BUSCADOR RECOLECTA

S0.1.1. Simulations with Molecular Dynamics: In the corresponding simulation box, the NVT ensemble was used to calculate a fixed number of N atoms. To maintain this fixed number, the isobaric ensemble was also considered. During the simulations, a time step of 0.25 ×10−15 s was implemented. The ReaxFF methodology incorporates a reactive force field (FF) to describe the chemical interactions in each system. In the present work, the FF developed by Kim et al [1] was applied, in which the interactions of the lignocellulosic components, namely carbon, hydrogen, and oxygen are considered. This FF was implemented due to its suitability for analogous lignocellulosic systems [2–4]. The MD calculations were performed with the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) [5] computational code and the Reax-module. The interactions among carbon atoms and the possible reactivity with the rest of the components were studied with a distance-corrected Morse potential [6]. This potential incorporates the Van der Waals (vdW) dispersion description and the computation of the bond orders (BO) to evaluate chemical interactions through the pyrolysis process. In the computational simulation models, the total energy of the system was calculated by Eq. 1: Esystem = Ebond + Eover + Eunder + Elp + Eval + Etor + EvdW + ECoulomb (1). In Eq. 1, Ebond corresponds to the bonding energy, Eover refers to the overcoordination stability, Eunder includes the energy to regulate the energy of undercoordinated atoms, and the Elp term is the lone pair energy. The Eval is termed the valence energy. The torsional energy among the atomic components inside the system is Etor, while EvdW includes the description of the vdW interactions. Additionally, the Coulomb energy is given by the ECoulomb term in Eq. 1. Those atomic interactions with non-bonding nature are fully described with a seventh-order function [7]. The morphology of the porous structure was theoretically modeled by computing the pore size distribution (PSD) of the final carbon materials resulting from the simulated pyrolysis. The calculations were performed with the Zeo++ computational code [8], which is intended to map the geometry of the pores via the Voronoi decomposition. The method incorporates a probe molecule with a radius size of 1.79˚A to evaluate the volume accessible to this probe. This radius corresponds to the approximate size of a nitrogen molecule, which is used in the physisorption experiments. The cellulose and hemicellulose precursors were studied as polymeric models in accordance to Martínez-Casillas et al [4]. Such molecular components were incorporated in the present work due to their successful incorporation into analogous biomass systems. The massive model systems were formed by tracking the specific rate contents for each lignocellulosic component with the Packmol code [9]. It produces random distributions in a closed simulation box with a side size of 140˚A, which is shown in Fig. 2 of the main text. The model systems of the agave fibers BA and BAC are based upon the experimental data, in which the following contents were found: 17% lignin, 38% cellulose, and 24% hemicellulose for BA, while for the latter, 13%, 44% and 10% for lignin, cellulose and hemicellulose respectively for BAC. The remaining components are byproducts, mainly ashes, which are not considered in the present calculations. Such compositions were based on the NREL characterization method implemented in the experiments [10]. To fulfill the total content of the sample, the molecular models were further normalized. The molecular weights of each model system were introduced to build the massive molecular system depicted in Fig. 2 (a), depicted in the main text. The lignin Adler’s model was set as the reference system, with a molecular weight of 2407.10 g/mol. The simulation box comprised 40 macromolecules and an accumulated molecular weight of 96,284 g/mol. It was then fixed as 24% of the complete molecular weight of the lignocellulosic components, after normalization. The polymer models for cellulose and hemicellulose corresponded to normalized contents of 45% and 31% [11]. Consequently, molecular weights of 180,532.5 g/mol and 124,366.83 g/mol for these two components, respectively, were considered for the BA model. In terms of polymer units, 69 hemicellulose and 109 cellulose units were introduced. The BAC model was also normalized by using the same criteria. The pyrolysis processes were evaluated with two different heating rates; namely, at 0.027 K/fs, and also at a slower rate of 0.005 K/fs for selected cases. The top temperature at which. the pyrolysis simulations were performed corresponds to the temperatures of 500 °C and 700 °C, which were reached with the experimental procedure [3, 4]. These temperatures remained constant upon annealing during 100,000 steps with a step size of 0.25 fs. When this stage of the simulation is finalized, a quenching process was applied to meet the initial room temperature of 300 K. A subsequent series of 50,000 MD steps at this fixed temperature evolved the system to reach a thermal equilibrium. It is worth mentioning that a methodology to compute the pressure values in annealing conditions, as that developed byWang et al [12] was impossible to incorporate in the simulations, since the vdW equation of state was applied. S0.1.2. Radial distribution function computation The X-ray diffraction data obtained between 2θ of 1° to 120° was used to compute the radial distribution function (RDF) that is aimed to elucidate the crystal structure of solids or amorphous materials [13]. The RDF is given in accordance with the eq. 2: G(r) = 4πrρ0[g(r) − 1] (2) in which r stands for the radial distance, and ρ0 is the atomic number density in average. Finally, the term g(r) corresponds to the macroscopic atomic pair density [13]. The physical significance of eq. 2 corresponds to the distribution of probability to find atomic pairs at a certain distance r. Moreover, G(r) is further assessed with a sine Fourier transform. It refers to the reciprocal space of the structure factor S(Q), containing the scattering structure distribution that comes from the experimental XRD data. Using the structure factor, Eq. 2 is re-written as: G(r) = 2/π Z ∞ 0 Q[S(Q) − 1] sin (Qr)dQ (3) Upon combination of eq. 2 and 3, the RDF can be obtained as given by g(r). Such values were found with the aid of the PDFGETX2 computational code [14]. The G(r) function was assessed via a Fourier transform using the structure factor S(Q) as input. A value of 16.0˚A−1 was used as the Qmax parameter [1] to apply the Fourier transform. As a final statistical treatment, the PDFGUI code was implemented to adjust the experimental RDF. The PDFGUI is a software with a user graphical interface based upon the PDFFIT2 computational code [15]. The software requires a starting guess to refine the structural data such as atomic positions, lattice constants, correlated atomic motion, and anisotropic atomic displacement. S0.1.3. Envelope Density assessment The envelope density methodology [16] was used to evaluate the densities of the final carbon materials. This study was intended to compare the densities given in the experiment with those computed with the theoretical approach. The ratio of the carbon material to the total volume is defined as the envelope density, and it was used in the experimental analysis. It is worth mentioning that a volume of 0.2mL was considered in the evaluation, which is given by Corning Inc. PCR R®tubes.-- Under a Creative Commons license BY-NC-ND 4.0, A full description of computational details related to molecular dynamics, radial distribution function computation, and Envelope Density assessment, along with experimental results concerning SEM images of group I and group II carbon materials.-- S0.1. Computational Details: S0.1.1. Simulations with Molecular Dynamics. S0.1.2. Radial distribution function computation. S0.1.3. Envelope Density assessment. S0.2. Complementary experimental results, The financial support for this work was provided by DGAPA-PAPIIT-UNAM through the projects IG100217, IG100923, IN106122 and IA102522. [...]. D.R. Lobato-Peralta and A. Ayala-Cortés received Ph.D. fellowships from the Consejo Nacional de Humanidades, Ciencias Tecnologías (CONAHCYT). J.M. thanksthe financial support of Fondo Sectorial de Investigación para la Educación-CONAHCYT under project No. A1-S-13294, and Fronteras de la Ciencia-CONAHCYT under Project No. 21077. J.M. also acknowledges the computational infrastructure provided by Laboratorio Nacional de Conversión Almacenamiento de Energía (CONAHCYT) under project No. 270810, as well as the UNAM Supercomputing Department for the computing resources under Project No. LANCAD-UNAM-DGTIC- 370 and LANCAD-UNAM- DGTIC-310. The computing time in an AWS EC2 instance is also acknowledged, which was granted under Project No. 8 of the DGTIC-AWS initiative., Peer reviewed

