BUSCADOR RECOLECTA

[Methods] Experiment locations and climate Trans-Mediterranean translocation of Posidonia oceanica fragments took place between Catalunya (Spain), Mallorca (Spain) and Cyprus in July 2018 and were monitored until July 2019 (Fig. 1). Sea surface temperature data for each transplant site were based on daily SST maps with a spatial resolution of 1/4°, obtained from the National Center for Environmental Information (NCEI, https://www.ncdc.noaa.gov/oisst ) (Reynolds et al. 2007). These maps have been generated through the optimal interpolation of Advanced Very High Resolution Radiometer (AVHRR) data for the period 1981-2019. Underwater temperature loggers (ONSET Hobo pro v2 Data logger) were deployed at the transplant sites in Catalunya, Mallorca and Cyprus and recorded hourly temperatures throughout the duration of the experiment (one year). In order to obtain an extended time series of temperature at transplant sites, a calibration procedure was performed comparing logger data with sea surface temperature from the nearest point on SST maps. In particular, SST data were linearly fitted to logger data for the common period. Then, the calibration coefficients were applied to the whole SST time series to obtain corrected-SST data and reconstruct daily habitat temperatures from 1981-2019. Local climate data was also compared to the global thermal distribution of P. oceanica to assess how representative experimental sites were of the thermal distribution of the species (Supplementary materials). Collectively, seawater temperatures from the three locations span the 16th - 99th percentile of temperatures observed across the global thermal distribution of P. oceanica. As such Catalunya, Mallorca and Cyprus are herein considered to represent the cool-edge, centre and warm-edge of P. oceanica distribution, respectively. Transplantation took place toward warmer climates and procedural controls were conducted within each source location, resulting in six source-to-recipient combinations (i.e. treatments, Fig. 1). Initial collection of P. oceanica, handling and transplantation was carried out simultaneously by coordinated teams in July 2018 (Table S1). Each recipient location was subsequently resampled four times over the course of the experiment, in August/September 2018 (T1), October 2018 (T2), April 2019 (T3) and May/June 2019 (T4, Table S1). Between 60-100 fragments were collected for each treatment. A fragment was defined as a section of P. oceanica containing one apical shoot connected with approximately five vertical shoots by approximately 10-15 cm of rhizome with intact roots. Collection occurred at two sites within each location, separated by approximately 1 km. Within sites, collections were conducted between 4 – 5 m depth and were spaced across the meadow to minimise the dominance of a single clone and damage to the meadow. Upon collection, fragments were transported for up to one hour back to the nearest laboratory in shaded seawater. Handling methods In the laboratory, fragments were placed into holding tanks with aerated seawater, at ambient temperature and a 14:10 light-dark cycle. All shoots were clipped to 25 cm length (from meristem to the tip of the longest leaves), to standardise initial conditions and reduce biomass for transportation. For transport by plane or ferry between locations, fragments were packed in layers within cool-boxes. Each layer was separated by frozen cool-packs wrapped in wet tea towels (rinsed in sea water). All fragments spent 12 hrs inside a cool-box irrespective of their recipient destination, including procedural controls (i.e. cool-cool, centre-centre and warm-warm) to simulate the transit times of the plants travelling furthest from their source location (Fig. 1a). On arrival at the destination, fragments were placed in holding tanks with aerated seawater at ambient temperature as described above in their recipient location for 48 hrs, prior to field transplantation. Measurement methods One day prior to transplantation, fragments were tagged with a unique number and attached to U-shaped peg with cable-ties. Morphological traits for each fragment were measured and included: 1) length of the longest apical leaf, width and number of leaves 2) total number of bite marks on leaves of three vertical shoots per fragment, 3) number of vertical shoots, 4) leaf count of three vertical shoots per fragment and 5) overall horizontal