR increases from 1.16 to 8.44 μA/s when the RH varies from 11.3% to 84.3%, indicating that the CEH sensor can detect variable humidity with high sensitivity (0.0933 μA/s/1%) in a wide range , showing a low cost  for 12 h is about 0.18 mg/cm+ and Br−, will migrate directionally in the electric field, and current will be generated in the external circuit at the same time. The metal-air redox reaction provides an energy basis for self-powered humidity sensors.We proposed the working mechanism of this CEH sensor, which is illustrated in Fig. 48. More water molecules will be absorbed under higher humidity, resulting in the swelling of the GO/SF/LiBr and reduction of the transmission steric hindrance to ions. In addition, absorption of water also increases the hydration and mobility of ions50. The synergy of these two effects will lead to an increase in the ion mobility in the GO/SF/LiBr electrolyte with increased humidity, corresponding to an internal resistance decrease and external current increase. The CCR first increases from 0 to a positive value, corresponding to the rapid absorption of water molecules on the GO/SF/LiBr electrolyte. Then, the CCR gradually returns to 0, indicating the absorption gradually reaching equilibrium. The CCR value is closely related to the water molecules’ adsorption and desorption speeds, which can be affected by external environmental humidity and temperature. Specifically, high humidity will accelerate the adsorption speed of water molecules, resulting in an increase in CCR. Under a certain humidity, the desorption speed of water molecules increases with temperature. Therefore, when the temperature is too high (above 70 °C), the desorption of water molecules is significantly accelerated, leading to a decrease in the total amount of absorbed water and a reduction in the CCR of the sensor.The response of the sensor to humidity significantly depends on the adsorption of water molecules in the GO/SF/LiBr electrolyte layer. According to the widely reported adsorption theory, water molecules bind hydrophilic sites  in SF and GO through hydrogen bondsR/R0) of a GO/SF/LiBr film under different RH. The water content of a GO/SF film (10 mm × 10 mm × 0.1 mm) increased from 33.76% to 66.54% as measured by an analytical balance and its ΔR/R0 decreased from −88.63% to −98.50% when the RH varies from 11.3% to 84.3% , asthma, and pneumonia, can severely endanger life within a few minutes. For example, SAS refers to the repeated occurrence of apnea or hypopnea symptoms due to various reasons, leading to brain hypoxia, lethargy, hypertension, coronary heart disease, mental disorders, diabetes, and other diseasesFigure We designed an all-in-one respiratory monitoring-diagnosing-treatment system and demonstrated its application. The system is composed of a signal collecting unit (humidity sensor), a signal processing unit (Arduino), a signal transmission unit (wireless connection module), a control unit (Arduino), and an output unit (light-emitting diode (LED), buzzer, and air pump) with multi-working modes Fig. ,  30. ThFurtherly, we demonstrated the application of the humidity sensors in the diagnosis and treatment of sleep apnea syndrome Figs. , f. An iAnother important way to treat SAS is through ventilator-assisted ventilation therapy. Currently, most of the current ventilators can only work continuously with the set parameters, which cannot be adjusted in time by monitoring the breathing status of the patient. In contrast, the designed SAS diagnosis and treatment system can actively treat SAS with a high degree of comfort  with different heights above the water surface, indicating the high stability and reliability of the sensor. The ΔI of the humidity sensor decreases from 23.10 to 2.87 μA as the height above the water increases from 2 to 12 mm.As illustrated in Fig. Based on the above results, we designed a non-contact human–machine interaction system, including a signal collecting unit (CEH sensor), a signal processing and control unit (Arduino), and a signal output unit (elevator model). Compared with the conventional contact control system, the non-contact control system can avoid the spread of the virus, as illustrated in Fig. 4d,Phosphors that can emit fluorescence under ultraviolet (UV) light were used to simulate viruses. After operating the contact controller, the index finger was infected by the viruses Fig. . In contIn summary, we report a self-powered CEH sensor driven by metal-air redox reaction and demonstrate its high performance in remote health management and medical treatment. The CEH sensor is composed of GO/SF/LiBr gel electrolyte sandwiched between an active anode and an inert cathode, which can be fabricated through a facile printing process using a GO/SF/LiBr dispersion that shows excellent processability. Humidity can be sensitively detected by the sensor by tracking its output current, which depends on the ion mobility of the electrolyte that is highly affected by the content of water. The high sensitivity can be ascribed to the abundant hydrophilic groups in GO and SF as well as the good hygroscopicity of LiBr. Furthermore, the unique layered brick-and-mortar structure of GO/SF/LiBr hinders the fast migration of ions and thus improves the lifetime of the sensor. The sensor shows high sensitivity (0.09 μA/s/1%), short response (1.05 s) and recovery (0.80 s) time, and a wide RH working range (11–84%). For demonstration purposes, we designed a smart respiratory system that can monitor, diagnose, and treat SAS. In addition, we fabricated a non-contact human–machine interface using the CEH sensor, which can effectively block virus transmission and bacterial infection. We anticipate that this strategy of fabricating self-powered sensors based on metal-air redox reactions may not only provide a humidity sensor for health management but also inspire new pathways for designing and fabricating power-free devices for portable and wearable applications.2, K2CO3, NaBr, NaCl, and KCl were purchased from Aladdin. Silk cocoon was purchased from Alibaba. The graphite paper, copper foil, copper wire, and copper mesh were purchased from Tanqianlang New Materials Co., Ltd. All reagents were used directly without further purification. Deionized water was produced through Master-R and used throughout the study.Lithium Bromide and sodium bicarbonate were obtained from Meryer. LiCl, MgCl3 for half an hour. 2.0 g of degummed silk fibers were added into 10 ml LiBr solution (9.3 mol/L) to form a SF/LiBr solution at 80 °C for 1 h.Bombyx mori silkworm cocoons were cut into small pieces and degummed in boiling water with 0.5 wt% NaHCOGraphene oxide powder was purchased from Suzhou Tanfeng Graphene Technology Co., Ltd., China. To obtain GO/SF/LiBr ink with different viscosities, SF/LiBr solution (prepared in the previous step) and GO (GO content:2.5 –10 wt%) were mixed in a speed mixer at 3500 rpm for 10 min. Unless otherwise stated, the GO content of the GO/SF/LiBr ink used in this work is 7.5 wt%.GO/SF/LiBr ink with different viscosities can be printed on various electrodes to prepare custom-designed patterns through various techniques, including direct writing, screen printing, and extrusion printing. For the direct writing method, the designed patterns were written on substrates using a Chinese brush with GO/SF/LiBr (GO content: 2.5 wt%) ink. For the screen printing method, the designed patterns were printed on substrates using a stencil mask with a custom-designed pattern with GO/SF/LiBr (GO content: 7.5 wt%) ink. For the extrusion printing, the designed pattern was printed using a 3D printer (Anycubic I3 MEGA). Specifically, the pattern was designed using 3ds Max and then the pattern was converted into G-code through Ultimaker Cura. The GO/SF/LiBr (GO content: 10.0 wt%) ink was loaded in an injection syringe (150 µm in diameter) and was extruded by a syringe pump (Lead Fluid TSD01) at a speed