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Study sites and data collectionDuring 2017 and 2018, we carried out 14 experiments in rivers located in temperate, tropical, and subarctic biomes to capture a gradient of river productivity and climatic characteristics (Table 1, Fig. 1). Apart from the Mekong and Sekong rivers in Cambodia that were impacted by plantations, rice cultivation, grassland, and urban areas (56% impacted land cover in the Mekong and 38% in the Sekong), the selected rivers were predominantly in pristine areas (impacted land-use ≤ 8%), although two rivers in Mongolia were affected by livestock grazing (with 26% of land cover at the Khovd and 59% in the two Zavkhan rivers).We conducted traditional O2 concentration metabolic assessments, assessments of isotopic fractionation, and 24 h characterization of δ18O2 at each site. We measured changes in dissolved O2 concentrations and temperature every 10 min over at least 24 h with at least one MiniDOT logger (PME, Vista, California, USA). We calibrated for drift using the average measurement values made in 100% saturated water for at least 30 min before and after each deployment to allow adjustment to temperature and placed sensors in the river for at least 30 min prior to using data to allow equilibration to temperature (following methods detailed in ref. 52).We collected δ18O2 samples by hand every 2 h during the same 24-h period of the O2 concentration measurements in pre-evacuated 100 mL vials loaded with 50 µl HgCl2 as a preservative and sealed with septum stoppers (Bellco Glass Inc., Supelco, Vineland NJ). We analyzed samples for δ18O2 at the Nevada Stable Isotope Lab of the University of Nevada, Reno with a Micromass Isoprime (Middlewich, UK) stable isotope ratio mass spectrometer. We followed the method described by ref. 17 and injected 1.0–2.5 mL of headspace gas taken from the serum bottles using a gastight syringe (SGE, Australia) into a Eurovector (Pavia, Italy) elemental analyzer equipped with a septum injector port, and a 1.5 m long molecular sieve gas chromatography column. Water-δ18O was also collected at each site every 2 h and analyses were performed using a Picarro L2130-i cavity ringdown spectrometer at the Nevada Stable Isotope Lab of the University of Nevada, Reno. δ18O2 values are reported in the usual δ notation vs. VSMOW in units of ‰, with an analytical uncertainty of ±0.2‰ for δ18O2, or an analytical uncertainty of ±0.1‰ for water-δ18O.We characterized physical characteristics at each site to provide parameters to estimate whole-system metabolism. We measured conductivity, slope, and flow velocity and depth at ten transects using a flow meter when wadeable or with an Acoustic Doppler Velocimeter (Sontek, Xylem, San Diego, CA) when rivers were not wadeable. At each site, we measured light as photosynthetically active radiation (PAR) every 10 min, using Odyssey PAR loggers (Data Flow Systems, Christchurch, New Zealand) calibrated with a Li-Cor PAR sensor (Lincoln, Nebraska, USA).At each site, we also directly measured biofilm ash-free dry mass (AFDM) from 8 to 12 rocks (53). The material was scrubbed from the rocks, agitated, filtered (Whatman glass microfiber GF/F filters). Rock area was estimated with calibrated pictures processed with the ImageJ processing program (National Institutes of Health and the Laboratory for Optical and Computational Instrumentation LOCI, University of Wisconsin). For AFDM analyses, samples were dried, and weighed before and after combustion.Additionally, we collected data on the percentage of impacted land use in the watershed above each sampling site: for the Mekong and the Sekong we used Landsat satellite imagery from ref. 54, for the US and Mongolian sites land use characteristics were derived from the National Land Cover Database55 and for Patagonia we used the Chilean national land use inventory maps from ref. 56.δ18O2 stable isotope fractionation during respiration in sealed recirculating chambersModels based on oxygen isotopes are sensitive to the oxygen isotope fractionation factor (αR) during respiration used; αR can vary widely among sites and is influenced by temperature and water velocity30. We used in our models the range of αR values measured by30 using sealed Plexiglas recirculating chambers as in ref. 57. These measurements were done at the same time as the 24 h δ18O2 sample collections in the rivers of this study. We placed rocks, sediment, macrophytes (macrophytes dominated in the Zavkhan 1 site) inside the chambers, depending on the site’s dominant substrata (see ref. 30 for more details on chamber measurements). We collected water samples in the chambers for δ18O2 analyses before and after the incubations and the O2 isotope fractionation factor was calculated using Eq. (2).$$delta =(delta i+1000){F}^{left(alpha -1right)}-1000$$