rhizome length. A subset (n=10) of fragments per treatment were marked prior transplantation to measure shoot growth. To do this, all shoots within a single fragment were pierced using a hypodermic needle. Two holes were pierced side-by-side at the base of the leaf/top of the meristem. Transplant methods All transplant sites were located in 4 – 5 m depth in area of open dead-matte, surrounded by P. oceanica meadow. In Mallorca and Cyprus, fragments were distributed between two sites, separated by approximately 1 km. In Catalunya, a lack of suitable dead matte habitat, meant that all fragments were placed in one site. Fragments were planted along parallel transects at 50 cm intervals and with a 50 cm gap between parallel transects (Fig. S1). Different treatments were mixed and deployed haphazardly along each transect. Resampling methods and herbivory On day 10 of the experiment, a severe herbivory event was recorded at both warm-edge translocation sites. Scaled photos of all fragments were taken at this time to record the effects of herbivory on transplants. At the end of each main sampling period (T0 – T1, T1-T2 and T3 – T4), all pierced fragments were collected and taken back to the laboratory to measure shoot growth. At T1, T2 and T3, additional sets of fragments (n = 10 per treatment) were marked using the piercing method to record growth in the subsequent time period. In addition, at T1 and T3, n = 20 shoots within the natural meadow at each site were marked to compare growth rates between the native meadow and transplants. Underwater shoot counts and a scaled photo was taken to record fragment survivorship, shoot mortality, bite marks, and shoot length among all remaining fragments within each site and sampling time. In the laboratory, morphological measurements (described above) were repeated on the collected fragments and growth of transplant and natural meadow shoots was measured. Growth (shoot elongation, cm d-1) of the marked shoots was obtained by measuring the length from the base of meristem to marked holes of each leaf (new growth) of the shoot and dividing the leaf elongation per shoot by the marking period (in days). For each shoot, total leaf length (cm shoot-1) and the number of new leaves was also recorded. The rate of new leaf production (new leaves shoot-1 d-1) was estimated dividing the number of new leaves produced per shoot and the marking period. New growth was dried at 60 ºC for 48 hrs to determine carbon and nitrogen content of the leaves, and carbon to nitrogen (C:N) ratios. Carbon and nitrogen concentrations in the new growth leaf tissue was measured at the beginning of the experiment and each subsequent time point for each treatment. Nutrient analyses were conducted at Unidade de Técnicas Instrumentais de Análise (University of Coruña, Spain) with an elemental analyser FlashEA112 (ThermoFinnigan). Underwater photos of shoots were analysed using ImageJ software (https://imagej.net). Maximum leaf length on each shoot in warm-edge transplant sites (cool-warm, centre-warm and warm-warm) were recorded for the initial (day 10) herbivore impact, T1, T2 and T3 time-points and related to transplant nutrient concentrations. Herbivore impact was estimated as the proportional change in length of the longest leaf relative to initial length at T0. Thermal stress Long term maximum temperatures were recorded as the average of annual maximum daily temperatures in each transplant site, averaged between years from 1981-2019. Maximum thermal anomalies were calculated as the difference between daily temperatures in a recipient site over the course of the experiment and the long-term maximum temperature in the source site for each corresponding population. ‘Heat stress’ and ‘recovery’ growth periods of the experiment were defined as T0 -T2 (July-October) and T2-T4 (November-June), respectively, corresponding to periods of positive and negative maximum thermal anomalies. Thermal anomalies experienced by the different transplant treatments were plotted using the ‘geom_flame’, function in the ‘HeatwavesR’ package (Schlegel & Smit 2018) of R (version 3.6.1, 2019) ., 1. The prevalence of local adaptation and phenotypic plasticity among populations is critical to accurately predicting when and where climate change impacts will occur. Currently, comparisons of thermal performance between populations are untested for most marine species or overlooked by models predicting the thermal sensitivity of species to extirpation. 