of 200 μL/min, The moving speed of the nozzle was set at 10 mm/s. Unless otherwise stated, the custom-designed GO/SF/LiBr electrolytes were printed through the screen printing method.The CEH sensor was prepared by assembling GO/SF/LiBr electrolytes, flexible inert electrodes, and metal electrodes. The custom-designed GO/SF/LiBr electrolyte was directly printed on the flexible inert electrode (graphite paper) through the above method. Then, a flexible active metal electrode  was directly attached to the GO/SF/LiBr. Finally, copper wires were attached to the inert electrode and active metal electrode using silver paste for testing and applications. Unless otherwise stated, the CEH sensor used for test and application was prepared with flexible graphite paper and aluminum foil.2, K2CO3, NaBr, NaCl, and KCl). Unless otherwise stated, the experiments were carried out at 25 °C, 1 atm. During the temperature stability test, the temperature range was 0–80 °C. During the test of the effect of air pressure and O2 concentration, the sensor was placed in a vacuum chamber. The air pressure was pumped from 1 atm to 0.01 atm and held for 30 min. During the lifetime test, the RH was controlled at 25%. The tests were repeated three times independently with similar results. During non-contact sensing tests, the distances of the humidity sensor above the water (37 °C) were controlled using a SHIMADZUAG-IS tester. Data were analyzed through Origin 2018 edu.The electrical measurement was carried out using an electrochemical workstation . During humidity sensing tests, water vapor was introduced into the testing chamber, in which specific RH can be precisely controlled by using different saturated salt solutions. A schematic illustration of the test set is shown in Supplementary Fig −1 to 103 s−1. The thermal stability and water content (11.3% RH) of the GO/SF/LiBr ink were characterized using a thermogravimetric analyzer . Raman spectra of the GO were collected using a Raman spectroscope . X-ray photoelectron spectra (XPS) of graphite paper and GO were collected using a PHI Quantera SXM (ULVAC-PHI) scanning X-ray photoelectron spectrometer microprobe.The topography of GO flake and GO/SF/LiBr composite was characterized using an AFM  and Cryo-SEM . Each experiment was repeated three times independently and produced similar results. The rheological properties of the GO/SF/LiBr inks were measured by a rheometer . The viscosities of the ink were recorded within a shear rate from 10A CEH sensor was integrated into the mask. The respiratory data were collected through Arduino, and then the data were sent to the cloud (bemfa.com) through the ESP8232 Wi-Fi module. The respiratory data were observed in real-time through the mobile phone (iPhone Xs) at the website (bemfa.com) and further used a Python program to read cloud data and displayed it on a laptop (Lenovo X1 Carbon 2017). Consent to publish identifiable images of research participants was obtained.A CEH sensor was integrated into the mask. The respiratory data were collected through Arduino, and real-time processing and judgment were performed through an Arduino program at the same time. According to the judgment result, the LED, buzzer, and air pump were further controlled with Arduino. Two healthy subjects, including 1 male and 1 female, were involved in the respiratory monitoring test. Consent to publish identifiable images of research participants was obtained.The humidity change signal of the finger approached were collected by Arduino and a CEH sensor, and real-time processing and judgment were performed with an Arduino program at the same time. The motor (elevator) was controlled according to the judgment result.The data were obtained with the informed consent of all participants. The Institutional Review Board of Tsinghua University approved this study (NO. 20220118).Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Reporting Summary


A comparison of temperature-dependent electricalmeasurements of millimeter-scale passivated and bare graphene on SiO2/Si indicates that the passivated graphene maintains its highfield effect mobility desirable for applications. Surprisingly, thetemperature-dependent resistivity is reduced in passivated grapheneover a range of temperatures below 220 K, due to the interplay ofscreening of the surface optical phonon modes of the SiO2 by high-dielectric-constant Ga2O3 and therelatively high characteristic phonon frequencies of Ga2O3. Raman spectroscopy and electrical measurements indicatethat Ga2O3 passivation also protects graphenefrom further processing such as plasma-enhanced atomic layer depositionof Al2O3.We demonstrate a large-area passivation layer for grapheneby mechanicaltransfer of ultrathin amorphous Ga High-quality hexagonal boron nitride(h-BN), hand-exfoliated from small single crystals, has been widelyused as a wide bandgap insulator for vdW heterostructures, enablingexceptional device quality.5Insulating layers are essentialcomponents of van der Waals heterostructures8 In addition, hBN grown in this method can only betransferred on graphene through a wet-chemical process that can contaminatethe heterostructure. This may cause difficulties with industrial-scaleapplications of hBN10 prompting a search for othersuitable insulators to enable large-area vdW heterostructures.However, the growth of large area hBN is limitedto mainly chemicalvapor deposition growth on metal, which requires large-scale infrastructure,or high-purity costly metal rendering the process neither cost-effectivenor user-friendly.11 dielectric constant,12 and surface optical (SO) phonons which remotelyscatter carriers in the graphene,14 and trade-offsexist, e.g., insulators with intermediate dielectric constants maybe optimal.15In the case of encapsulating graphene, optimizing the materialis highly complex; graphene’s electronic properties are largelydetermined by the insulator’s properties, including chargedimpurity concentration,19 Indeed, Ga2O3 has already been shown to be (i) an effective gate dielectric20 and (ii) an excellent encapsulating layer fortransition metal dichalcogenide crystals (TMDs),21 preserving and even enhancing their optical properties.Recently, the surfaceof liquid metals has been used to synthesizelarge-area atomically thin materials with facile mechanical transferonto other substrates through a cost- and user-friendly process thatdoes not require very high temperature, costly catalysts, toxic chemicals,or feedstock gases.2O3 as a large-area encapsulating layer for graphene with anintermediate relative static dielectric constant κ ∼10.22 We mechanically transfer large-area Ga2O3 onto one portion ofa millimeter-scale graphene-on-SiO2 device, allowing usto compare the electronic transport properties of bare and Ga2O3-encapsulated portions of the same device. Wefind that coating graphene with Ga2O3 preservesthe charge carrier mobility close to 3000 cm2 V–1 s–1. Surprisingly, we observe a reduction in temperature-dependentresistivity at temperatures below 220 K in the graphene encapsulatedby the Ga2O3 dielectric, explained by the interplayof screening of the SO phonon modes of the SiO2 by high-dielectric-constantGa2O3 and the relatively high characteristicphonon frequencies of Ga2O3 itself. We furthershow that Ga2O3 is useful as a passivation layer,protecting against damage from deposition of Al2O3 via plasma enhanced ALD.Here we investigate liquid-metal synthesized GaSupporting Information) usinga commercial (Graphene Supermarket23) CVD-grownmonolayer graphene/monolayer h-BNfilm (henceforth referred to as “Gr”) already transferredonto a 285 nm SiO2/Si (p-doped) substrate that functionsas a global back-gate dielectric and electrode. The Gr was then etchedinto a Hall bar geometry 0.4 mm wide and 1.2 mm long, with multiplevoltage electrodes spaced by 0.25 mm, and contacted by Ti/Au electrodesfabricated using conventional photolithography. Next, millimeter-scaleultrathin Ga2O3 was prepared on a PPC film ona PDMS stamp  through a liquid metal “squeeze-printing”19 technique. Finally, Ga2O3 was deterministically transferred onto half of the Gr device.21 We compare the experimental signatures of “bare”and “Ga2O3-covered” parts of graphenein the same Hall bar device to understand the effect of Ga2O3.Devices were fabricated . 