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The HYSETS database contains a multitude of datasets that, when combined, provide a rich and comprehensive environment for testing in various applications such as in hydrological modelling. Four categories of data are combined to provide this sandbox environment: Hydrometric, watershed delineation, meteorological and physiographic data. The methods to extract, validate and combine these sources of data are presented according to each category.
Hydrometric data
The daily hydrometric data were collected independently for the three countries covered in this database, namely Canada, the United States and Mexico. The Canadian hydrometric data were provided through the Environment and Climate Change Canada (ECCC) Water Survey Canada (WSC) National Water Data Archive (HYDAT), available at: https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/. The data are downloadable in a single Microsoft Access table and include station metadata, such as station location, drainage area and flow regime, as well as the actual daily flow data. A filter was applied to select only the stations whose flow regime is natural, i.e. is not regulated by man-made structures over the study period. This was performed to ensure that the hydrometric data and meteorological data were as naturally correlated as possible.
Hydrometric data for the United States were collected from the United States Geological Survey (USGS) National Water Information Service (NWIS) web portal23, available at: https://waterdata.usgs.gov/nwis/uv/?referred_module=sw. Data were batch-downloaded and processed to obtain full time-series for each station. While some metadata is made available such as drainage area, station coordinates and statistics on historical flow, there is no information on the flow regime or on the presence of regulation structures. An alternative method to filter regulated structures was thus devised. To determine which sites were regulated or were affected by regulation, the streamflow dataset was cross-checked against the peak flow statistics database from NWIS. Stations whose peaks were influenced by regulation works at least once in the archive were removed from the HYSETS dataset. Therefore, all regulated, or partly regulated stations were excluded to keep the data as close to natural as possible. While this method seems to have produced the desired results, it is possible that the filtering method is not perfectly accurate and as such, users should verify individual hydrographs if any doubts arise on the river’s regulation status.
Finally, hydrometric data in Mexico were collected from the “Banco Nacional de Datos de Aguas Superficiales” (BANDAS) produced and maintained by the National Water Commission (CONAGUA) from Mexican Ministry of Environment. The data were downloaded from the BANDAS web portal and was filtered according to data quality (visual inspection, hydrological model calibration performance) and time series length. The BANDAS data are available at: www.conagua.gob.mx/CONAGUA07/Contenido/Documentos/Portada%20BANDAS.htm.
Metadata is available for every station in the database and includes station coordinates, drainage area and information about the presence/absence of regulation structures. The stations included in the HYSETS database are all located on basins exempt from any regulation structures. Finally, a filter was applied to ensure that all stations had at least one year of recorded streamflow data. All hydrometric data were converted into units of cubic meters per second (m3/s).
Watershed delineation
The watershed delineation boundaries are a critical component of the dataset as all meteorological data need to be extracted according to those limits for each watershed. For most of the watersheds, the water management agencies provide the official boundaries directly in the form of a shapefile or geodatabase. The Canadian data are available at: http://donnees.ec.gc.ca/data/water/products/national-hydrometric-network-basin-polygons/?lang=en and the United States boundaries are available at the following website: water.usgs.gov/GIS/metadata/usgswrd/XML/streamgagebasins.xml.
The drainage area was made available for most hydrometric gauges by the water management agencies that collate and curate those sources of data. However, a filter was applied to remove all stations that did not have an official drainage area value at the hydrometric gauge, as the value is key in determining if the watershed bounds are acceptable or not. The drainage areas were validated using the watershed delineation boundaries as described above in the geospatial analysis software QGIS 3.4. However, in some instances, the water management agencies did not provide watershed boundary files as they had not been produced or made available publicly. In those cases, estimated watershed contours were taken from the Global Streamflow Indices and Metadata (GSIM) project21,22 where available. For catchments where GSIM boundaries were kept for the data extraction a flag (“flag_GSIM_boundaries”) was set to 1 to inform users that the boundaries are from GSIM and not from the official agencies. The GSIM-derived area is also identified in those cases in the dataset, under the “Drainage_Area_GSIM_km2” heading. For catchments smaller or equal to 50 km2 in size according to the official gauge, a bounding box equal to the surface area around the catchment outlet was provided as the contour of the catchment as at those scales catchment delineations are difficult due to the resolution and hydrometric gauge accuracy. These catchments are represented by the “flag_artificial_boundaries” indicator in the dataset files. Furthermore, weather data and other catchment attributes are coarser than the area of the catchments in most cases.
In the HYSETS database, all boundaries are provided in a WGS84-projected ESRI shapefile and include the following properties: Watershed ID (to link to the data in the netCDF files), source of the data, name of the hydrometric station, official ID of the hydrometric station and the flag to identify if the boundaries were derived from GSIM. All drainage areas are in km2.
Meteorological data
The HYSETS database contains meteorological data from five sources. The following sections detail the methods applied to integrate the data into the database at the catchment scale. All precipitation data are provided in millimetres per day (mm/d), and temperature values are in degrees Celsius (°C).
Station data
Three weather station products were used to cover the North-America domain: The Environment and Climate Change Canada (ECCC) weather stations for Canada, available at: https://climate.weather.gc.ca/, the Global Historical Climate Network Daily (GHCND) station database24,25 for the United States and Mexico, available at: https://www.ncdc.noaa.gov/ghcnd-data-access and the station-based serially complete dataset for North America (SCDNA)26, available at: https://zenodo.org/record/3735534. The ECCC and GHCND datasets were combined to provide a North American weather station dataset for raw observations with a potential incomplete coverage for the desired 1950–2018 period. The SCDNA was also added to provide another dataset of stations with complete records between the 1979–2018 period.
The daily historical ECCC weather data were downloaded from the ECCC web portal for the years 1950–2018. Data include daily precipitation, maximum and minimum temperature for over 8578 stations across Canada, with varying levels of data completeness and record length. While the GHCND database contained Canada’s weather stations as well, the ECCC database was more complete and was preferred. Over Mexico, a total of 5249 precipitation and 5071 temperature stations are available in the GHCND dataset. Similarly, there are 55693 precipitation and 16011 temperature stations across the United States. While these stations undergo some quality control (QC), both data with and without QC were extracted and provided in the HYSETS database.