2. Here we compared the ecological response and recovery of seagrass populations (Posidonia oceanica) to thermal stress throughout a year-long translocation experiment across a 2800 km gradient in ocean climate. Transplants in central and warm-edge locations experienced temperatures >29 ºC, representing thermal anomalies >5ºC above long-term maxima for cool-edge populations, 1.5ºC for central and <1ºC for warm-edge populations. 3. Cool, central and warm-edge populations differed in thermal performance when grown under common conditions, but patterns contrasted with expectations based on thermal geography. Cool-edge populations did not differ from warm-edge populations under common conditions and performed significantly better than central populations in growth and survival. 4. Our findings reveal that thermal performance does not necessarily reflect the thermal geography of a species. We demonstrate that warm-edge populations can be less sensitive to thermal stress than cooler, central populations suggesting that Mediterranean seagrasses have greater resilience to warming than current paradigms suggest., Australian Research Council, Award: DE200100900. Horizon 2020 Framework Programme, Award: 659246. Fundación BBVA., Peer reviewed

29 pages. -- Content: The tracking process of adsorption of CdSe species on the SnSe surface; XRD patterns of SnSe and SnSe-x%CdSe nanocomposites; SEM images of SnSe-3%CdSe nanocomposites at the different stages; SEM images of annealed SnSe-x%CdSe nanopowders; Grain size evolution study for bare SnSe and SnSe-3%CdSe; SEM images at different magnifications of SnSe and SnSe-3%CdSe pellets; EBSD microstructure of SnSe and SnSe-3%CdSe pellets; XRD pattern of recrystallized CdSe; SEM images of annealed SnSe powder at 350°C; EDS elemental mapping for SnSe-3%CdSe; Surface treatment; Thermogravimetric analyses; SnSe-CdSe phase diagram; High-temperature XRD analyses of SnSe and SnSe-3%CdSe; Lattice parameters and unit cell volume of SnSe-3%PbS pellet; TE properties of SnSe-CdSe samples with different content of CdSe; Band structure changes in SnSe induced by the CdSe NPs; TE properties of SnSe and SnSe-3%CdSe measured in parallel direction; Heat capacity Cp of SnSe-3%CdSe; Percentage variations in the TE properties of SnSe-x%CdSe compared to SnSe; Lattice thermal conductivity (κL) calculation; Literature comparison; TEM images of SnSe-3%CdSe sample; Material stability and repeatability; Cylindrical pellet cutting; Theoretical zT prediction; Pellet density and composition; References., SnSe has emerged as one of the most promising materials for thermoelectric energy conversion due to its extraordinary performance in its single-crystal form and its low-cost constituent elements. However, to achieve an economic impact, the polycrystalline counterpart needs to replicate the performance of the single crystal. Herein, we optimize the thermoelectric performance of polycrystalline SnSe produced by consolidating solution-processed and surface-engineered SnSe particles. In particular, the SnSe particles are coated with CdSe molecular complexes that crystallize during the sintering process, forming CdSe nanoparticles. The presence of CdSe nanoparticles inhibits SnSe grain growth during the consolidation step due to Zener pinning, yielding a material with a high density of grain boundaries. Moreover, the resulting SnSe–CdSe nanocomposites present a large number of defects at different length scales, which significantly reduce the thermal conductivity. The produced SnSe–CdSe nanocomposites exhibit thermoelectric figures of merit up to 2.2 at 786 K, which is among the highest reported for solution-processed SnSe., Peer reviewed

10 pages. -- Figures and tables. -- Figure S1: SEM image of (a) bulk g-C3N4 and (b) ultrathin g-C3N4, (c) N2 adsorption-desorption isotherms of bCN and uCN. -- Figure S2: FTIR spectra of OAC, OLMA and TiO2 before and after ligands remove. -- Figure S3: Zeta potential distribution spectrum of TiO2 after ligands removal (a) and uCN (b). -- Figure S4: SEM image and EDS compositional maps of a T1/uCN1 composite. -- Figure S5: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S6: SEM image of T1/uCN2 and corresponding EDS spectrum. -- Figure S7: SEM image of T1/uCN2 and corresponding EDS spectrum; Figure S8: Chromatogram plots for 0.5 ml of standard hydrogen injected every half hour. -- Table