2O3-covered and bare side  of a Gr-device. The AFM lineprofile (overlaid) yields a step height of 7.3 nm for Ga2O3, similar to AFM measurements performed on similar devices, andconsistent with thicknesses reported by Wurdack et al.212O3-covered Gr-devices. The3D schematic of the device and top-view micrograph are shown in 2O3 film covershalf of the Gr-device. The orientation of the Ga2O3 film has been carefully controlled so that the Ga-metal particlesat the boundary do not affect the electrical transport characteristicsbetween the voltage probes.Vg)dependence of the longitudinal conductivity σ measured at temperature T = 100 K for both bare and Ga2O3-coveredGr. The gate voltage Vg is offset by the gate voltage of minimum conductivity  to facilitate comparison between the two sides of the device.The comparison of the two parts of the sample shows three notabledifferences, with the Ga2O3-covered part showing(i) slightly enhanced conductivity at high Vg, (ii) increased magnitude of minimum conductivityσmin, and (iii) a broader minimum–conductivityplateau.cg = capacitance of the SiO2 back gate) obtained fromσ(Vg) data (2O3-coveredgraphene (μFE = 2900 cm2 V–1 s–1) relative to bare graphene (μFE = 2800 cm2 V–1 s–1). The highest observed hole mobility in Ga2O3-covered Gr (μFE = 2200 cm2 V–1 s–1) is lower than in bare Gr (μFE = 3000 cm2 V–1 s–1) but might not be a global maximum, as it is at the edge of the Vg measurement window. μFE is observed to be mostly unchanged by the addition of Ga2O3, if not a little increased at high positive Vg.In order to highlight the first of these features weplot the fieldeffect mobility (Vg) data 2d. The p2O3 passivationand are reproducible, we have measured60 additional devices, 30 with Ga2O3 passivationand 30 without; the detailed results are shown in Supporting Information, Section S11. We found that with theaddition of Ga2O3, room temperature electronmobility μe increased by 47.7 ±15.0% and hole mobility μh decreasedby 52.2 ± 4.4% . Due to the greater importance of remote optical phononscattering at room temperature (see below) a quantitative comparisonis difficult, but the room-temperature observations are qualitativelysimilar to that seen in 2O3 passivation showed smaller changes (3.8± 12.1% decrease in μe and18.4 ± 7.9% increase in μh)which are likely due to cleaning and annealing.To understand whetherthe changes in mobility are due to Ga2O3 is remarkablebecause previous experiments found thatdeposition of oxide usually degrades the mobility of graphene24 due to introduction of disorder. In contrast,screening by a clean dielectric can reduce charged impurity scattering.25 To infer the impurity concentration in SiO2/Gr/Ga2O3 with RPA-Boltzmann theory,26 we use κ = 10 for the amorphous Ga2O322 and μ = 2900 cm2 V–1 s–1 and calculate an impurityconcentration of nimp,Ga2O3 = 3.8 × 1012 cm–2 (Section S6.A in the Supporting Information), roughly twice the impurity concentration inferred for our baregraphene on SiO2 . This indicatesthat our liquid-metal synthesized and mechanically transferred Ga2O3 layer has low charged impurity concentration,comparable to thermally grown SiO2.The slight enhancementof mobility after deposition of Ganimp should also lead to a very weak reductionof σmin26 and a narrowing of the minimum conductivity plateau in the Ga2O3-covered graphene (Section S6.B in the Supporting Information), in contrast to our observation and respective coupling strength βi (Vgα). Empirically, the dependenceon gate voltage is found to follow a power law with α ≈1.We expect that dielectric layers affect the temperaturedependentmobility of graphene through scattering of charge carriers by SO phonons.This process, also known as remote optical phonon (ROP) scattering,is expected to contribute a resistivity proportional to a Bose–Einsteindistribution.2O3-covered graphene, we extracted ρROP from ρ . Briefly,we perform a global fit of ρ at different Vg at 70 K ≤ T ≤ 100 K to determine the acoustic phonon scattering contributionρLA(T), which islinear in temperature and independent of Vg, and the impurity contribution ρimp, which depends on Vg but not temperature. Subtracting these twoquantities from ρ allows us to extract ρROP.To examine the differences in ROP scattering for bare andGaROP to resistivity as a function of temperature, for various positivegate voltages . ρROP increasessuperlinearly in temperature, with larger magnitude at smaller Vg – Vg,min. Remarkably, in the temperaturerange 70–220 K, ρROP is lowerin the Ga2O3-covered Gr compared to bare Grfor all values of Vg – Vg,min. The dashed lines in M = 1) for bare (dashed) andGa2O3-covered (dot-dash) graphene.0, to be larger in Ga2O3-covered graphene(92.8 meV) compared to bare graphene on SiO2 (69.5 meV).The observation of slightly higher ℏω0 comparedto the expected lowest phonon mode for SiO2 (61 meV) wasalso observed by Chen et al.,14 and itis likely due to the additional contribution of the higher-energySiO2 mode. The power-law exponent α is similar forbare and Ga2O3-covered graphene and close tothat of the previous literature.28 The couplingstrength β is found to be 6.0 (h/e2) for bare graphene, which is roughly double the valueof 3.26 (h/e2) foundby Chen et al.14 Due to the different valuesof α for Ga2O3-covered graphene, it isdifficult to directly compare the coupling β, which has differentdimensions and consequently has a different magnitude to that forbare graphene.The results of the fits in ROP at low temperatures (T ≲220 K) for Ga2O3-covered graphene is due toa higher effective phonon energy, resulting in a lower ρROP at low temperatures due to lower phononpopulation, and eventually crossing over to higher ρROP at high T due to strongercoupling. To better understand the lower ρROP contribution in Ga2O3-covered graphene,we develop a simple analytical model of the SiO2/graphene/Ga2O3 heterostructure, following the methodology usedin semiconductor inversion layers29 andgraphene systems.28 The details of the model aredescribed in Section S8 of the Supporting Information.Our observations 1 indicat2O3-covered graphene is schematicallyrepresented in Supporting Information, for more details). We find that the graphene/SiO2 structure has two SO modes, i.e., ℏω1 = 61 meV and ℏω2 = 149 meV. The Ga2O3/graphene/SiO2 structure has three SO modes,with energies and coupling strengths shown in 1′ = 56 meV, ℏω2′ = 147 meVare the perturbed SiO2 modes, while ℏω3′ = 95 meVoriginates in Ga2O3. While all these modes arethermally activated, the Ga2O3 mode couplesparticularly strongly, also reflected in the high disparity betweenϵGa2O30 and ϵGa2O3∞ . At the sametime, however, the large ϵGa2O3∞ screens the ROP scattering from SiO2 modes.Hence we expect the ω3′ mode to dominate the temperature-dependentresistivity. Importantly, the energy corresponding to the ω3′ mode matcheswell with the ℏω0 value obtained from fittingour experimental data  for the Ga2O3/graphene/SiO2 structure and bare graphene/SiO2 calculated using Supporting Information). Also shown for comparison are graphene/h-BN,and HfO2/graphene/SiO2 . We see that ρROP(T) in Ga2O3/graphene/SiO2 is lower than for bare graphene/SiO2 at temperatures below approximately 220 K. Because ROP isthermally activated, at low temperatures the SiO2 mode(ω1) dominates ρROP for bare graphene/SiO2, while for the Ga2O3/graphene/SiO2 structure, the Ga2O3 effectively screens these SiO2 contributions.At higher T, the Ga2O3 ROPscattering with higher mode energy (ω3′ > ω1′) becomes active and quicklybegins to dominate due to the higher