These stations were combined into a 69520-station dataset for precipitation and 29960-station dataset for temperature. The extraction process was performed twice for station data: Once using the quality controlled GHCND database and another using all available data, even the non-quality-controlled data.
The SCDNA provides daily precipitation, maximum and minimum temperature for 27280 stations over North America. A strict quality-control was performed on the original station data by the SCDNA authors. Missing data were infilled/reconstructed using information from neighbouring stations as well as three reanalyses products (ERA5, JRA-55 and MERRA-2). Strategies based on quantile mapping, statistical interpolation and machine learning were used to implement the corrections. Overall, this dataset was shown to provide a better agreement to station observations compared to four gridded products.
All three station datasets (ECCC/GHCND with and without QC and SCDNA) were weighted separately using Thiessen polygons for each watershed. The stations contributing to the Thiessen polygon calculation were those located within an artificial boundary defined as the real watershed boundaries extended by a buffer of 1° of latitude and longitude. This step was performed in order to exclude stations that would be too distant to represent the watershed conditions. Due to the highly variable nature of gauge-data quality and station longevity in the case of the ECCC/GHCND combined datasets, Thiessen polygons were computed for each day, using the available data for each day. Therefore, there are some discontinuities when a station is added or removed, or when a station temporarily has no record for a given period of time. There are also cases where no data at all were available for a given period. In those cases, the meteorological data fields are set to NaN, and the user is encouraged to replace those data using other means (either manual replacement or replacement with one of the gridded products as described below). The 1950–1978 period for the SCDNA dataset was also set to NaN.
All three sets of catchment-averaged data are available in the HYSETS database, opening the possibility of performing quantitative assessments of the impacts of using more (but perhaps less reliable) meteorological data in impact studies.
Natural resources canada gridded climate data for canada
The Natural Resources Canada (NRCan) gridded climate data product was made available from Natural Resources Canada’s Canadian Forest Service and covers the entirety of Canada up to approximately 84°N latitude27,28,29. It includes daily precipitation, maximum and minimum temperature data on a daily scale on a ~10 km spatial grid. Data cover the period 1950–2010 inclusively. Data points falling within the catchment boundaries were averaged to obtain a single time-series of continuous data as the NRCan dataset contains no missing data. When catchments were too small to contain a data point, the closest data point to the catchment centroid was used to populate the time series for that watershed. Information on the NRCan dataset can be found at the following website: https://cfs.nrcan.gc.ca/projects/3/4.
Livneh gridded climate data for continental USA, Mexico and southern Canada
The Livneh database includes interpolated precipitation and temperature data on a regular 0.0625 × 0.0625° grid over the continental United States, Mexico and southern Canada30,31. The data cover the period 1915–2015, although only the portion 1950–2015 was used in this dataset. It includes daily precipitation, maximum and minimum temperatures for the entire period without any missing data. Some discontinuities are present at the United States/Mexico border as station density and quality differ and influence the interpolation process. The same is also present but at a smaller scale on the United States/Canada border. The catchment-averaging process was the same as for the NRCan dataset. The Livneh data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at https://www.esrl.noaa.gov/psd/.
ERA5 and ERA5-Land reanalyses
The ERA532 and ERA5-Land33 reanalyses are hourly products developed by the European Center for Medium-Range Weather Forecasting (ECMWF). The reanalyses provide estimates of a multitude of hydrometeorological and atmospheric variables including precipitation and temperature on regular grids covering the entire surface of the Earth. ERA5 was first implemented with a 0.25° x 0.25° spatial resolution and data are available from 1979–2019, although the HYSETS database stops in 2018 to remain consistent with the other data sources. The ERA5-Land reanalysis is a refined version of ERA5 with a spatial resolution of approximately 9 km. It was driven by the ERA5 reanalysis and a mask was applied such that only land masses are modelled in the refined domain. ERA5-Land covers the period 1981-onwards, and as such the years 1981–2018 are available at present in the HYSETS database.
As both the ERA5 and ERA5-Land products are hourly and data are archived without UTC offsets, it was required to shift the data according to the grid point locations. The longitude of the watershed centroid was used to assess to which time zone it belongs. Based on this time zone, the hourly data were shifted from the same number of hours to realign the daily cycle between 00:00 and 24:00. For instance, a station located in the time zone -7 will have the whole time series shifted by 7 hours to match its proper daily cycle. The ERA5 and ERA5-Land data were downloaded from the Copernicus Climate Data Store, available at: https://climate.copernicus.eu/climate-reanalysis. Note that the HYSETS dataset contains modified ERA5 and ERA5-Land reanalysis data from the Copernicus Climate Change Service Information and that neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains.
Once the data processing was complete to bring it to the daily scale, the extraction followed the same process as for the NRCan and Livneh datasets, however, for the reanalysis products, the data are available for all watersheds given that reanalyses cover the entire globe.
Snow Water Equivalent (SWE) data
Two snow-water equivalent databases are provided in HYSETS. The first is a 9-year (2010–2018) time series of watershed-averaged daily high-resolution (roughly 1 km) data provided by the Snow Data Assimilation System (SNODAS) analysis34 available at: https://nsidc.org/data/g02158. The SNODAS data were averaged at the catchment scale using points within the watershed boundaries, or the closest point for the smallest of watersheds that did not contain a point within their limits. SWE data units are millimetres (mm) and represent the value expected on the ground for that day. Any missing values are replaced by NaNs. SNODAS data incorporate multiple sources of data and provide the best possible estimate of SWE at each point location. HYSETS simply averages those values at the catchment scale for users to easily interpret and analyse the data with respect to the rest of the data. SNODAS’ main limitation is that its spatial coverage includes the continental USA as well as the lower portions of Canada below 54°N latitude. This means that many snowy catchments in northern Canada and Alaska are not covered by SNODAS.