(2)
where δ is the O2 isotopic composition of dissolved oxygen at the end of the dark incubation, δi is the O2 isotopic composition of dissolved oxygen at the beginning of the dark incubation, F the fractional abundance of O2 concentration remaining at the end of the dark incubation, and α is the isotopic fractionation factor during respiration.Ecosystem metabolism O2 single station modelingWe modeled metabolism as a function of GPP, ER, and reaeration with the atmosphere, using the single-station open-channel metabolism method4 using the same approach as15, given in Eq. (3).$${O}_{{2}_{(t)}}={O}_{{2}_{(t-1)}}+left(left(frac{{GPP}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+frac{{ER}}{z}+{K}_{{O}_{2}}left({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)right)triangle t$$
(3)
where GPP is gross primary production in g O2 m−2 d−1, ER is ecosystem respiration in g O2 m−2 d−1, ({K}_{{O}_{2}}) is the reaeration coefficient (d−1). PPFD is photosynthetic photon flux density (µmol m−2 s−1), z is mean stream depth (m), and ∆t is time increment between logging intervals (d). We used Bayesian inverse modeling approach to estimate the probability distribution of parameters GPP and ER that produce the best model fit between observed and modeled O2 data. We fixed site-specific ({K}_{{O}_{2}}) estimates using K600 (d−1) (normalized beyond gas-specific Schmidt number conversions among gases58) based on prior work characterizing K using BASE59, and converted these prior estimates of K600 to ({K}_{{O}_{2}})using appropriate temperature corrections. We estimated daily GPP and ER from diel O2 data only (Eq. (3)) to be used as prior estimates of daily GPPO2 and ERO2 in the coupled O2 and δ18O2 model (Eqs. (4a) and (4b))15, where the mean and SD of GPP and ER from the O2 _only method were used as prior estimates of GPPO2 and ERO2 in the dual O2 and δ18O2 model described below.Ecosystem metabolism: Diel δ18O2 modelingWe also modeled metabolism using an updated version of the model developed by ref. 15 coupling high-frequency O2 concentration data with δ18O2 collected every 2 h throughout the same 24 h period of the O2 concentration measurements. With this model, daily rates of ecosystem metabolism are derived from diel changes in δ18O2 and O2, where values of δ18O2 are converted to g 18O m−3 (18O2 in Eq. 4b) and modeled as a function of water isotope values, isotope fractionation, reaeration with the atmosphere, ER, and GPP. As with Eq. 3, the ratio of light at the previous logging time (({{PPFD}}_{left(t-1right)})) relative to the sum of light over 24 h (({sum {PPFD}}_{24h})) is used to characterize times when GPP is zero and only ER is taking place (Eqs. (4a) and (4b)):$${O}_{{2}_{left(tright)}}= , {O}_{{2}_{left(t-1right)}}+left(frac{{{GPP}}_{O2}}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}right)+left(frac{{{ER}}_{O2},xtriangle t}{z}right)\ +left({K}_{{O}_{2}}xleft({O}_{{2}_{{sat}left(t-1right)}}-{O}_{{2}_{left(t-1right)}}right)xtriangle tright)$$
(4a)
$${18O}_{{2}_{(t)}}=, {18O}_{{2}_{(t-1)}}+left(frac{left({{GPP}}_{O2}+{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{{sum {PPFD}}_{24h}}x,{alpha }_{P},x,{{AF}}_{W}right)\ +left(frac{{{ER}}_{O2},xtriangle t}{z}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left(frac{left(-{dielMET}right)}{z}xfrac{{{PPFD}}_{left(t-1right)}}{sum {{PPFD}}_{24h}}x,{alpha }_{R},x,{{AF}}_{{DO}}left(t-1right)right)\ +left({K}_{{O}_{2}}x,{alpha }_{g}xtriangle t,xleft(left({O}_{{2}_{{sat}left(t-1right)}}x,{alpha }_{g},x,{{AF}}_{{atm}}right)-{18O}_{{2}_{(t-1)}}right)right)$$
(4b)
Where GPPO2 and ERO2 (g O2 m−2 d−1) refer to the values obtained from diel O2 only, dielMET (g O2 m−2 d−1) is the diel metabolism term that allows for the estimation of diel ER and GPP from 18O2, KO2 is the O2 gas exchange rate (d−1), z is mean stream depth (m), PPFD is photosynthetic photon flux density (µmol m−2 s−1), Δt is time step between measurements (d), 18O2 is the concentration of 18O in dissolved O2 (g 18O m−3), AFDO is atomic fraction of dissolved O2 (mol18O:mol O2, measured), AFw is atomic fraction of H2O (mol 18O:mol O2, measured), AFatm is atomic fraction of atmospheric air (mol18O:mol O2, literature), αg is the fractionation factor during air–water gas exchange (0.9972, from ref. 60), αR is the fractionation factor during respiration measured in the chambers (varied by site30; Fig. 1), αp is the fractionation factor during