coupling. Our observation thataddition of Ga2O3 to graphene/SiO2 can lower the overall interfacial phonon scattering may inspirethe design of other heterostructures to further reduce scatteringphenomena of charge carriers in graphene, perhaps yielding a moresignificant improvement at or above room temperature.2. The experimental data agreewell with our model, and this is consistent with previous ROP experimentalmodels in graphene on SiO2.28 This mightbe surprising because one might expect the hBN monolayer crystal toplay a significant role in determining the ROP scattering in graphene.However, the experimental results suggest otherwise. These resultssuggest that the surface modes of h-BN/SiO2 are comparableto that of bare SiO2; however, further work is needed tounderstand why that is the case. This may be explained by the similardielectric constants of the SiO2 and h-BN resulting insimilar SO properties for the monolayer h-BN/SiO2 compositeto bare SiO2. Our model on SiO2/graphene/HfO2 device matches with previous results as expected.28In ourmodel, we have ignored the effect of the monolayer h-BNin between graphene and SiO2O3 does not enhanceimpurity scattering in graphene and even reduces the impact of phononscattering in a certain temperature range, we now investigate whetherGa2O3 is effective in protecting graphene fromfurther processing. Methods of growing large-area dielectric films,such as CVD, atomic layer deposition (ALD), sputtering, and e-beamevaporation, have proven to be damaging for graphene, leading to enhancementof impurity scattering and consequently degradation of mobility.34Having demonstrated that Ga2O3 avoids such damaging processes, as demonstrated in 2O3 by using plasma-enhancedALD to grow a 5.5 nm layer of Al2O3 over theentire sample, following the method of Tang et al.31 , in order to replicate conditions that normallymight damage graphene. The ALD chamber temperature is modified to150 °C, to avoid possible change of morphology of amorphous Ga2O3 by thermal annealing.21Our gentle transfer technique for thin Ga2O3 deposition on the bare andthe Ga2O3-covered side of the Gr device. Partsa and b of 2O3-coveredsides respectively before and after ALD deposition. Both areas ofthe device show monolayer graphene G and 2D Raman peaks at 1591 and2709 cm–1, with expected ratio ∼2. BeforeALD deposition the graphene spectra on both sides are nearly identical,confirming that Ga2O3-transfer process doesnot lead to any structural disorder in graphene. However, there isa stark difference between the Raman spectra on both sides of thedevices after ALD processing, where the bare graphene area shows afully formed D-peak (1357 cm–1), which is nearlyabsent in preprocessed graphene and remains unchanged in the Ga2O3-covered graphene even after processing. TheD peak is activated by point disorder and indicates deposition-induceddamage in bare graphene, which is not present in Ga2O3-covered graphene. This clearly indicates that the Ga2O3 transfer has no adverse effect on graphene,and it also acts as an encapsulating layer to protect graphene againstfurther damage during subsequent deposition processes.2O3 deposition  the transport in processedGa2O3-covered graphene shows similar performanceto prior to ALD, with higher μFE values, and similar σ at ≈100 V. The broadening (andslight reduction) of the μFE peakin the Ga2O3-covered side .After deposition of Al2O3 via ALD on Ga2O3 passivated and bare devices, we find that theGa2O3 passivation provided significant protectionof mobility for both electron (12.0 ± 11.0% increase) and holecarriers . After ALD, the bare devices underwenta large reduction in hole mobility (42.7 ± 7.9%), while electronmobility varied greatly (net 16.9 ± 28% increase), indicatingmore disorder compared to Ga2O3-covered samples,similar to the effects seen in We checked the reproducibility of Ga2O3 is a viable large-area mechanically transferredpassivationlayer for vdW heterostructures. Encapsulation of graphene by Ga2O3 preserves the mobility, and reduces ROP scatteringin graphene below T = 220 K due to the interplayof high energy phonon modes and dielectric screening in this oxidewith intermediate dielectric constant. The large area passivationcapability of Ga2O3 enables other depositionmethods without causing damage at the interface, which should allowintegration with a variety of materials and processes. The liquidmetal printing technique is highly versatile with a wide range ofmaterials already demonstrated;19 hence,this work opens the possibility of expanding to other liquid metalprinted ultrathin materials for large-area vdW heterostructures.Our results demonstrate that liquid-metal synthesizedGa


Rhodobacteraceae and Flavobacteriaceae increased with distance downstream, while Sporichthyaceae and Comamonadaceae decreased. Some prokaryotic taxa, such as Phycisphaeraceae and Chromobacteriaceae, were present nearly exclusively in pure freshwater environments, while some additional prokaryotic taxa, including the SAR86 clade, Clade I, AEGEAN-169 marine group, and Actinomarinaceae, were barely present in pure freshwater environments. The eukaryotic communities were mainly composed of the Chlorellales X, Chlamydomonadales X, Sphaeropleales X, Trebouxiophyceae XX, Annelida XX, and Heteroconchia. The prokaryotic and eukaryotic communities were split into abundant, common, and rare communities for NCM analysis, respectively, and the results showed that assembly of the rare community assembly was more impacted by stochastic processes and less restricted by species dispersal than that of abundant and common microbial communities for both prokaryotes and eukaryotes. Overall, this study provides a valuable reference and new perspectives on microbial ecology during the transition from freshwater rivers to estuaries and the sea.The transition areas of riverine, estuarine, and marine environments are particularly valuable for the research of microbial ecology, biogeochemical processes, and other physical–chemical studies. Although a large number of microbial-related studies have been conducted within such systems, the vast majority of sampling have been conducted over a large span of time and distance, which may lead to separate batches of samples receiving interference from different factors, thus increasing or decreasing the variability between samples to some extent. In this study, a new in situ filtration system was used to collect membrane samples from six different sampling sites along the Sanya River, from upstream freshwater to the sea, over a nine-hour period. We used high-throughput sequencing of 16S and 18S rRNA genes to analyze the diversity and composition of prokaryotic and eukaryotic communities. The results showed that the structures of these communities varied according to the different sampling sites. The α-diversity of the prokaryotic and eukaryotic communities both decreased gradually along the downstream course. The structural composition of prokaryotic and eukaryotic communities changed continuously with the direction of river flow; for example, the relative abundances of  Microbial communities, including bacteria, archaea, eukaryotes, and viruses, are extremely diverse at the taxonomic and metabolic levels, and play a key role in global biogeochemical processes ,2,3,4,5.The relationship between the distance from upstream to downstream and the diversity of organisms in the river is not always clear and may depend on the river system and the types of organisms being examined. A strong relationship of increasing fish biological diversity with decreasing distance from the river outlet has been previously documented ,15. The While studies on eukaryotic microbial communities in river water are relatively limited ,22,23, pAt the river-to-estuary scale, water movement is thought to play a key role in regulating species taxonomy and microbial community assemblages, because the movement of water