The second dataset is the ERA5-Land reanalysis product, which covers the period 1981–2018. It was extracted using the same method as for the ERA5-Land precipitation and temperature data and was also averaged at the daily scale using an hourly UTC offset depending on the time zone. One main advantage of the ERA5-Land reanalysis SWE is that it covers the entire globe and thus is available for all catchments, even those above 54°N that are not covered by SNODAS.
Physiographic data
One of the strengths of the dataset is the inclusion of a multitude of properties to describe and characterize each of the watersheds. The process is similar for all of the properties, but some variables required slightly more complex operations than others.
The first set of data was based on geographic and topographic properties and was derived from the EarthEnv-DEM90 digital elevation model35 available at: https://www.earthenv.org/DEM.html. The process was performed in a meta-software called PAVICS-Hydro (Power Analytics and Visualization for Climate Science – hydrological modelling toolbox) being developed for this purpose, available at: https://pavics-sdi.readthedocs.io/. This set includes mean watershed elevation (meters), slope (degrees), aspect (degrees), Gravelius index (unitless) and perimeter (kilometers). The elevation and perimeter are self-explanatory. The slope is the average slope when considering the individual elevation differences between tiles and can be seen as an indicator of the catchment relief, with higher slopes indicating more mountainous regions. The aspect is the main orientation of the catchment, i.e. where the average slope points towards. The Gravelius index is the ratio of the perimeter of the watershed compared to the perimeter of a circle of the same area. Higher values indicate more elongated or less compact catchments. All the DEM points falling within a watershed boundary were used to compute these characteristics.
The database also provides elevation band data for each catchment in 100-meter intervals. This information was extracted from the EarthEnv 90 m DEM by applying a zonal histogram to a reclassified DEM based on 100-meter intervals in the QGIS software. The data are provided in a separate.csv file “HYSETS_elevation_bands.csv” and represent the percentage of catchment area lying below that elevation. Therefore, the curves are cumulative sums of these areas. This will allow users to provide information to more complex routines and models such as the well-known CemaNeige snow accounting and melt model. The elevation bands can also be used to adjust precipitation rates and temperatures per precipitation bands based on the average catchment elevation and the elevations in the bands, depending on the desired lapse rates and correction methods.
The land use percentages reflect which fraction of the watershed is covered in the different classification categories. The North American Land Change Monitoring System (NALCSM) imagery data from 2010 was used for this purpose36,37. NALCMS was developed by Canada, the United States and Mexico to track the evolution of land use over time. A static dataset for 2010 is available and contains 19 land use classes values that were combined to form 7 meta-categories: forests, shrubs, croplands, wetlands, water, urban and permanent snow/ice. For example, coniferous, deciduous and mixed forests were combined into the “forest” category. The original data are available at the Commission for Environmental Cooperation website:

For each watershed, the NALCMS raster dataset was queried through the PAVICS zonal statistics toolbox for each of the 19 original categories. The values were then aggregated to the 7 categories used in the HYSETS dataset and the relative fraction of each was computed.
The dataset used to characterize catchment geology is the GLobal HYdrogeology MaPS (GLHYMPS) of subsurface permeability and porosity38. This dataset provides quantitative estimates of permeability and porosity below the soil horizon. Catchment averages of these two variables were calculated, by considering the contribution of each spatial polygon being weighted by the fraction of a catchment it covers. The arithmetic mean was used for porosity, but for permeability, the geometric mean was taken. The same process as for NALCMS was performed. However, a pre-processing of the GLHYMPS vector data was performed to transform it to a raster format. The same zonal statistics tools from PAVICS were used to extract the average values of both variables for each catchment. The permeability units are in m2 whereas the porosity is archived as a fraction. The GLHYMPS dataset is available at: https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=doi:10.5683/SP2/DLGXYO.
For all the above-mentioned properties, if for any reason the extraction process could not be performed (watershed out of product boundaries, unavailability at a given location) the values are replaced by NaNs and a flag was set in the metadata and properties file.
Monthly hydrometeorological data
One aspect that must be noted is that the data are all averaged temporally at the daily scale and spatially at the catchment scale. This means that for large catchments, it is possible that the daily data are not very representative of the localized precipitation and runoff events. For this reason, the HYSETS database also includes monthly-aggregated data for all catchments, which will allow evaluating products in terms of bias and mass balance over longer periods. The daily data are still made available for all catchments and the users are invited to consider which timescale is more appropriate for their use-case.
The monthly hydrometeorological data include data from the seven temperature and precipitation data sources as well as for the streamflow. SWE values were not provided at the monthly scale as they are typically not as variable as other variables. All monthly data are combined into a single netCDF file named “HYSETS_2020_monthly_meteorolgical_data.nc”. More

Characterization
Evidence for the successful incorporation of alumina into the PES membranes after SIS treatment can be seen in X-ray photoelectron spectroscopy (XPS) measurements (Fig. 1b), which show an absence of alumina before treatment and a strong intensity Al 2p peak after treatment consistent with Al2O3. Alumina incorporation was further confirmed by differences in membrane weight before (71.6 ± 0.1 g) and after (85.9 ± 0.5 g) SIS treatment, which suggests the treated films are 17% alumina by weight. The alumina content as determined by thermogravimetric analysis (TGA) was slightly higher at 28% (Supplementary Fig. 1a). This discrepancy could be explained by mass loss during the reaction of TMA with the PES membrane during SIS, making the actual loading higher than the mass change would suggest. Overall, however, the chemical composition of the original PES polymer is unchanged in the resulting organic–inorganic composite membrane, as confirmed by FTIR measurements (Supplementary Fig. 1b) and in agreement with the previous work37.