S1: Gas Chromatography Peak Processing Data based on figure S8. -- Figure S9: Standard hydrogen curve for gas chromatography. -- Table S2: Exponential decay-fitted parameters of fluorescence lifetime of uCN, TiO2 and T1/uCN1. -- Figure S10: Photocatalytic hydrogen generation amount on bCN, TiO2 and T1/bCN1 during 4 h under simulated solar light irradiation; Table S3: Photocatalytic hydrogen production about TiO2/g-C3N4 based catalysts. -- Table S4: The AQE values with different incident light wavelengths for T1/uCN1. -- Figure S11: (a) Stability cycles of the T1/uCN1 for H2 evolution under simulated solar light irradiation; (b) TEM image of T1/uCN1 after 20 h photocatalytic H2 evolution reaction and (c) XRD pattern of T1/uCN1 before and after 20 h photocatalytic H2O2 evolution reaction., CN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCAProgramme / Generalitat de Catalunya., Peer reviewed

6 pages. -- Supplementary Figure 1. Current mean maximum summer temperature (average 𝑇!"# """""" for the period 1980-2005) across potential seagrass distribution. -- Supplementary Figure 2. Difference between current mean maximum summer temperature ( 𝑇!"# """""" ) and the Tlimit as a function of latitude. Negative and positive latitude values for southern and northern hemispheres, respectively. -- Supplementary Figure 3. Uncertainty associated to the time (in years) for mean maximum summer temperature to reach seagrass upper thermal limit (Tlim) at the warming rates projected under the RCP8.5 scenario around potential seagrass sites. -- Supplementary Figure 4. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP8.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas. -- Supplementary Figure 5. The time (in years) to reach Tlimit at the warming rates predicted under the RCP4.5 scenario around potential seagrass sites. -- Supplementary Figure 6. Time (in years) for mean maximum summer temperature to reach the upper thermal limits (Tlim) of temperate and tropical affinity seagrass flora at the warming rates projected under the RCP4.5 scenario around potential seagrass sites in the Mediterranean Sea and Queensland (Australia) coastal areas., Seagrasses have experienced major losses globally mostly attributed to human impacts. Recently they are also associated with marine heat waves. The paucity of information on seagrass mortality thermal thresholds prevents the assessment of the risk of seagrass loss under marine heat waves. We conducted a synthesis of reported empirically- or experimentally-determined seagrass upper thermal limits (Tlimit) and tested the hypothesis that they increase with increasing local annual temperature. We found that Tlimit increases 0.42± 0.07°C per°C increase in in situ annual temperature (R2 = 0.52). By combining modelled seagrass Tlimit across global coastal areas with current and projected thermal regimes derived from an ocean reanalysis and global climate models (GCMs), we assessed the proximity of extant seagrass meadows to their Tlimit and the time required for Tlimit to be met under high (RCP8.5) and moderate (RCP4.5) emission scenarios of greenhouse gases. Seagrass meadows worldwide showed a modal difference of 5°C between present Tmax and seagrass Tlimit. This difference was lower than 3°C at the southern Red Sea, the Arabian Gulf, the Gulf of Mexico, revealing these are the areas most in risk of warming-derived seagrass die-off, and up to 24°C at high latitude regions. Seagrasses could meet their Tlimit regularly in summer within 50-60 years or 100 years under, respectively, RCP8.5 or RCP4.5 scenarios for the areas most at risk, to more than 200 years for the Arctic under both scenarios. This study shows that implementation of the goals under the Paris Agreement would safeguard much of global seagrass from heat-derived mass mortality and identifies regions where actions to remove local anthropogenic stresses would be particularly relevant to meet the Target 10 of the Aichi Targets of the Convention of the Biological Diversity., Peer reviewed