and microorganisms is directional—from river to sea, most ecosystems are both recipients and sources of microorganisms, and the migrant pools that reach a particular ecosystem will vary depending on their location in the hydrological continuum and some anthropogenic or natural factors, which can lead to strong linkages and the intense exchange of microorganisms between local and exogenous input communities ,33,34. TTo date, the vast majority of studies of microbial communities in rivers or estuaries have been based on samples collected at different temporal and spatial scales, including those which may have been collected over long periods of time and have thus been subject to many disturbing factors. In contrast, studies of in situ prokaryotic and eukaryotic communities in the river-to-sea continuum that are little disturbed by external factors within a short time period are scarce. To address this research gap, we collected 12 samples from along the upper Sanya River to the sea using an in situ filtration system to characterize changes in prokaryotic and eukaryotic communities over a nine-hour period. The Illumina Hiseq platform was used to perform high-throughput DNA sequencing of partial 16S and 18S rRNA genes, thus describing the composition and structure of the in situ prokaryotic and eukaryotic communities.The Sanya River, located in the southern part of Sanya city, passes through the city from north to south and discharges into the sea at Sanya Port, with a total length of 28.8 km and a catchment area of 337.02 square kilometers. We previously set up five sampling sites along the Sanya River, from the upstream section to the estuary, and collected one 3 um and one 0.22 µm polycarbonate (PC) membrane  at each sampling point using an in situ filtration system . At the http://www.ncbi.nlm.nih.gov/ (accessed on 27 December 2022)) under BioProject accession number: PRJNA915948 for prokaryotic and eukaryotic community datasets.The filter membranes were cut into small pieces with sterile scissors prior to nucleic acid extraction. DNA was extracted from different filter membrane samples (three extraction blank control samples were used) with DNeasy PowerSoil Pro Kit , according to the manufacturer’s instructions. The extracted DNA was quantified with a Qubit fluorometer  and used for amplification of the V4 region of the 16S rRNA gene with the primer pair 515f Modified (5′GTGYCAGCMGCCGCGGTAA-3′) and 806r Modified (5′GGACTACNVGGGTWTCTAAT-3′) , as wellAfter sequences were assigned to their respective samples, barcodes as well as forward and reverse primers  were removed to obtain clean data. The FLASH program (v. 1.2.8)  was used2 represents the overall goodness-of-fit of the model, with higher R2 values indicating that it is closer to the neutral model, i.e., the construction of the community is more influenced by stochastic processes and less influenced by deterministic processes, while N describes the metacommunity size, which, in this paper, is the total abundance of all OTUs in each sample, and m quantifies the migration rate at the community level, which is uniform for each community member (independent of species). A smaller value of m indicates a more restricted species dispersal throughout the community, and, conversely, a higher value of m indicates a less restricted species dispersal. Nm is the product of metacommunity size (N) and migration rate (m), which quantifies the estimate of dispersal between communities and determines the correlation between the frequency of occurrence and relative regional abundance [Several previous studies have shown that stochastic events  play a key role in shaping the structure of different communities, including those of macroorganisms, prokaryotes, and eukaryotes. Neutral community models (NCMs) have been shown to successfully reproduce the observed abundance distributions of species communities and can therefore be used to predict prokaryotic and eukaryotic community assembly mechanisms ,53,54,55bundance .A total of 541,658 prokaryotic sequences and 590,031 eukaryotic sequences were obtained from the 12 in situ filtration samples, with separate filter samples having retained the particle-associated and free-living microorganisms, respectively, for each site. An average of 45,138 ± 6465 prokaryotic sequences and 49,169 ± 12,142 eukaryotic sequences were obtained from each sample. In order to obtain a more accurate result of α-diversity, we rarefied to 39,024 prokaryotic sequences and 29,690 eukaryotic sequences per sample for analysis of the prokaryotic and eukaryotic diversity, composition, and structure. The α-diversities of the original, abundant, rare, and common microbial communities from the 12 in situ filtration samples were calculated. The Shannon, Inverse Simpson, and Chao1 indices and observed richness all indicated that the α-diversity of the prokaryotic and eukaryotic microbial communities decreased gradually along the Sanya River from upstream to downstream .NMDS analysis of prokaryotic and eukaryotic communities clearly showed six principal groups that corresponded to the sampling sites of Sanya 1, Sanya 2, Sanya 3, Sanya 4, Sanya 5, and Sanya 6 . These rRhodobacteraceae and Flavobacteriaceae, which are affiliated with the Proteobacteria (Alpha) and Bacteroidota, respectively, increased in the downstream direction. The relative abundance of Sporichthyaceae and Comamonadaceae, which are affiliated with the Actinobacteriota and Proteobacteria (Gamma), respectively, were opposite to the flow direction of the Sanya River. Other prokaryotic taxa, such as SAR86 clade, Clade I, AEGEAN-169 marine group, and Actinomarinaceae, were barely present in pure freshwater environments. However, in this study, Phycisphaeraceae and Chromobacteriaceae, were present almost exclusively in pure freshwater environments. Furthermore, at the level of the genus, a large number of prokaryotic ASVs were labeled as unclassified taxa. The relative abundance of the hgcI clade, which are affiliated with the Sporichthyaceae (Actinobacteriota), decreased in the downstream direction. The genera with high relative abundances also included Synechococcus CC9902, RS62 marine group, Ponticaulis, Polynucleobacter, Oleibacter, NS5 marine group, NS3a marine group, ML602J-51, Hydrogenophaga, Cyanobium PCC-6307, Clade Ib, Clade Ia, CL500-3, Candidatus Aquiluna, Candidatus Actinomarina, Alteromonas, and Acinetobacter.The relative abundance of prokaryotes was evident at the phylum, family, and genus levels, with a similarity of 97% for ASV classification, and this provided detailed information on the composition of the microbial communities . The domChlorellales X, Chlamydomonadales X, Sphaeropleales X, and Trebouxiophyceae XX, all of which are affiliated with the Chlorophyta, and the Annelida XX and Heteroconchia, which are affiliated with the Metazoa, together constituted the main taxa of the eukaryotic communities  analyses were performed based on the abundant, common, and rare taxa of prokaryotic and eukaryotic datasets, respectively . Our resMicroorganisms, both prokaryotic and eukaryotic, are important in ecosystems, and riverine microorganisms are the most fundamental part of riverine biomes and ecosystem structure. They play an important role in the transformation and cycling of nutrients, organic matter formation and decomposition, global material cycling and energy flow, and various other biological processes ,60,61,62The study of prokaryotic and eukaryotic communities in river water is relatively limited, and the in situ sampling of different locations along the river course within a short period of time can exclude a large number of disturbance factors, as well as provide new perspectives on the microbial ecology research of rivers and their discharge into estuaries and the sea. In this study, we characterized the changes in prokaryotic and eukaryotic communities over a 9 h period through the