photosynthesis (1.0000 from ref. 60).The inverse modeling approach finds the best estimates of parameters to match measured and modeled dissolved O2. The model assumes that the measured changes in O2 concentration represent the actual net diel changes in O2 concentration and uses an additional parameter, dielMET, that is a function of the isotopic enrichment occurring during respiration, derived from diel 18O2. This parameter increases daily ERO2 and GPPO2 of the same amount, adding and subtracting dielMET, to obtain daily δ18O2-ER and δ18O2-GPP, respectively.We estimated the posterior distributions of unknown parameters (ERO2, GPPO2, and dielMET) using a Bayesian inverse modeling approach15 and Markov chain Monte Carlo sampling with the R metrop function in the mcmc package61,62. Each model was run for at least 200,000 iterations using nominally informative priors based on the range of ERO2 and GPPO2. For dielMET, we used a minimally informative uniform prior distribution (0–100 g O2 m−2 d−1). We removed the first 10,000 iterations of model burn-in and assessed quality of model fit. Model runs using the minimum, average, and maximum αR values measured in the field recirculating chambers were also compared, and we selected the αR and report associated model metabolism estimates that generated the lowest sum of squared differences between the observed and modeled O2 and 18O2 diel values.Temperature-normalized comparisonsTo test the effect of temperature from the daily δ18O2-ER and δ18O2-GPP rates and account for daily variations in temperature, we normalized estimates from models to 20 °C (and report them as 20δ18O2-ER and 20δ18O2-GPP) for comparison with O2-derived metabolism estimates following33 with Eq. (5):$${rate},{at},20,{}^circ C=frac{{2.523* e}^{(0.0552* 20)}}{{2.523* e}^{(0.0552* {t}_{1})},* {rate},{at},{t}_{1}}$$
(5)
Where t1 is site temperature and rate is the measured rate (i.e., GPP or ER) at t1.Statistical analysesWe used multiple linear regression to find the best predictor of the magnitude of diel 20δ18O2-ER and differences between sites. To select the best model, we performed a stepwise variable selection and selected the best model based on the lowest AIC. Tested variables included percentage of impacted land use (%), 20δ18O2-GPP (g O2 m−2 d−1), conductivity (µS/cm), ash-free dry mass (AFDM, g), slope (%), water depth (m), and flow velocity (m/s) measured in the field. We used ANOVA to test the relative contribution of each variable selected with the AIC to total variance. Analyses were run with the R software61.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More

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Soil sampling and determination of soil physical properties and synoptic dataSoil samples were taken from two coastal deserts in the north and south of Iran. Details of their geographic distribution and eco-physiological characterization were shown in Table 1. A total of 2 kg of soil samples were collected from 2 distinct sampling locations ranging in depth from 0 to 30 cm, and the samples were dried for 3 days at room temperature and in the dark before sifting. The soil samples were sieved using a 2 mm sieve to remove stones and other inert material before being stored in zip-top bags. Table 1 lists the soil samples’ physical characteristics, including soil texture (sand 2–0.02 mm; silt 0.02–0.002 mm; clay 0.002 mm), pH, and the proportions of clay, silt, and sand. Synoptic data from the past 10 years (2009–2019), including the average annual temperature, maximum temperature, minimum temperature, average rainfall, average annual wind speed, and maximum wind speed, were obtained from the I.R.OF Iran Meteor (http://www.irimo.ir/far/index.php).Bacterial isolation and effect of manure-based medium on their growthAccording to Chen et al. 2005, the soil-borne bacteria were isolated using direct-spreading method. For this essence soil samples were treated through a series of dilutions. The mixture of 1 g of soil sample was vortexed for 1 min after being suspended in 2 ml of sterile physiological saline (0.9% w/v NaCl). The mixture was then diluted serially (typically 10–1 to 10–7), and level 100 μl of the diluted soil samples were scattered on the surface of solidified plates using glass spreaders. The samples were then incubated for 1 to 3 days at 30 °C in an inverted posture without light. For bacterial isolation, we used eleven culture media including Nutrient Agar (NA), Nutrient Agar plus MnSO4 (NA + MnSO4), LB, Moller Hinton Agar (MHA), Acidithiobacillus (APH) medium, Violet Red Bile Lactose (VRB) agar medium, GYM Streptomyces medium, DPM