The LIG pyrolysis mechanism has been studied previously for other polymers, though not specifically for PES, and is thought to involve thermal decomposition of C–O and C–N bonds due to the rapid increase in temperature, followed by growth of ring clusters forming graphitic structures38. In the case of PES (and other sulfonated polymers), the transformation results in clusters with some insertion of sulfur in the graphene skeleton in the form of C-S-S and -C=S bonds22. Here, the conversion of PES to LIG is observed as a visual color change in the membranes from white to black (Supplementary Fig. 2) and is verified using Raman spectroscopy, which shows the presence of D, G and 2D bands characteristic of graphene containing materials (Fig. 1c). Optical images of the membranes lased at increasing laser powers (Supplementary Fig. 2) show that a critical laser power is required for conversion to LIG, as has been seen previously for other polymers13. The images also show that without the SIS treatment, the membranes transition through two regimes: at low but non-zero power, the membranes soften at their glass transition and become transparent (Supplementary Fig. 2); at a critical laser power, they exhibit the expected visual color change, though to not nearly as dark a color as the SIS-treated membranes and at higher laser powers than those required to convert SIS-treated membranes. SIS-treated membranes do not soften but instead graphitize directly, giving the first indication that the incorporation of alumina allows the membranes to resist changes to their nanoscale features during graphitization. Although the critical laser power that is required for LIG formation depends on lasing parameters such as the spacing between the laser scanning lines and laser speed, this same trend was observed at all setting tested, including the minimum spacing between scanning lines.
Cross-sectional scanning electron microscopy (SEM) images (Fig. 1d–f) demonstrate the powerful impact of SIS treatment on the membrane’s physical stability during lasing. Figure 1d shows the cross-sectional structure of a treated PES microfiltration membrane before lasing, along with a higher magnification image showing the micropores (Fig. 1g). When PES membranes without SIS treatment are lased, the membrane’s structure collapses into a dense bottom layer (Fig. 1f, k) and an exfoliated top layer (Fig. 1j). The membrane’s total cross-sectional thickness decreases to 38–58 μm compared to the initial 140 μm (Fig. 1d). In contrast, after lasing, SEM images of SIS-treated membranes have a thickness ranging from 90 to 132 μm (Fig. 1e, Supplementary Figs. 3–6) depending on the plane of the cross-section and the laser power used, showing that most of the membrane thickness is retained. Both the top 40.2 ± 1.0 μm of the membrane (high contrast in Fig. 1e, h), which is taken to be the lased region, and the remaining 85 ± 1.0 μm bottom layer (Fig. 1e, i) maintain an open and porous structure very similar to the starting membrane (see SI and Supplementary Fig. 7 for more detail about the membrane’s surface and cross-section structure). This indicates that graphitization happens in place without any macroscale deformation.
Performance
SIS-treated LIG membranes maintain the same permeability within uncertainty before and after lasing (872 Lm−2 h−1 bar−1) (Fig. 2a). Without SIS treatment, membranes show a dramatic decrease (from 1124 Lm−2 h−1 bar−1 to 35 Lm−2 h−1 bar−1) in permeability, owing to pore-closure in the subsurface.
Fig. 2: Performance of conductive membranes.

a Permeability of PES membranes (with and without SIS treatment) before and after lasing. Error bars represent the range of data from repeated measurements. b Sheet resistance of lased membranes with and without SIS treatment as a function of the laser power used. c Applied potential required to maintain a reducing current of 10 mA cm−2 using SIS-PES-LIG electrodes over 14 days.

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In addition to maintaining their permeability, SIS-treated membranes also exhibit relatively low sheet resistance. Conductivity measurements of the LIG-coated membranes with and without SIS treatment (measured by the Van der Pauw method) as a function of laser power (Fig. 2b) show that the SIS-treated membranes achieve a sheet resistance of 37.7 ± 0.7 Ω □−1 or a conductivity of 660 S/m, based on the thickness of the conductive region seen in Fig. 1e. This sheet resistance is slightly higher than LIG formed from polyimide polymer, which show sheet resistance values as low as 15 Ω □−1, but is comparable to CNT membrane coatings which exhibit similar sheet resistances3,39. In contrast, the sheet resistance of lased membranes without SIS treatment (PES-LIG), which only graphitize and become conductive at higher laser powers (Supplementary Fig. 2), is an order of magnitude higher, in excess of 1000 Ω □−1. Their conductivity is also highly anisotropic: the two-point probe conductivity of these membranes is much higher along the direction of the laser path compared to perpendicular to the laser path.
Beyond high electrical conductivity, during operation, conductive membranes must be electrochemically stable enough to sustain either a capacitive voltage to electrostatically repel foulants or a faradaic current to electrochemically degrade foulants, strip scalants, and generate gas bubbles to remove accumulated contaminants. To verify their electrochemical stability, SIS-treated, lased membranes were subjected a reducing current of 10 mA/cm2 sufficient to perform water electrolysis40. Testing was performed on strips of the membrane surface that were dipped into 0.1 M NaCl electrolyte solution. To prevent contact between the electrolyte and the electrical contact wire due to wicking, only a small fraction of the lased area was dipped into the solution, far from the electrical contact point. In this configuration, membranes showed no loss in performance up to at least 14 days of continuous operation (Fig. 2c). An initial drop in the voltage required to drive 10 mA cm−2 of current density is attributed to the wicking of water further up the test strip during the first day of testing. Thus, laser-scribed SIS-treated membranes make excellent candidates for separations that require conductive membranes, or any technology where templated conductive structures are required.