collection of 12 samples using a filtration system along the course of the Sanya River followed by high-throughput sequencing. We also split the prokaryotic and eukaryotic datasets into three sub-datasets, abundant, common, and rare, for separate analysis and study. Our results showed that the α-diversity of prokaryotic and eukaryotic communities along the Sanya River gradually decreased from upstream to downstream. Similar results have also been demonstrated in a previous study where 454 pyrosequencing of the benthic biofilms from 114 rivers was performed, and the results showed a decreasing trend in microbial diversity from the headwater source to downstream .Phycisphaeraceae and Chromobacteriaceae were present almost exclusively in pure freshwater environments. Phycisphaeraceae were also found in stream and lake surface water samples from the Fuglebekken and Revvatnet basins in southern Svalbard [Chromobacteriaceae, are very common in freshwater rivers or lakes, and are capable of peptidoglycan degradation [SAR86 clade, clade I, AEGEAN-169 marine group, and Actinomarinaceae were almost absent in pure freshwater environments in this study. SAR86 is an abundant and widespread heterotroph in the surface ocean that plays a pivotal role in the function of marine ecosystems [SAR11 clade of the Proteobacteria (Alpha) is one of the most abundant planktonic bacteria in the marine ecosystem [AEGEAN-169 marine group and Actinomarinaceae are also common microbial taxa in the ocean [Chlorellales X, Chlamydomonadales X, Sphaeropleales X, and Trebouxiophyceae XX, all of which are affiliated with the Chlorophyta, and the Annelida XX and Heteroconchia, which are affiliated with the Metazoa, together constituted the main taxa of the eukaryotic microbial community in the samples. Chlorella species are the best known unicellular green algae. These organisms were frequently used in early studies on photosynthesis [Chlamydomonadales X, consisting mainly of Spermatozopsis and Chlamydomonas, were largely distributed in freshwater environments, although Chlamydomonas could also be found in seawater environments [Sphaeropleales X are common planktonic freshwater algae [Trebouxiophyceae XX detected in this study mainly belonged to Choricystis, a photosynthetic endosymbiotic eukaryotic algae commonly found in freshwater sponges and distributed in freshwater environments [Choricystis in this study. In addition, many taxa of Annelida XX and Heteroconchia were found to be distributed in brackish and freshwater environments, including Ficopomatus, which can tolerate considerable variations in salinity [Lysidice [Mytilopsis [Corbicula [Mercenaria [In this study, Svalbard , and in Svalbard . The Vogradation ,67. SAR8osystems . SAR11 che ocean ,71. At tynthesis  and are ynthesis ,74. In tronments ,76,77. Ter algae , and, inronments , and wersalinity , as wellLysidice , Mytiloptilopsis , Corbicuorbicula , Mercenarcenaria , and somNMDS-based structural analysis of the prokaryotic and eukaryotic communities showed that the community structure differed among sampling sites, with prokaryotic community structure being mainly influenced by rare taxa, while eukaryotic community structure was influenced by a combination of abundant, rare, and common taxa. Generally, microbial communities are usually large in size, but a large number of these taxa are rare and should be very susceptible to ecological drift . EcologiThe results of our neutral community model (NCM) analysis showed that NCM could better predict rare microbial communities compared to abundant and common microbial communities, by estimating the frequency of occurrence of ASVs compared to their relative abundance changes, with higher explanatory rates of community changes. Furthermore, the m values of rare microbial communities were higher than those of common and abundant microbial communities for both prokaryotes and eukaryotes. These findings suggest that the structure formation of rare microbial communities, for both prokaryotes and eukaryotes, is more influenced by stochastic processes and less restricted by species dispersal than that of abundant and common microbial communities. Neutral theory assumes that community structures are regulated by the stochastic processes of birth, death, colonization, extinction, and speciation, rather than species traits ,93. AlthFurthermore, classical ecological studies suggest that species may differ in their use of multiple limiting resources, and, thus, much of ecology is based on the assumption that species differ in their niches, but it is difficult to infer the underlying ecological processes based on such diversity patterns as species abundance patterns, because different processes or hypotheses can produce very similar, or even the same, diversity patterns ,102,103.Rhodobacteraceae and Flavobacteriaceae increased with distance downstream the Sanya River, while the Sporichthyaceae and Comamonadaceae were decreased. Some prokaryotic taxa, such as Phycisphaeraceae and Chromobacteriaceae, were present almost exclusively in pure freshwater environments, while others, including SAR86 clade, Clade I, AEGEAN-169 marine group, and Actinomarinaceae, were barely present in pure freshwater environments. The eukaryotic communities were mainly composed of the Chlorellales X, Chlamydomonadales X, Sphaeropleales X, Trebouxiophyceae XX, Annelida XX, and Heteroconchia. Furthermore, the α-diversity of the prokaryotic and eukaryotic communities decreased gradually with distance downstream. The structure formation of rare communities, whether prokaryotic or eukaryotic, was more influenced by stochastic processes and less restricted by species dispersal than that of abundant and common communities. Overall, our study, by virtue of in situ sampling at different sites from upstream to the sea within a short time period, excluded a large number of disturbance factors and also provided new perspectives on microbial ecology in riverine, estuarine, and marine environments.Microorganisms are of fundamental importance for the functioning of riverine, estuarine and marine environments. Although a large number of studies have been conducted, the scope and time span of sampling have been particularly large, and considering the extremely complex physical, chemical, hydrological and anthropogenic disturbances in the river to marine watershed environment, more precise studies of the structural changes of prokaryotic and eukaryotic communities in this system have been relatively rare, especially those for which all samples are collected in a very short period of time. In this study, prokaryotic and eukaryotic communities formed six different community structure groups from the upstream area of the Sanya River to the sea, based on different sampling locations. The structure of prokaryotic communities was mainly influenced by rare microbial taxa, whereas the eukaryotic community structure was influenced by a mixture of abundant, rare, and common microbial taxa. The relative abundance of