The dataset provides Rhizostoma pulmo newly released ephyrae statoliths measurements., The data set is provided as an Excel file within a compressed folder that also includes a single README file (in text format) containing a detailed description of the data structure., [METHODOLOGY] Description of methods used for collection/generation of data: Six combinations of temperature and PCO2 (18, 24 and 30ºC with a PCO2 of 500 and 1000 ppm each) were applied during 32 days to different groups of polyps of Rhizostoma pulmo. From the released ephyrae during this period, three specimens from each treatment were randomly taken. From each ephyra, three of its eight statocysts were randomly selected and all the statoliths inside were counted and measured via inverted optical microscopy.-- Methods for processing the data: Data were analyzed using RStudio software. To obtain the variable "volume", its values were obtained using the formula for the volume of a regular hexagonal prism., This data set includes data collected from ICMAN-CSIC (Spain), consisting of measurements of the size and number of the statoliths present in newly released Rhizostoma pulmo ephirae under conditions of oceanic warming and acidification (IPCC SSP5-8.5 scenario). From each ephyra, three of its eight statocysts were randomly selected and all the statoliths inside were counted and measured., This research was funded by the Spanish Ministerio de Economía y Competitividad (grant number CTM2016-75487-R “Respuesta de medusas mediterráneas al efecto interactivo de motores climáticos de impacto: supervivencia en un Mediterráneo más cálido y ácido”, proyect MED2CA), CSIC (grant number 202330E57 “Estudio de los efectos de motores de impacto del cambio climático sobre las dinámicas poblacionales de medusas mediterráneas”) and the Project Agreement “Detección de medusas en el mar Balear y su relación con las condiciones ambientales: hacia el desarrollo de un sistema de predicción pre-operacional” among Govern des Illes Balears, SOCIB and CSIC (Disposición 15052 del BOE núm. 310 de 2020). It was also financially sustainned by a Ph.D. fellowship (FPU22/03487) from the Spanish Ministerio de Ciencia, Innovación y Universidades., Peer reviewed