medium, Azospirillum medium, Azotobacter medium and Manure based medium (MB).To prepare MB medium, dry animal manure and distilled water (1:6 w/v) were combined to create MB medium, which was then let to sit at room temperature for 16 h. The resulting mixture was then centrifuged at 5000 rcf for 30 min after being filtered twice. The next stage involved adding Hoagland salts (10% w/v) to the final extract, adjusting the medium’s pH to 5.8 ± 0.02, and autoclaving it for 20 min at 121 °C and 1.5 kPa. Before sterilization, bacteriological agar (1.5 w/v) was employed as a gelling agent to solidify the medium.After bacterial isolation on NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospibrillum media, the growth of all isolates was evaluated on an MB medium. To investigate isolates biomass in the same condition, we elected MB medium. First, the bacteria were grown in the liquid form of NA, NA+ MnSO4, LB, MHA, APH, VRB, GYM, DPM, and Azospirillum and Azotobacter media at 30 °C for 48 h, then 103 cells of each isolate were transferred to 48 wells plates containing MB medium, and plates were incubated at 30 °C for 10 h. Then, the growth of bacteria was read at an optical density (OD) of 630 nm 10 h after inoculation, the experiment was performed with three replicates. In the following step, CFU/ml equivalent to each OD was obtained by inoculating the uniform amount of liquid culture of the isolates on the solid form of MB medium at 30 °C for 16 h.Phenotypic characterization and biochemical identification of bacterial isolatesThe morphological analysis of the cell shape, colony (i.e., shape, color, and size), and biochemical tests were used to identify the bacterial isolates. Biochemical characterization was carried out By using gram staining, KOH27, oxidase, and catalase tests. For this essence, following Bartholomew’s method28, gram staining of bacteria was studied 48 h after inoculation on MHA, and the non-staining KOH method was used to confirm the results. Using 0.5 ml of a 10% hydrogen peroxide solution, a catalase test was conducted, and the generation of gas bubbles was monitored. Using biochemical oxidase discs, the oxidative activity of 27 isolates was investigated.Effect of abiotic stresses on bacterial isolatesTo determine the effect of abiotic stresses on isolates alkaline (MH medium with pH  10), salinity (MH medium supplemented with the final concentration of 100 mM NaCl), osmotic [MH medium supplemented with 25% polyethylene glycol (PEG) Mn6000], and thermal stresses (MH medium incubated at 15 °C for cold stress and 60 °C for heat stress) were screened. For all experiments, the incubation period was 15 h, and plates were kept in a dark condition.MALDI-TOF MS identification of isolatesSoil bacterial isolates were subcultured twice on MHA and incubated at 30 °C for 24 h before MALDI-TOF MS measurement. Then ∼0.1 µg of cell material was directly transferred from a bacterial colony or smear of colonies to a MALDI target spot. After drying at laboratory temperature, sample spots were overlaid with 1 μl of matrix solution (10 mg/mL a-cyano-4-hydroxycinnamic acid in 50% acetonitrile and 2.5% trifluoroacetic acid) and each measurement was carried out in triplicate (technical replicates). MS analysis was performed on an Autoflex MALDI-TOF mass spectrometer (Bruker Daltonics, Germany) using Flex Control 3.4 software (Bruker Daltonics, Germany). Calibration was carried out with the use of the Bacterial Test Standard (Bruker Daltonics, Germany). Soil isolates with a valid MALDI-TOF MS score of 2 were undoubtedly assigned to the genus/species level. For bacterial classification and identification, BioTyper 3.1 software (Bruker Daltonics, Germany) equipped with MBT 6903 MPS Library (released in April 2016), the MALDI Biotyper Preprocessing Standard Method, and the MALDI Biotyper MSP Identification Standard Method adjusted by the manufacturer (Bruker Daltonics, Germany) were used. Only the highest score value of all mass spectra belonging to individual cultures (biological and technical replicates) was recorded25. The score between 2.3 and 3.00 shows highly probable species-level identification and between 2.0 and 2.29 represents genus-level identification and probable species level of identification. A score between 1.7 and 1.99 indicates probable genus-level identification29.Effects of bacterial isolates on plants growthThe Seed and Plant Improvement Institute of Karaj (Karaj, Iran; http://www.spii.ir/homepage.aspx?site=DouranPortal&tabid=1&lang=faIR) provided the maize, canola, and wheat seeds (Zea mays. Var Kosha; Brassica napus Var Nima; Triticum aestivum