Mechanism of improved stability
Given these favorable results, we explored the mechanisms behind the improved stability of SIS-treated membranes during lasing (i.e. suppression of deformation) and the improved conductivity of the LIG formed. One possible mechanism behind the structural stability during lasing is a change in the glass transition temperature (Tg) of the membrane due to the addition of alumina. However, DSC analysis (Fig. 3a) shows only a minor increase in the glass transition temperature (Tg) from 212 °C to 230 °C after SIS treatment. These values are consistent with previously reported Tg values for PES membranes41,42 and further indicate that the inclusion of alumina does not prevent the polymer in the membranes from undergoing a glass transition. SEM images of the membranes after the DSC measurements (i.e. after heating them above their glass transitions) (Fig. 3b, c) show that the PES completely loses its original porosity, while the alumina infiltrated sample remains mostly porous, with slight deformation. This suggests that, despite the similarity in Tg values, the presence of alumina alters the rheological properties of the SIS-PES membranes and stabilizes the membrane structure under elevated temperatures above the Tg of PES (the laser irradiation increases the PES temperature well above 230 °C)13. To test this hypothesis, we performed dynamic mechanical analysis (DMA) to measure the tensile storage (G′) and loss (G′′) modulus of PES and SIS-PES as a function of temperature (Fig. 3d). While the PES sample undergoes a full transition from a glassy state at 200 °C to terminal flow (i.e. liquid-like) behavior at 275 °C with only a mild entanglement plateau, SIS-PES shows a slight relaxation above 230 °C, but then exhibits a prolonged plateau in G′ (around 0.1 GPa) up to the instrumental limit of 400 °C. The solid-like properties of the SIS-PES sample are also reflected by the minimal sample elongation during testing, especially compared to PES, which yields over 100% and prematurely ends testing (Supplementary Fig. 8). The dramatic difference in mechanical properties between the two samples and the extended plateau in G′ of SIS-PES suggests that the alumina has formed a continuous network that stabilizes the original membrane structure well beyond the Tg of PES43.
Fig. 3: Mechanism of structural resilience during laser pyrolysis.

a DSC scans of PES membranes with and without SIS treatment showing the similar glass transition temperatures of the polymer in the two membranes. SEM image of the b PES (scale bar 2 µm) and c SIS-treated PES membranes (scale bar 2 µm) after DSC measurement showing the different pore structure between the two membranes after heating. d Storage and loss modulus measurements of PES and SIS-treated PES membranes. e Cross-section SEM image of lased, SIS-treated membrane (scale bar 4 µm) and f its EDX line-scan along the yellow arrow shown in the SEM image. Lower magnification top surface SEM images of g lased PES (scale bar 100 µm) and h SIS-treated PES membranes (scale bar 100 µm). i D to G band intensity ratios and full width at half max of the D band of Raman spectra of SIS-treated PES membranes lased at increasing laser powers. Error bars represent the standard deviation of repeated measurements.

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While the infiltrated alumina is responsible for the mechanical structural resilience of the membrane during lasing, it is unlikely to be responsible for the improved conductivity and electrochemical stability of the LIG formed. Cross-sectional SEM and energy-dispersive X-ray spectroscopy (EDX) cross-sections of SIS-PES (Fig. 3e, f) show that after lasing, the top of the film is absent of aluminum introduced by SIS, likely due to sublimation under the high temperatures induced by the laser. At the LIG/PES interface, there is a small region where the alumina appears to have ripened into nanoparticles coating the polymer/LIG film but the conductive region of the resulting film is completely absent of the infiltrated alumina in its original crosslinked structure. Note that Fig. 3e is an SEM image of a membrane lased at lower laser power (14%) than the SEM image shown in Fig. 1e, which is why the thickness of the conductive layer is different. Further analysis of the PES-LIG and SIS-PES-LIG using XPS does not reveal significant chemical compositional differences. Both materials show primary C 1s peaks at a binding energy of 284.4 eV in XPS fine scans, consistent with sp2-bonded carbon13, with some additional higher binding energy peaks (Supplementary Fig. 9). While the PES-LIG shows a greater intensity of higher binding energy carbon peaks (286.2 eV), suggesting more ether carbon remains in the films after laser treatment, it is unlikely this difference in composition would lead to such a drastic difference in sheet resistance. Al 2p fine scans of the SIS-PES-LIG (Supplementary Fig. 10) also show that the aluminum within the film has remained in an oxide form, eliminating the possibility for the formation of Al metal. Raman spectra of PES-LIG and SIS-PES-LIG also do not exhibit significant enough differences to account for differences in conductivity. The D to G peak ratios in Raman spectra of carbonaceous materials are often used to make qualitative statements about the nature of LIG such as the crystallite size of graphitic clusters44. The peak ratios of the Raman spectra for PES-LIG and SIS-PES-LIG (Fig. 1c) suggest similar crystallite sizes ranging from 7–10 nm. However, LIG from untreated PES have broader peaks indicating increased disorder in these films relative to the SIS-treated samples45. In addition, some spots on the untreated PES films showed significant fluorescence (Supplementary Fig. 11a), indicating the presence of regions with minimal LIG coverage. Surface SEM images of the lased PES membrane without SIS show a heavily exfoliated structure with order-of-magnitude larger features (Supplementary Fig. 11a), and continuous regions of non-porous polymer underneath and in between, which is consistent with these Raman spectra. In contrast, spectra from SIS-treated PES after lasing showed consistent bands (Supplementary Fig. 11b) and SEM images of these membranes show a more homogeneous structure, similar to the pore-structure of the starting membrane.
The drastic differences in conductivity are therefore likely due to the less homogenous coverage of the lased surface by LIG. While membranes with and without SIS treatment show similar LIG thickness after lasing (Fig. 1e, f), lower magnification top-down SEM images of PES-LIG (Fig. 3g) reveal that after laser scribing, the laser creates linear trench structures of graphitized regions, separated by large gaps of non-porous polymer. These gaps between LIG structures also explain the high degree of anisotropy in conductivity mentioned earlier. A structural anisotropy is also visible in SIS-treated membranes (Fig. 3h). Previous work has shown that differences in lasing conditions can lead to vastly different LIG morphologies, in part due to differences in the resulting polymer temperature and anneal rate14. Here, regions outside of the direct laser path are still graphitized without any loss of porosity, but they likely do not reach the same annealing temperature and therefore exhibit different morphology.
Ultimately, the temperature that is achieved during laser irradiation plays a critical role in the resulting pyrolysis process and is affected by a combination of factors, such as the total absorption of IR light by the polymer and alumina (when present), the heat capacity of the membranes with and without alumina (which can be qualitatively deduced from the DSC data (Fig. 3a)), the structural changes due to polymer softening, the loss of porosity (without alumina), the rate of heat dissipation, and the total mass of polymer irradiated. While the relative impact of each of these factors was outside the scope of this work, it can be reasonably concluded that the presence of alumina increases the resulting membrane temperature during laser irradiation, since there is a reduction in critical laser power required for graphitization (Supplementary Fig. 2): the SIS-PES membranes can reach a higher temperature with lower laser energy and thus require less laser power to achieve the temperature required for graphitization.