The dataset contains data on sediment C02 efflux rates, carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the P. Oceanica meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO3 content. CO2 incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC., Organic carbon analysis: In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b) y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60) OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC., Sediment CO2 production: Container headspace CO2 gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO2 air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO2 in seawater (in μmol CO2 L−1) was calculated from the concentration of CO2 (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO2 remaining in seawater after equilibration with the air phase ([CO2]SW−eq) by, [CO2]SW−eq = 10−6 β [C CO2]Air P where β is the Bunsen solubility coefficient of CO2, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO2]Air is the CO2 concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO2 concentration in seawater before the equilibrium ([CO2]SW−before eq) was calculated (in ml CO2 /ml H2O) by [CO2]SW−before eq = ([CH4]SW−eq VSw + 10−6 ([CO2]Air −[CO2]Air background) VAir)/VSW Where VSw is the volume of seawater in the core or in the seawater closed circuit, [CO2]Air background is the atmospheric CO2 background level and VAir is the volume of the headspace or the closed air circuit. Finally, the initial CO2 concentration was transformed to µmol CH4 L−1 by applying the ideal gas law. CO2 efflux values were calculated from CO2 variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m-2 s-1)., The dataset provides data on sediment C02 efflux rates (μmol CO2 m-2 s-1), carbon emissions during the experiment (gm-2), % Organic Carbon, Organic Matter content (g m-2) of the Posidonia oceanica seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions., CO2 efflux.xlsx, dataset_units.xlsx, Sediment_Organic_Carbon.xlsx, Peer reviewed

16 pages. -- Scheme S1. Scheme of the synthesis of Co-TAA. -- Figure S1. FT-IR spectra of the model compound Co-TAA, and Poly-TAA-Co powder. -- Figure S2. (a-b) HRTEM images and (c-d) STEM images of Poly-TAA-Co. -- Figure S3. EELS chemical composition maps from the red squared area of the STEM micrograph. Individual Co L2,3-edges at 779 eV (red), N K-edges at 401 eV (green), O K-edges at 532 eV (blue), and C-K edges at 284 eV (grey) and composites of Co-N, Co-O, Co-C, N-O and Co-N-C. -- Figure S4. (a)-(b) Survey, high resolution C1s XPS spectra of Poly-TAA powder, respectively. -- Figure S5. (a) Survey, high resolution C1s XPS spectra of Poly-TAA-Co powder, respectively. -- Figure S6 TGA analysis of Poly-TAA-Co under argon by heating to 600 ℃ at the rate of 5 ℃/min. -- Table S1. Co K-edge EXAFS fitting parameters for Poly-TAA-Co. -- Figure S7. N2 adsorption and desorption of Poly-TAA (a), Poly-TAA-Co (b) and Poly-TAA-Co-CNT (c), respectively. -- Figure S8. (a) Total current density of Poly-TAA-Co-CNT (7:3). (b) FE of CO and H2 at various potentials for Poly-TAA-Co-CNT (7:3). -- Figure S9 Current density for H2 production on Poly-TAA-Co-CNT(1:1) and Poly-TAA-Co-CNT(3:7). and. -- Figure S10. Nyquist plots of the electrochemical impedance spectroscopy (EIS) data of (a) Poly-TAA-Co, (b) Poly-TAA-Co-CNT(1:1) and Poly-TAA-Co-CNT(3:7) electrodes after the activation process. -- Figure S11. Linear sweep voltammetry (LSV) curves of (a) Poly-TAA-Co. -- Figure S12. FE of H2 at various potentials on Co-TAA-CNT(3:7), CoPc-CNT(3:7) and Poly-TAA-Co-CNT(3:7). -- Figure S13. XRD patterns of Poly-TAA-Co-CNT loaded on carbon paper before and after CO2RR. -- Figure S14 HAADF-STEM (a, c), BF-TEM (b, d) and HRTEM micrographs (c, e) of Poly-TAA-Co-CNT (3:7) sample (before and after CO2RR). -- Figure S15. EELS chemical composition maps obtained from the red squared area of the STEM micrograph. Individual Co L2,3-edges at 779 eV (red), N K-edges at 401 eV (green), O K-edges at 532 eV (blue), and C-K edges at 284 eV (grey) and composites of Co-N, Co-O, Co-C, N-O and Co-N-C. (Poly-TAA-Co-CNT (3:7), after electrocatalytic CO2RR). -- Figure S16. Calculated energy diagrams for CO2 to CO at －0.5 V conversion on CoPc and CoTAA molecule, respectively. -- Table S2. The comparison of electrochemical reduction of CO2 to CO for reported cobalt based electrocatalysts., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and ICN2 and IREC are funded by the CERCA Programme /Generalitat de Catalunya., Peer reviewed