Var Kalate). In greenhouse trials, 2 × 103 cells/seed of soil-borne isolates cultured in a manure-based medium were inoculated to maize, canola, and wheat plants. During the studies, sand that had been acid washed and autoclaved was used for planting. For three weeks, seedlings were kept under a 16/8 h day/night photoperiod with a 25 °C temperature. Three replications of a complete randomized block design were used for the colonization experiment’s treatments. Under the bacterial treatments, measurements were made of the plant growth parameters including shoot dry biomass (mg), root dry biomass (mg), shoot length (cm), root length (cm), shoot density (mg/cm), root density (mg/cm), and shoot/root weight (mg). Samples were dried at 60 °C for three days to measure dry biomass.Statistical analysisStatistical analysis was done by R software (version 4.1.3). One-way analysis of variance (ANOVA) was used to determine the significance of the experiment, and Fisher’s protected Least Significant Difference (LSD) test with a P-value of 0.01 was performed to separate the means. Furthermore, PCA analysis has been carried out based on the Clustvis package and the SVD imputation approach.Ethics approval and consent to participateAll authors agree to the ethics and consent to participate in this article and declare that this submission follows the policies of Scientific Reports. Accordingly, the material is the author’s original work, which has not been previously published elsewhere. The paper is not being considered for publication elsewhere. All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.Ethics for research involving plantsAll authors confirmed that experimental research and field studies on plants, including receiving the seeds from the Seed and Plant Improvement Institute of Karaj, complied with relevant institutional, national, and international guidelines and legislation. Furthermore, methods were conducted according to the relevant guidelines and regulations. More

We are pleased to announce the winners of Rising Stars in Polymer Science 2022 as young influential. Polymer Journal has been enriched by the complex of wonderfully talented and diverse groups of these young scholars in addition to outstanding teams of well-established senior researchers. They bring a variety of new insights, both personal and professional, to the task of better understanding polymer science and engineering. Here they provide us with an array of novel observations drawn from such disciplines as synthesis, structure and physical properties and functions and applications. We believe our readers will appreciate the opportunity to learn new voices in this special issue.
Daisuke Aoki

Chiba University
Daisuke Aoki currently serves as an Associate Professor in the Department of Applied Chemistry and Biotechnology, Faculty of Engineering, at Chiba University. He obtained his Ph.D. from Tokyo Institute of Technology in 2014 under the tutelage of Prof. T. Takata. Between 2014 and 2017, he served as a specially appointed Assistant Professor in the group of Prof. T. Takata. From 2017 to 2022, he was an assistant professor at Tokyo Institute of Technology in the group of Prof. H. Otsuka. From 2018 to 2022, he also served as Japan Science and Technology Agency (JST) PRESTO Researcher. In 2022, he was appointed to his current position at Chiba University. His research is focused on the functional polymers with applications in materials science, the topological polymers, and the polymer recycling system. He has received the Award for Encouragement of Research in Polymer Science (2017) and The Young Scientist Lecture Award of the Kansai Regional Chapter (2020) from the Society of Polymer Science, Japan.
Rajashekar Badam

Japan Advanced Institute of Science and Technology
Rajashekar Badam completed M.Sc in Chemistry from Sri Sathya Sai Institute of Higher Learning, India in 2011. He received his Ph.D. in Materials Science from Japan Advanced Institute of Science and Technology (JAIST) with an “outstanding graduate award for the year 2016” in the area of carbon based electrocatalysis. Further he worked at Toyota Technological Institute as Postdoctoral fellow. In April 2018 he joined Matsumi lab, JAIST as Asst. Professor and since Oct 2020 he has been promoted to Sr. Lecturer in the same group. He has around 25 international publications and 10 patents (granted/pending) to his credit. His key research interest lies in organic-inorganic hybrid energy materials as catalysts, cathode material for metal air batteries, anode materials for Li-ion batteries and polymer binder materials for battery application.