The saturation in sheet resistance of the SIS-treated membranes above a laser power of 20–22% is also partially explained by the anisotropy in lased area. Initially, increasing the laser power is correlated with improved conductivity (Fig. 2b), as a greater fraction of the top surface of the membrane is converted to LIG and the laser continues to penetrate deeper into the membrane bulk. At 14% power, the laser is able to convert only parts of the membrane to LIG, which is seen as dark regions with low alumina content in surface SEM images, EDS maps, and EDS line scans (Supplementary Fig. 12). Cross-sectional images at 14% power show that the dark regions are part of a hemispherical path scribed by the laser (Supplementary Fig. 12). As the laser power is increased, the radius of this path grows and neighboring paths overlap until full surface coverage is achieved at which point the conductivity also saturates. We analyzed Raman spectra of the lased surfaces at different powers to see if higher laser powers changed the graphitic nature of the formed LIG. Between 14 and 18% laser power, there is an increase in the D to G peak intensity and area ratio, a narrowing of the D band full width at half max (Fig. 3i), and an emergence of a 2D peak (Supplementary Fig. 13). These changes plateau at higher powers, mirroring the conductivity measurements. These trends seen in the Raman spectra indicate that although initially, higher laser power leads to less disorder in the type of defects found on the LIG and increased stacking of graphitic clusters, defects found on the graphitic regions persist even at high powers46. Thus, continued improvement in the sheet resistance of the membranes will require alternative approaches, such as other chemical treatments besides alumina.
Interfaces and interfacial properties play a central role in many technologies other than membranes at the water and energy nexus2. PES membranes are taken as a prototypical example of porous polymers where nanosized features need to be preserved during laser scribing. The method of combining SIS of organometallic precursors into polymers with LIG formation described in this paper can be generalized to other applications where maintaining micro and nano-sized features of polymers at temperatures well above their Tg is desired. An even broader advantage of this approach is the improved mechanical and chemical properties that are observed at these high temperatures, without the need to change the chemistry of the underlying polymer28. Other modification approaches that could potentially be used, such as crosslinking of the polymer, would alter the polymer chemistry, potentially altering the material’s LIG forming properties. Since stability of LIG formed from polymers is a known concern47, the method described herein could be beneficial for all other polymers used for LIG formation as well.
In summary, we present a simple, solvent-free process for making conductive membrane coatings without altering underlying polymer structure. We demonstrate how infiltration with alumina stabilizes the PES membrane against deformation above the glass transition temperature37, allowing it to maintain its structure during laser treatment. These membranes are shown to be more conductive than LIG formed directly from the bare polymer, are electrochemically stable and maintain their permeability after lasing. These results demonstrate the immense versatility of hybrid polymer-ceramic materials as a promising class of materials to be used in conjunction with the LIG process. More

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Femtosecond laser fabrication of SWSA surfaces
A description of the experimental setup for fabricating the SWSA surface is provided in Supplementary Fig. 1a. In particular, a sheet of Al foil of 200 µm thickness and 22 mm × 40 mm dimensions, mounted on an xy translational stage, was scanned normal to the femtosecond laser beam (Ti: Sapphire, Spittfire, Spectra Physics) operating at a wavelength of 800 nm, 780 µJ per pulse energy and 1 kHz repletion rate. In a typical experimental procedure, the laser beam was focused onto the target surface with a focal spot size of ∼100 µm using a planoconvex lens with a focal length of 250 mm and scanned in a spiral manner. The scan speed was optimized for 0.5 mm s−1 and interline spacing was 100 µm to generate an SWSA surface with maximum optical absorbance.
Optical absorbance measurements in the ultraviolet–visible–near-inrared and mid-infrared regions
The hemispherical optical reflectance of the SWSA surface was measured in the spectral range of 0.25–2.5 µm using a PerkinElmer Lambda-900 double-beam spectrophotometer coupled with a 50-mm-diameter integrating sphere. Similarly, hemispherical reflectance in the mid-infrared region (2.5–25 µm) was measured using a Thermo Fisher Scientific Nicolet 6700 FTIR spectrometer coupled with a PIKE research integrating sphere. An accessory (B0137314) was used with the PerkinElmer 900 spectrophotometer to measure specular reflectance in the spectral range of 0.25–2.5 µm for incident angles ranging from 15° to 75° to demonstrate omnidirectional or Lambertian absorptive surfaces. In a similar manner, an accessory (PIKE VeeMAX III) coupled with the Thermo Scientific Nicolet 6700 FTIR spectrometer was used to measure specular reflection in the spectral range of 2.5–25 µm for incident angles ranging from 30° to 80°. As the SWSA sample is opaque, hemispherical/specular absorbance is complimentary to measured scattering/reflectance in ultraviolet–visible–near-infrared and mid-IR range; therefore, absorbance is obtained using A = 1 − R.
Surface topography and surface morphology measurements
The surface topography and depth profile of hierarchical microstructures on the surface of the SWSA sheets was measured using a 3D scanning laser microscope (Keyence VK 9710-K) with an elevation resolution of 0.2 µm. The surface morphology of the SWSA surface was measured using a SEM/FIB Zeiss-Auriga scanning electron microscope.
Wetting dynamics measurements
Two types of water-wetting-dynamics measurements were performed. In the first measurement, the SWSA sample was mounted onto a vertical platform (Supplementary Fig. 6) and 200 µl water was placed at the lower end of the SWSA sample. The video of water-wetting dynamics was recorded with a high-speed camera at 200 frames per s (Supplementary Videos 1 and 2). The video, shown in Supplementary Videos 1 and 2, speed was slowed down by 10× with video processing software and snapshots were captured at different moments to generate Fig. 2d and Extended Data Fig. 2a. In another set of measurements, the SWSA sample was mounted on a micrometre-control translational stage with motion in the vertical direction (Supplementary Fig. 7a). The lower end of the SWSA surface was allowed to touch the water surface placed on the computerized weighing balance. When the lower end of the SWSA surface touched the water surface, a sudden decrease in the mass of water was observed. The decrease in the water mass gives the water-wetting rate on a vertically mounted SWSA surface and, ultimately, the rate of water-mass uplifting.