Spliceosomal introns are a unique feature of eukaryotic genes. Previous studies have established that many introns were present in the protein-coding genes of the last eukaryotic common ancestor (LECA). Intron positions shared between genes that duplicated before LECA could in principle provide insight into the emergence of the first introns. In this study we use ancestral intron position reconstructions in two large sets of duplicated families to systematically identify these ancient paralogous intron positions. We found that 20–35% of introns inferred to have been present in LECA were shared between paralogs. These shared introns, which likely preceded ancient duplications, were wide spread across different functions, with the notable exception of nuclear transport. Since we observed a clear signal of pervasive intron loss prior to LECA, it is likely that substantially more introns were shared at the time of duplication than we can detect in LECA. The large extent of shared introns indicates an early origin of introns during eukaryogenesis and suggests an early origin of a nuclear structure, before most of the other complex eukaryotic features were established. Analysis of ancestral intron positions in two large sets of duplicated gene families suggests that introns may have originated earlier on in eukaryogenesis, before other complex eukaryotic features were established. Upon transcription, the introns are removed from the pre-mRNA by the spliceosome and the exons are spliced together to form mature mRNA, which is subsequently exported from the nucleus and translated into a functional protein. There are two types of introns; the vast majority of introns is of U2-type2, which are recognised and spliced out by the major spliceosome. U12-type introns are removed by the minor spliceosome and comprise less than a percent of introns in eukaryotic genomes2, with a recently discovered exception of 12% in Physarium polycephalum3; in many species U12-type introns are completely absent4.Protein-coding genes in eukaryotic genomes are characterised by the presence of introns6 and a complex major spliceosome with approximately 80 proteins7. LECA also had U12-type introns and a minor spliceosome8. Eukaryotic evolution after LECA predominantly involved the loss of introns, while only certain lineages including plants and animals experienced net intron gain6.Ancestral reconstructions have revealed that the last eukaryotic common ancestor (LECA) had a genome with a relatively high intron density compared with present-day eukaryotes9. These self-splicing introns can proliferate in the host genome but are rarely present within genes in prokaryotes. The most widely assumed scenario is that the self-splicing introns were introduced in the host genome from the protomitochondrion11 but we previously called other sources possible as well12. The emergence of intragenic introns underlined the importance of a nucleus—the defining feature of eukaryotes—to separate transcription and translation for splicing to take place completely prior to protein synthesis13. Furthermore, the origin of nonsense-mediated decay and the elaboration of ubiquitin signalling are proposed to be defence mechanisms against aberrant transcripts and proteins caused by the spread of introns14.It has been established that spliceosomal introns originated from prokaryotic self-splicing group II introns during the prokaryote-to-eukaryote transition17. Mainly genes involved in establishing and regulating a complex cell and relatively few metabolic genes duplicated during eukaryogenesis17.Eukaryotes are considered more complex than prokaryotes: cells are much larger and contain multiple membrane-bound compartments. Underlying the increase in cellular complexity during the transition to eukaryotes (eukaryogenesis) was an increase in the number of genes caused by gene transfers and gene duplications18. However, a study investigating the evolutionary history of a specific protein family, the spliceosomal Lsm and Sm proteins, found introns shared between multiple pre-LECA paralogs19. This implies that introns had spread through the genome before the duplications resulting in these paralogs took place. It also suggests that more of these shared intron positions could be detected in other duplicated families.Both the numerous gene duplications and the spread of introns through the genome occurred during eukaryogenesis and their interaction could inform the reconstruction of intermediate stages of this still largely unresolved transition. The relation between proto-eukaryotic gene duplications and introns can be researched by identifying positions of introns that are shared between ancient paralogs. An analysis performed on six eukaryotic genomes almost fifteen years ago identified very few shared intron positions that could represent intron insertions predating gene duplication eventsIn this study we utilise the greatly expanded set of eukaryotic genomes currently available to reassess the relation between the emergence of introns and gene duplications during eukaryogenesis. We detected many more shared intron positions than previous estimates. Our findings have implications for the dynamics of intron evolution and the timeline of events during eukaryogenesis.17 . Intron loss occurred frequently throughout eukaryotic evolution, with some lineages losing all introns in our set of genes. Intron gain only had a substantial contribution at certain branches, especially dinoflagellates, which has been described before21. These findings for the dynamics of introns from LECA to present-day eukaryotes are fully consistent with a previous study that also reconstructed intron-rich ancestors6 and with the complexity of the spliceosome in LECA and subsequent simplification in most eukaryotic lineages, as inferred before22.To investigate the interaction between the spread of introns and gene duplications during eukaryogenesis we used sets of proto-eukaryotic duplications inferred by two independent approaches: the Pfam domain trees from a recent study17 Fig.  and the  18 Fig. . Intron 18, which is probably due to a combination of the low number of six available genomes that were used and the frequent loss of introns. The percentage of shared introns was even higher for the Pfams, with 34.8% of the 7524 LECA introns in paralogous OGs being shared.The relatively high number of introns that could be traced back to LECA underlined the potential to find LECA introns that are in the same position in OGs that stem from a proto-eukaryotic duplication, which we refer to as proto-eukaryotic paralogs. For the KOGs, 19.9% of the 19,184 LECA introns considered had an equivalent LECA intron in at least one paralog Fig. . This is23) must have been the result of parallel intron gains. We compiled a set of separately acquired genes for both datasets and obtained percentages of 5.0% and 5.4% shared LECA introns between separate acquisitions for the KOGs and Pfams, respectively. Notwithstanding the influence of incorrect OG assignment inflating the estimated number of LECA introns shared between separate acquisitions ), this shows that parallel intron gain is a real phenomenon , P = 3.1 × 10−220 (Pfams)). Another potential explanation for shared introns is transfer of introns between paralogs due to gene conversion24. However, this scenario cannot account for the frequent presence of multiple LECA introns in the same position between multiple paralogs  and shared LECA introns were even more biased towards the start of the gene  LECA introns and post-LECA introns with respect to the relative position of the introns in the gene. Whereas the relative positions of post-LECA introns showed a fairly uniform distribution, unique LECA introns were more at the 5′ end of the gene  statistic = 0.034, χ2 = 966, df = 4, P = 8.1 × 10−208 (KOGs); χ2 = 182, df = 4, P = 2.4 × 10−38 (Pfams); Supplementary Tables We also compared the phases of the introns, which refers to the three possible positions of an intron in a codon. The phase distribution of the three different categories of introns was also different . Pfam clades inferred to have been a proto-eukaryotic invention or with an Asgard archaeal or diverse prokaryotic sister group in the phylogenetic tree had the highest fraction of duplications with introns . An additional explanation could be that Pfam domains are more conserved. Consequently, it would be more likely for an intron to be paired with an intron in a paralog. Notwithstanding the subtle differences between the two datasets, both revealed consistent findings.27 in a certain phase. A biased loss could also explain the phase bias28. Another plausible explanation that has been put forward is that the initial distribution was uniform and that the eventual phase bias was due to a combination of massive U12-type