Yu-Cheng Chiu

National Taiwan University of Science and Technology
Yu-Cheng Chiu joined the Department of Chemical Engineering at National Taiwan University of Science and Technology (Taiwan Tech). as a tenure-track assistant professor since August 2017. Currently, his major interests are the elastic and self-healing semiconducting materials, soft organic devices including transistor and transistor memory, and morphology characterization by synchrotron technique. Prior to joining the faculty, Yu-Cheng was a postdoc in the Zhenan Bao research group at Stanford University when he devoted on the research of intrinsically stretchable/healable semiconducting polymer and high-performance OFET by solution shearing technique. Before moving to Stanford, he received his Ph.D. degree under the supervision of Prof. Wen-Chang Chen in December 2012 from the Chem. E at National Taiwan University and then stayed in the same group for his first postdoctoral research until Oct. 2014. He also experienced international internship program as a Ph.D. student in 2010 and special appointed assistant professor position in 2018 for polymerization research in the group of Prof. Toyoji Kakuchi and Prof. Toshifumi Satoh at Hokkaido University.
Nagoya University
Yuya Doi received his Ph.D. degree under the supervision of Prof. Yushu Matsushita and Assoc. Prof. Atsushi Takano from Nagoya University in 2016. He worked as a Program-Specific Assistant Professor in the group of Prof. Hiroshi Watanabe at Kyoto University in 2016–2017, and was a visiting scholar in the group of Prof. Dimitris Vlassopoulos at FORTH, Greece in 2017. Then, he worked as a postdoctoral researcher at Nagoya University (in the group of Prof. Yushu Matsushita) from 2018, and at Forschungszentrum Jülich, Germany (in the group of Prof. Stephan Förster) from 2019. Since 2020, he has been an Assistant Professor at Nagoya University working with Prof. Yuichi Masubuchi and Assoc. Prof. Takashi Uneyama. His research interest is fundamental physical properties of model polymers studied by rheological and scattering methods.
Yuuka Fukui

Keio University
Yuuka Fukui received Ph.D. degree from Keio University in 2012 under the supervision of Professor Keiji Fujimoto. She was a JSPS research fellow (DC2) from 2010 to 2012. She joined the laboratory of Professor Keiji Fujimoto at Keio university as a research associate in 2012 and was promoted to an assistant professor in 2017. Her research interests focus on the design and synthesis of polymeric materials (particles, porous materials, membranes) and organic–inorganic hybrid materials inspired from biological systems. Her current research also includes development of functional materials to aim for applications in drug and cosmetic delivery systems and tissue engineering.
Mikihiro Hayashi

Nagoya institute of technology
Mikihiro Hayashi received his Ph.D. degree from Nagoya University (Prof. Yushu Matsushita group) in 2015. During his doctor course, he had been selected as a JSPS research fellow (DC2) and experienced researches in ESPCI Paris-Tech (Prof. Ludwik Leibler) and in Shanghai Jiao Tong University (Prof. Xinyuan Zhu). He then re-joined Ludwik Leibler’s group as a postdoc, and experienced another postdoc in Prof. Masatoshi Tokita in Tokyo institute of technology. In 2017, he became an assistant professor in Prof. Akinori Takasu group (Nagoya institute of technology), and currently manages his own laboratory as a PI. His research interest is the design of functional cross-linked materials. As recent awards, he won the SPSJ polymer research encouragement award (year—2019) and SPSJ award for the outstanding paper in Polymer Journal sponsored by ZEON (year—2021).
Kanazawa University
Asae Ito is an assistant professor under the Koh-hei Nitta’s laboratory; Polymer Physics Laboratory. She has received her B.S. in Chemistry in Tokyo University of Science in 2010, and M.S. in Tokyo Institute of Technology in 2012. She joined in R&D section of SHARP corporation and engaged in the fabrication of OLED devices (2012–2016). Then, she went on to Japan Advanced Institute of Science and Technology (JAIST) and obtained Ph.D. under the supervision of Prof. M. Yamaguchi in 2019 on polymer rheology. Her major interests are the correlation between structure and mechanical properties in glassy as well as semicrystalline polymeric materials.