Calibrating the solar simulator and power meter and designing the sample plane
The solar simulator (Sanyu) with an AM1.5G airmass filter was first calibrated for 1 Sun (1,000 W m−2) using a NREL-certified PV reference solar cell (PV Measurements). An output of a thermopile power meter (FieldMax II TO, Coherent), set at a wavelength of 500 nm, corresponding to 1,000 W m−2 from the calibrated solar simulator was used as a unit of 1 optical concentration. For example, thermopile power meter results for 283 mw for 1,000 W m−2 of incident flux from the solar simulator. The head of the pyroelectric power meter was circular in shape with a diameter of 19 mm (area, 2.83 cm2). A planoconvex lens (focal length, 300 mm; diameter, 150 mm) was mounted at the output port of the solar simulator to concentrate a 10 cm × 10 cm square beam of the solar simulator into a 4 cm × 4 cm square beam in the horizontal plane. The most uniform area of 2.5 cm × 2.5 cm (the sample size that was exposed with light is 2.0 cm × 2.0 cm) at the centre of the 4 cm × 4 cm beam was used for the measurement. To vary the optical concentration in the xy plane, we varied the current in the xenon lamp and waited for 20–30 min every time the current was changed for the solar simulator to stabilize before measurement. A time of 5–10 min was given to the thermopile head to obtain stabilized readings. For a given current passing through the xenon lamp, power was measured at the 2.5 cm × 2.5 cm central region of the 4 cm × 4 cm beam with an average time of 20 s. The detector was then moved 6 mm in the x direction to measure the time-averaged power at the next spot. Using this method, we measured the power at four locations in the xy plane (Supplementary Fig. 17) to estimate the error in the optical concentration (Fig. 3e). The current in the solar simulator was varied to adjust solar irradiance from 1,000 W m−2 (283 mW at thermopile head) to 5,000 W m−2 (1,415 mW). The solar simulator was turned on for 20–30 min to obtain a stabilized output, and times of 5–10 min were given to the thermopile head to obtain stabilized readings.
Temperature measurement of the SWSA surface
The SWSA sample was mounted onto the surface of polystyrene foam in the horizontal plane. Two thermocouples (TC1 and TC2) were mounted onto the front and back surface of the SWSA sheet or unprocessed Al sheet (Supplementary Fig. 17). The surface of the SWSA samples was irradiated with light from a vertically downward beam of the solar simulator. The outputs of the thermocouple were fed to the computer through an electronic data logger (TC08, Omega Engineering) and stored in the computer for further processing. The temperatures of the front and back surfaces of the SWSA and unprocessed Al sheets were measured for different optical concentrations (Extended Data Fig. 3).
Indoor water-evaporation measurements in the vertical and horizontal planes
Water-evaporation measurement in the horizontal plane
The SWSA sample of 20 mm × 36 mm was bent into a U shape to generate a square 20 mm × 20 mm horizontal light absorber and evaporator surface (working area) and two parallel surfaces, each of 8 mm × 20 mm, (auxiliary surfaces) to transport water on the absorber surface. The U-shaped sample was mounted onto a low-cost polystyrene thermal insulating foam (which is generally used in packaging; thickness, 7 mm), and cut into a circular shape to fit into the opening of a glass or plastic water container (Extended Data Fig. 4). The diameter of the insulating foam that was used for mounting was adjusted in such a way that it floated on the water surface. The water container along with the U-shaped SWSA sample mounted onto the insulating foam was placed onto a computerized electronic weighing balance (Radwag SMB-60/AS 60/220.R2) to measure the mass of the water with time with a sampling rate of 1 data point per second. A square aperture of 20 mm × 20 mm, which was cut into a piece of black thick cardboard and which was carefully aligned with the absorber surface, was used to prevent additional light falling onto the non-sample area to avoid additional solar-thermal heating (Extended Data Fig. 4e–g). First, the water vaporization mass in the dark, with and without the SWSA surface, under the same opening was measured for 1 h as a reference for self-evaporation. Later, the loss in water mass, with and without the SWSA surface, was recorded for different optical concentrations (COpt = 1–5). Water was changed after each measurement to delete any thermal storage history in the water.
Two K-type thermocouples, of which the first was mounted onto the SWSA surface and the second was installed just below the insulating foam, were used to measure the absorber surface and water temperature using an electronic data logger (TC08, Omega Engineering). Thermal images of the absorber surface and bulk water were recorded using an infrared camera (FLIR TG167; Extended Data Fig. 4h–k).
Water-evaporation measurements in the vertical plane
The SWSA sample (area, 20 mm × 28 mm) was used for water-evaporation measurements in the vertical plane. In this case, the surface area of 20 mm × 20 mm was used as a solar-thermal vapour generator (working area), while the remaining 8 mm × 20 mm area was used as an auxiliary surface to transport water to the absorber surface. The SWSA sample, which was vertically mounted onto the surface of polystyrene foam (Supplementary Fig. 20, Extended Data Fig. 4), was floated on the water surface. The complete system was placed onto a computerized balance to measure the loss in water mass for a different angle of mounting. The SWSA sample plane was bent at different angles (0°, 30°, 45° and 60°) from vertical to measure effects of angle of light incidence (light flux) on the evaporation rate. We first measured the dark-condition water-evaporation rate for each angle for 50 min, and then irradiated the absorber surface with COpt = 1 Sun to measure the loss in water mass under solar irradiation. The dark-condition evaporation rate was subtracted from the corresponding evaporation rate under light illumination. The average evaporation rate was measured by linear fitting of five different segments of 10 min.