intron loss and the directed conversion of phase 0 U12-type to U2-type introns2. The phase bias of recent U2-type intron gains in the dinoflagellate lineage and recent U12-type intron gains in Physarium lends support to the protosplice site model in at least these eukaryotic lineages21. The similar phase bias of pre-LECA gains and losses . Other families seem conspicuously devoid of shared introns . The conservation of introns upon duplication challenges the main role of retrotransposition in creating proto-eukaryotic paralogs, which results in intronless paralogs and was proposed based on the initial lack of detected shared introns16. The protomitochondrion has been widely considered to be the source of introns, even though direct phylogenetic evidence is lacking12. Based on the analysis of shared introns, the close integration of the endosymbiont within the host by means of mitochondrial transport seemed to have occurred after substantial spread of introns. Although the symbiosis must have started before the close integration, this observation combined with the inferred timing from our branch lengths analysis is not easy to reconcile with the hypothesis that spliceosomal introns originated from mitochondrial self-splicing group II introns. The self-splicing introns could have come from another lineage instead.A notable exception to the described pattern of shared introns between most categories is the low number of duplications with shared introns in alphaproteobacterial acquisitions, which were very likely present in the protomitochondrion. It is tempting to speculate that these duplications were due to another mechanism; for example, they may have been the result of serial endosymbiotic gene transfersOur analysis was to the best of our knowledge the second large-scale investigation on the association between introns and proto-eukaryotic duplications, yet it was the first to encounter a large-scale occurrence of introns shared between proto-eukaryotic paralogs. Besides the potential implications on the order and causality of events during eukaryogenesis, the strong association between proto-eukaryotic duplications and introns also sheds unique light on the origin and evolution of intron phases and positional biases as well as the discussion on the emergence of U2- and U12-type introns 31 using hidden Markov model profile searches30. Sverdlov et al.18 used the homologous clusters of eukaryotic orthologous groups (KOGs) and candidate orthologous groups (TWOGs) from Makarova et al.15. KOGs are included in the euNOGs and we used the euNOG corresponding to a TWOG, if present, as determined in Vosseberg et al.17. Both types of euNOGs are referred to as “KOG” in the main text. We detected a few differences between the Makarova et al. and Sverdlov et al. clusters and chose one clustering over the other on a case-by-case basis after manual inspection  proteomes, as compiled for a previous study32. In short, we selected eukaryotic sequences based on best bidirectional hits between Opimoda and Diphoda for tree inference and supplemented these with prokaryotic sequences. In the resulting phylogenetic trees, acquisition, duplication and LECA nodes were inferred. The tree sequences belonging to a LECA node were complemented with the eukaryotic sequences that had one of these tree sequences as their best BLAST33 hit, resulting in an OG. Sequences from species that are not in the set of 167 species and human sequences that are not in the primary assembly were removed from the OG. If there was only one OG for a Pfam, it was not included.We also used the Pfam LECA families and duplications that we published recently34, with the exception of Homo sapiens, whose genome was replaced with the corresponding primary assembly (ftp://ftp.ensembl.org/pub/release-87/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.primary_assembly.fa.gz), and Stentor coereleus, for which we used the file from NCBI  to be able to match the sequence file with the genome features file.For predicting the type of introns, genome sequence files of the species in our set were obtained using the links in Supplementary Table 35. Each KOG was aligned separately using the E-INS-i algorithm and the resulting KOG alignments for a cluster of KOGs were merged into a single alignment (merge option with E-INS-i). If this was not feasible due to memory issues, the alignment was made with the FFT-NS-i option, or FFT-NS-2 if that was also not feasible. Each Pfam OG was aligned separately, followed by a merged alignment of all OGs per Pfam, both with the L-INS-i algorithm. For PF00001 and PF00069, alignments had to be performed with the FFT-NS-i option.All multiple sequence alignments were performed with MAFFT v7.310We downloaded the genome annotation files from 156 species from our set that we could extract intron information from  or the acquisition of already duplicated genes. Shared intron positions in these had to be the result of parallel intron insertions. To identify these, we used the phylogenetic trees of these clusters that we inferred before32. If the sequences corresponding to different KOGs were in separate acquisitions and none of the acquisitions in the tree had another acquisition in the inferred sister group, the intron positions in these separately acquired KOGs were compared and shared introns were identified. Introns that were only shared between separate acquisitions were not included in the shared introns analysis. All introns of KOGs that were acquired separately from all other KOGs in the cluster were not used for calculating the fraction of shared introns.The different KOGs in the KOG clusters do not all represent gene duplications during eukaryogenesis; some were acquired as separate genes32.For the Pfams, separate acquisitions were identified based on phylogenetic trees as well using the same approach. For each duplication node in the trees, the intron positions that were present before the duplication event were inferred from the LECA introns using a Dollo parsimony approach. The inferred sister groups of acquisitions and the functional annotation and duplication length information were extracted from Vosseberg et al.39 (command used: cmscan -nohmmonly -rfam -cut_ga) with the spliceosomal snRNA Rfam 14.240 covariance models RF00003, RF00004, RF00007, RF00015, RF00020, RF00026, RF00488, RF00548, RF00618, RF00619, RF02491, RF02492, RF02493 and RF02494. Introns from species for which none of the snRNA genes specific for the minor spliceosome  were detected in the genome were annotated as U2. Intron types from the remaining 70 species were predicted with intronIC v1.0.11 + 2.gf7ac7be2, using all isoforms if needed. Intron positions that were predicted as U12 in at least three species were annotated as U12-type introns.Spliceosomal snRNA genes were searched for in the genomes using Infernal v1.1.2χ2 contingency table tests or Fisher’s exact tests (in case of 2 × 2 tables). When testing the overrepresentation of functional categories, KOGs with multiple categories spanning the three main groups  were excluded for comparisons between these groups. The numbers of unique LECA introns and shared LECA introns or duplications with and without introns traced back to the pre-duplication state were compared between different functions, cellular localisations and phylogenetic origins. Differences in relative position and branch lengths were assessed with Kolmogorov–Smirnov tests. All performed tests were two-sided and P values from multiple comparisons were adjusted for the false discovery rate. Statistical analyses were performed in Python using NumPy v1.21.141, pandas v1.3.142, SciPy v1.7.043 and statmodels v0.11.244. Figures were created with Matplotlib v3.4.245, seaborn v0.11.146, ETE v3.1.147 and Jalview v2.11.1.448.Associations between two categorical variables were tested with Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Reporting Summary