Tomohiro Miyata

Tohoku University
Tomohiro Miyata received his B.S. in 2013 and Ph.D. in 2018 from the University of Tokyo. After working as a JSPS postdoctoral researcher at Tohoku University, he got a post of Assistant Professor at Tohoku University in 2019. He received several awards, including Young Scientist Award from the Japanese Society of Polymer Science and Dean’s Award FY2017 for the Best Doctoral Student from the School of Engineering, the University of Tokyo. He has worked on ceramics and liquid analysis using TEM techniques since 2013, and engaged in atomic- and nano-scale analysis on polymeric materials since 2018 in Jinnai group at Tohoku University.
Yuta Nishina

Okayama University
Yuta Nishina obtained his Ph.D. degree in Engineering from Okayama University in 2010. Then, he became an independent assistant professor at Research Core for Interdisciplinary Sciences, Okayama University, and was promoted to associate professor in 2014 and research professor in 2018. He has also been appointed as visiting professor at Florida State University (2011), Nanyang Technological University (2011–2012), University of Strasbourg (2017), and Osaka University (2017–2020). His research activities include JST PRESTO (2013–2017), JST CREST (2018—present and 2020—present), and Adjunct Professor at University of New England. He is currently working in multi-discipline research based on organic chemistry, such as nanocarbon production and functionalization, biomedicals, catalysis, and energy-related devices.
Yasunari Tamai

Kyoto University
Yasunari Tamai received his PhD from Kyoto University in 2013 on the excited state dynamics in nanostructured polymer systems. He joined the Optoelectronics group at the University of Cambridge as a postdoctoral fellow under the supervision of Prof Sir Richard Friend, where he focused on ultrafast charge separation at organic semiconductor heterojunctions. Since 2016, he has been an Assistant Professor at Kyoto University. From 2018 to 2022, he was also a JST PRESTO researcher. His current research interests include exciton and charge dynamics in organic semiconductors, particularly conjugated polymers.
Nanjing University
Ye Zhang is currently an associate professor at the College of Engineering and Applied Sciences at the Nanjing University. She received her Ph.D. degree in Macromolecular Chemistry and Physics from the Fudan University in 2018 and then joined the Harvard Medical School as a postdoctoral research fellow. Her research focuses on the development of soft electronics including batteries, sensors, and bioelectronic devices.
Tohoku University
Huie Zhu is an assistant professor in Graduate School of Engineering, Tohoku University. She received her B.Eng. (2008) and M.Eng. degrees (2011) from Zhengzhou University, China. Then, she obtained her Ph.D. degree in Applied Chemistry from Tohoku University in 2014 under the supervision of Prof. Masaya Mitsuishi. After that, she worked shortly as a postdoctoral researcher with Prof. Masaya Mitsuishi in Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University until 2015 and then became an assistant professor in the same institute. From 2020, she started her current position. Her research interests are development of siloxane-based hybrid polymer materials under mild conditions for various applications such as adhesives and thermally stable coatings and nanostructure control of ferroelectric polymers at interfaces for improved performance. She has received several awards from academic organizations and conference committees, such as the Promotion and Nurturing of Female Researchers Contribution Award from the Japan Society of Applied Physics (2019) and the Award for Encouragement of Research in Polymer Science from The Society of Polymer Science, Japan (2020).
Zhejiang Sci-Tech University
Biao Zuo received all his degrees from Zhejiang Sci-Tech University (Hangzhou, China); Chemistry (BSc, 2008), Physical Chemistry of Polymers (MSc, 2011) and Textile Materials (PhD, 2014). After completing the Ph.D. degree, he took a lecturer position at the Department of Chemistry, ZSTU. In 2017 and 2021, he was promoted to associated professor and full professor, respectively. He has worked for a while at Princeton University (2018–2020) and Kyushu University (2016) as a visiting scholar. He is also a principal investigator (PI) at Key Laboratory of Surface & Interface Science of Polymer Materials (SISPM) of Zhejiang Province. His research focuses mainly on molecular dynamics, glass transition, viscoelastic relaxation, rheology and tribology of polymers at surface, interface and under confinement, e.g., ultra-thin films. He has been awarded Chinese Chemical Society (CCS) Young Chemist Award (2021) for the contribution of “Revealing molecular mechanisms of polymer dynamics at surfaces and interfaces”. He is also a recipient of Excellent Young Investigator of NSFC (2021). More

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