Outdoor water-evaporation measurements
Two SWSA samples, each with an area of 20 mm × 30 mm, were bent at a height of 10 mm to obtain a 20 mm × 20 mm working area and a 10 mm × 20 mm auxiliary area for the water transport. The first sample was bent at a right angle to generate a flat absorber, whereas the second sample was bent at 65° from vertical to generate an absorber plane to directly face the Sun at a zenith angle of 25°. Both of these SWSA samples were installed onto the surface of polystyrene foam and allowed to float on the surface of the water. To compare the rate of water evaporation from flat and tilted samples (30 June 2018), the mass of water in each container was measured at intervals of 30 min for 8 h. For the next three consecutive days (1 July 2018–3 July 2018), the water container with the Sun-faced sample was placed onto a computerized balance to measure the water loss for 10 h, 12 h and 7 h (Extended Data Fig. 9) on 1 July, 2 July and 3 July, respectively. The temperature of the absorber surface was measured using a thermocouple and the corresponding solar irradiance was measured using a Apogee 420 pyranometer.
Solar-based water sanitation and water quality testing
Preparation of contaminated water
Contaminated water samples were prepared by dissolving a known amount of impurities into double-distilled water. To simulate 500 ppm of heavy-metal-contaminated water (Cd, Cr, Pb, Ni), 20 mg salt (Cd(NO3)2, Cr2O3, PbCl2 or NiCl2) of the corresponding heavy metal was dissolved into 40 ml of double-distilled water. Similarly, standard salt solutions of 104 ppm (saline water) were simulated by dissolving 400 mg of corresponding salts (NaCl, KCl, MgSO4 and CaCl2) into 40 ml of double-distilled water. Ethylene glycol and dyes are industrial pollutants used as coolants and colouring, respectively. A 10 ml solution (11.1 g) of ethylene glycol was ultrasonically dissolved into 40 ml of distilled water to prepare 2.77 × 105 ppm aqueous solution of ethylene glycol. Similarly, 4.33 mg of R6G dye was dissolved into 40 ml of double-distilled water to obtain a 108.25 ppm dye solution. Detergent and glycerin are two common domestic pollutants. Sodium dodecyl sulfate (SDS; C12H25SO4Na) is a surfactant that is generally used in detergents, dishwashing liquids, toothpaste and all types of soaps. The 10 mM solution of SDS was prepared by dissolving 144.186 mg of SDS in 50 ml of double-distilled water, which is the equivalent of 2.88 × 103 ppm detergent solution. Urea is an agricultural pollutant and the main component of human and animal excretory product. A urea solution of 800 ppm was prepared by dissolving 32 mg of urea (NH2CONH2) into 40 ml of double-distilled water.
Solar-based water-sanitation setup
The SWSA device, which consisted of the SWSA sample mounted onto polystyrene foam, floated on the water surface with the absorber surface in the horizontal plane. The water-wet absorber surface of the device was irradiated with normally incident light from a spectrally calibrated solar simulator to generate vapour. The water vapour condensates at the walls of a transparent and cleaned container were collected as pure water. In detail, the SWSA sample bent into a U shape with a working area of 30 mm × 30 mm and two parallel water transport surfaces of 30 mm × 8 mm was mounted onto the surface of polystyrene insulating foam (similar to that shown in Supplementary Fig. 21a,c). The system could float on the surface of contaminated water in a glass container (first container). The first container, with contaminated water and the absorber, was placed into another precleaned glass container (second container) with a transparent glass lid with ∼95% optical transparency. The outer surface of the first container and the inner surface of the second container was cleaned several times with double-distilled water to avoid pre-existing contaminants in the region between two containers in which pure water was collected. The complete system (Supplementary Fig. 21c) was placed below the solar simulator with COpt = 2. Water evaporated, condensed on the top and interior walls of the second container and collected in the region between two containers. Each contaminated water sample was evaporated for 2 h to obtain about 5–7 ml of purified water.
Water testing
Pure water samples, which were obtained from the solar sanitation of simulated contaminated water such as heavy metals and light metals (salts), were tested by Culligan Water—a NELAP-accredited water testing laboratory—using inductively coupled plasma mass spectroscopy. EPA standard method 200.8 R5.4 was used to measure the concentrations of heavy metal in the purified water, whereas EPA 200.7 R4.4 was used to measure the concentrations of Ca, Mg, K and Na.
Ultraviolet–visible optical absorption spectroscopy was used to measure the concentration of dye and urea in purified water using Beer–Lambert law. The standard solutions were first prepared by dissolving a known amount of solvents followed by its dilution. For example, 13.5 ppm to 0.005 ppm solutions of R6G were prepared to draw a calibration line. The concentration of dye/urea in the purified water was measured on the basis of the absorbance value at a given wavelength (Supplementary Fig. 24).
The concentration of ethylene glycol, glycerol and detergent in purified water was measured using a physical method of contact-angle measurements36. The standard solutions of each impurity in double-distilled water were prepared to draw a line of calibration. The contact angle for each of the standard solutions was measured by putting 200 µm droplets on a superhydrophobic surface. A calibration curve (contact angle versus concentration) was drawn for each impurity to obtain a concentration of impurity in the purified water.
A sample of dirty water, collected from a nearby pond, was solar sanitized using the SWSA surface (Supplementary Fig. 25a,b for dirty water and purified water, respectively). The water samples before and after solar sanitation were tested for the presence of bacteria using the commercially available Bacteria in Water Test Kit (PRO-LAB, BA110). The water sample (1 ml) from dirty or sanitized water sample was mixed thoroughly with the bacterial growth medium provided in the kit. The bacterial growth medium with the dirty or purified water sample was transferred separately into different sterilized Petri dishes and left for 48 h for bacterial growth. The bacterial density from solar-sanitized dirty water and the control sample was calculated using the colony-counting method32. The Petri dish corresponding to dirty water sample had more than 500 colonies (Supplementary Fig. 25c), whereas no colony was visible in the other Petri dish (Supplementary Fig. 25d) in which sanitized water was used. More