11 pages. -- PDF files includes: Experimental section; Photocatalytic experiment; Synthesis of GDY powder; Preparation of CuO/GDY/TiO2 and GDY/TiO2; Preparation of Pt loaded TiO2, tables and figures. -- Figure S1. The prepared GDY powder by using deprotection-free method. -- Table S1. Screening of catalysts and solvents for the direct coupling reaction of HEB-TMS. + entry 15 was performed under Ar conditions. -- Figure S2. GC-MS spectra of the DMF solution after reaction (balck line) and the standard curve of the corresponding compound (red line). -- Figure S3. ICP-Mass results for content of Cu in the prepared CuO/GDY samples. -- Figure S4. Low- and high-magnification SEM images of the prepared CuO/GDY samples. -- Figure S5. 1*1*1 unit crystal model of CuO and atomic supercell model illustration of the CuO nanoparticle oriented as in TEM images. -- Table S2. Comparison between the experimental and the theoretical bulk plane spacing distances and angles between planes. -- Figure S6. High-resolution XPS spectra of Si in GDY. -- Table S3. The atomic percentage of different elements in pure GDY. -- Figure S7. XRD spectra of TiO2 and CuO/GDY/TiO2 with different content. -- Figure S8. (a) Raman spectra and (b) enlarged spectra of TiO2 and CuO/GDY/TiO2 with different content. - -Figure S9. (a) the full XPS spectra of TiO2 and CuO/GDY/TiO2; (b) high-resolution XPS spectra of Cu in CuO/GDY/TiO2. -- Figure S10. UV-Vis spectra of TiO2 and CuO/GDY/TiO2 with different content. -- Figure S11. photocurrent test of CuO/GDY/TiO2, GDY/TiO2, CNT/TiO2 and GR/TiO2. -- Figure S12. Tauc plot of the prepared GDY powder. -- Figure S13. Valence band spectra of the prepared GDY powder by XPS. -- Figure S14. (a) High-resolution XPS spectra of Cu in CuO/GDY/TiO2 before and after photocatalysis; (b) fitting of Cu in in CuO/GDY/TiO2 after photocatalysis. In comparison, the satellite observed for Cu 2p (corresponding to Cu2+) is significantly reduced after illumination. -- Figure S15. The proposed photocatalytic mechanism of the CuO/GDY/TiO2 photocatalysts., Peer reviewed

20 pages. -- PDF file includes S1. Substrate fabrication and growth details; S2. Degradation of surface topography after thermal oxide removal prior nanowire growth; S3. Faceting of GaAs(Sb) vs GaAs nanowires; S4. The role of InGaAs growth temperature; S5. The role of InAs growth temperature; S6. InAs/InGaAs field effect mobility measurements: influence of the InGaAs buffer growth temperature; S7. InAs/InGaAs band structure simulations; S8. Transport measurements of InGaAs/GaAs(Sb) SAG nanowires without the InAs channel; S9. InAs/InGaAs field effect mobility measurements: influence of the InAs growth temperature, figures and tables., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed

11 pages. -- PDF includes: 1. Experimental section; 2. Cyclic voltammetries measurements; 3. Morphological and structural characterisation data from SEM, DRX, TEM, SAED and STEM; 4. Electrochemical characterisation data by impedance spectroscopy, figures and tables., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant no. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed

8 pages. -- Figure S1. SEM images of CoMoP. -- Figure S2. (a) SEM image of Co–Mo MOFs. (b–c) SEM images and (d) EDX spectrum of CoMoP. -- Figure S3. (a) SEM image of Na2MoO4-ZIF-67. (b–c) SEM images and (d) EDX spectrum Mo–CoP. -- Figure S4. (a) SEM image of ZIF-67. (b–c) SEM images and (d) EDX spectrum CoP. -- Figure S5. (a–d) TEM image of CoMoP. -- Figure S6. (a–d) HAADF-STEM micrographs of CoMoP. -- Figure S7. EELS chemical composition maps obtained from the red squared area of the STEM mi-crograph. Individual Co L2,3-edges at 779 eV (red), Mo M4,5-edges at 230 eV (green), P L2,3-edges at 132 eV (blue), N K-edge at 401 eV (pink) and C K-edge at 284 eV (orange). -- Figure S8. (a) OER and (b) HER polarization curves of CoMoP with different Mo content in 1.0 M KOH. -- Figure S9. Cyclic voltammograms for (a) CoMoP; (b) Mo–CoP; (c) CoP and (d) RuO2 in the non-faradaic region of 1.12–1.22 V vs. RHE at various scan rates. -- Figure S10. (a–c) SEM image and d) EDX spectrum of CoMoP after long term OER stability test-ing. -- Figure S11. (a–c) SEM image and d) EDX spectrum of CoMoP after long term HER stability test-ing. -- Table S1. Comparison of OER performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution. -- Table S2. Comparison of HER performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution. -- Table S3. Comparison of OWS performance of CoMoP with some previously reported CoP-based catalysts in 1.0 M KOH solution., ICN2 is supported by the Severo Ochoa program from Spanish MINECO (Grant No. SEV-2017-0706) and is funded by the CERCA Programme/Generalitat de Catalunya., Peer reviewed