STM topographs and dI/dV spectra of molecules within molecular layers, dI/dV spectra of a wide voltage range, additional DFT results, an explanation of the Green function approach used to estimate TK, and a description of the synthesis of CoTPPBr2I2., Peer reviewed

Figure S1. Phylogenetic tree of voltage-gated K+ channel protein sequences from tomato and Arabidopsis and expression of SlK5.1 and SlK5.2 in tomato plants. Figure S2. Generation of slskor KO mutants with the CRISPR-Cas system. Figure S3. Phylogenetic tree of S-type anion channel protein sequences from tomato and Arabidopsis and gene expression in tomato roots. Figure S4. slskor plants exhibited increased Na+ and Cl- content in shoots under salt stress. Figure S5. Protein sequence alignment of AtSKOR and SlSKOR. Figure S6. Phylogenetic tree of CIPK protein sequences from tomato and Arabidopsis. Figure S7. Effect of SlCIPK9+SlCBL1 and SlCIPK2+SlCBL1 on SlSKOR activity. Table S1. Primers used in this study Table S2. Fold-change values in qRT-PCR experiments. Methods S1. Selection of homolog proteins and calculation of SlSKOR functional parameters. Dataset 1. Data supporting the findings of this study, Peer reviewed

MMC S1. Details of experimental techniques and supplementary hydrogen adsorption measurements., Peer reviewed

Schemes of glass formation of supercooled liquid and Moynihan equal-area method for determining Tf, raw data of isothermal physical aging tests, precision verification of TTS, procedure for TTS of reported data from literatures, and the corresponding master curve., Peer reviewed

See the supplementary material for the convergence tests of total energy and MAE (Figs. S1–S6), the dependency between the value of the MAE and Ueff (Figs. S7–S9), and the 3d-orbital-decomposed PDOS obtained by HSE06 (Fig. S10)., Peer reviewed

Further information on the (Te, Tl)–MDEF method, the multicoverage EANN PES, and the photoinduced CO desorption dynamics., Peer reviewed

Section 1. Methods Section 2. Extended data of anisotropic transport on device S1 Section 3. Comparison between samples Section 4. Angular dependence of the second-order non-linear response Section 5. Raw data of magnetoresistance measurements Section 6. Non-linear conductivity on device S2 Section 7. Scaling law of the non-linear conductivity, Peer reviewed

17 Pág., Forest biomass is an essential resource in relation to the green transition and its assessment is key for the sustainable management of forest resources. Here, we present a forest biomass dataset for Europe based on the best available inventory and satellite data, with a higher level of harmonisation and spatial resolution than other existing data. This database provides statistics and maps of the forest area, biomass stock and their share available for wood supply in the year 2020, and statistics on gross and net volume increment in 2010-2020, for 38 European countries. The statistics of most countries are available at a sub-national scale and are derived from National Forest Inventory data, harmonised using common reference definitions and estimation methodology, and updated to a common year using a modelling approach. For those counties without harmonised statistics, data were derived from the State of Europe's Forest 2020 Report at the national scale. The maps are coherent with the statistics and depict the spatial distribution of the forest variables at 100 m resolution., We thank the ENFIN and the NFI organisations and national correspondents of 26 European countries who participated in the data harmonisation study supported by the JRC Specific Contracts n. 13, 17, 18, 19, 20 and 21 for the production of data harmonised for definition and estimation method regarding forest biomass, forest area and biomass not available for wood supply, and forest increment. The study was partly funded by the Framework contract for the provision of forest data and services in support to the European Forest Data Centre, Contract numbers 388432 (2012–2017) & 934340 (2017–2021). The views expressed are purely those of the writers and may in no circumstance be regarded as stating an official position of the European Commission., Peer reviewed