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The incorporation of recycled materials in concrete as a partial replacement of cement is becoming an alternative strategy for decreasing energy-intensive and CO2 emissions imputable to the cement manufacture, while investigating new potential uses of such multifunctional materials for environmental sustainability opportunities. Therefore, low-cost and worldwide availability of by-products materials is being assessed for sensible heat thermal energy storage applications based on cementitious materials. A greater concern is especially required focusing on the thermal stability of cement paste under high temperature cycled conditions. Moreover, compatibility between cement type and supplementary cementitious materials is determinant for the proper performance reliability. In this study, benchmark cement types were selected, i.e., ordinary Portland and calcium aluminate. Six supplementary cementitious materials were added to both types of cement in a content of 10 % and 25 %. Thermo-mechanical properties were studied before and after 10 thermal cycles from 290 to 650 ◦C. Results after thermal cycling showed that calcium aluminate cement paste mixtures maintained their integrity. However, most ordinary Portland cement paste mixtures were deteriorated: only mixtures with 25 % cement replacement with chamotte, flay ash, and ground granulated blast furnace slag remained without cracks. Calcium aluminate cement paste mixtures obtained the highest compressive strength, for partial replacement of cement with 10 % of chamotte, ground granulated blast furnace slag, and iron silicate. The incorporation of supplementary cementitious materials did not increase the thermal conductivity. This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31 - MCIU/AEI/FEDER, UE) and by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors at University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme and by the Italian project ‘SOS-CITTA’ supported by Fondazione Cassa di Risparmio di Perugia under grant agreement No 2018.0499.026. Laura Boquera acknowledgments are due to the PhD school in Energy and Sustainable Development from University of Perugia. Laura Boquera would like to acknowledge the financial support provided by UNIPG – CIRIAF InpathTES project. The authors also thank the companies that provided the material to make possible this experimental research: Arciresa, Abrasivos Mendiola EDERSA—Masaveu Industria, General Admixtures S.p.A, Mapei, Ciments Molins industrial, and Promsa for the material supplied in this research. Financial support of the UNIPG-CIRIAF team has been achieved from the Italian Ministry of University and Research (MUR) in the framework of the Project FISR 2019: “Eco Earth” (code 00245) and it is gratefully acknowledged.

For the design of ground-source heat-pump systems, the local subsoil is an invariant factor. To improve the evaluation of the local heat exchange capability, significant efforts recently have been devoted to identifying the ground thermal conductivity vertical profile. In recent years, an innovative method using hybrid optic fiber cables inserted into the ground has been developed. The technique relies on copper wires that thermally stimulate the ground. Optical fibers measure the temperature variation over time all along the cable at a high spatial and temporal resolution. In this work, the hybrid cable was grouted into a 125-m well located in the Po Plain in Northern Italy. The provided core defined the geological environment as a continuous succession of unconsolidated alluvial deposits of very limited thickness, grouped in 15 different granulometric units. Three enhanced thermal response test (ETRT) data sets were acquired in different seasons; for 5 days of heating followed by 5 days of recovery, the soil temperature was recorded continuously along the well, with a spatial resolution of 1 m. A new approach using a multiple linear regression is proposed to analyze the data sets to distinguish the thermal conductivity of each individual granulometric unit. The obtained thermal conductivity values were compared and discussed considering the standard thermal response test outputs and the thermal conductivity data obtained from direct measurements performed on the cores. The analytical method's reliability stands due to the high repeatability of the obtained results, despite the increased complexity of the treated geological setting.

The most extreme environments are the most vulnerable to transformation under a rapidly changing climate. These ecosystems harbor some of the most specialized species, which will likely suffer the highest extinction rates. We document the steepest temperature increase (2010-2021) on record at altitudes of above 4,000 m, triggering a decline of the relictual and highly adapted moss Takakia lepidozioides. Its de-novo-sequenced genome with 27,467 protein-coding genes includes distinct adaptations to abiotic stresses and comprises the largest number of fast-evolving genes under positive selection. The uplift of the study site in the last 65 million years has resulted in life-threatening UV-B radiation and drastically reduced temperatures, and we detected several of the molecular adaptations of Takakia to these environmental changes. Surprisingly, specific morphological features likely occurred earlier than 165 mya in much warmer environments. Following nearly 400 million years of evolution and resilience, this species is now facing extinction.

AbstractThe recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.

The Emiliania huxleyi stoichiometry database contains data for the growth rate, cellular elemental content (particulate inorganic carbon - PIC, organic carbon - C, nitrogen - N and phosphorous - P) and C:N:P stoichiometry (PIC:C, C:N, N:P, C:P) compiled through a meta-analysis of literature reporting the results of laboratory experiments (cultures) on the coccolithophore species Emiliania huxleyi, an important calcifying marine phytoplankton. The database also reports selected additional parameters including cell size and/or volume, and chlorophyll a as well as additional meta-data associated with the original data source including strain details and culture experimental conditions. A description of the parameters contained in the database can be found in the file "The Emiliania huxleyi stoichiometry database data description". Please cite this dataset as: Sheward et al. (2021) The Emiliania huxleyi stoichiometry database. doi:10.5281/zenodo.4601185 The author can be contacted at sheward@em.uni-frankfurt.de to confirm versioning and to request details of any accompanying publications that arise. The compilation of this database was supported by Mount Allison University, Canada and Dalhousie University, Canada and funding through the Canada Research Chairs program (ZVF) and the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES grants 549937 to ZVF and 549935 to AJI).

The production of high-value products from lignocellulosic biomass is carried out through the selective scission of crosslinked CC/CO bonds. Nowadays, several techniques are applied to optimize biomass conversion into desired products with high yields. Photocatalytic technology has been proven to be a valuable tool for valorizing biomass at mild conditions. The photoproduced reactive oxygen species (ROSs) can initiate the scission of crosslinked bonds and form radical intermediates. However, the low mass transfer of the photocatalytic process could limit the production of a high yield of products. The incorporation of ultrasonic cavitation in the photocatalytic system provides an exceptional condition to boost the fragmentation and transformation of biomass into the desired products within a lesser reaction time. This review critically discusses the main factors governing the application of photocatalysis for biomass valorization and tricks to boost the selectivity for enhancing the yield of desired products. Synergistic effects obtained through the combination of sonolysis and photocatalysis were discussed in depth. Under ultrasonic vibration, hot spots could be produced on the surface of the photocatalysts, improving the mass transfer through the jet phenomenon. In addition, shock waves can assist the dissolution and mixing of biomass particles.

Abstract This paper presents a review of the application of model predictive control strategies to active thermal energy storage systems. To date, model predictive control has been used to manage such energy systems as heating, ventilation and air conditioning equipment or power generation plants. In all cases, the aim of the strategy has been to anticipate both production and consumption decisions to optimize the system performance, reducing the final energy cost. This ability of the strategy to forecast weather conditions and predict demand requirements in advance exceeds the performance of conventional control methods and made the strategy a very effective option to be coupled with active thermal energy storage systems. In this regard, this review paper presents the progress and results of the combination of these two technologies. The key contributions consist of a summary of the technical parameters employed, such as the prediction horizon length, the computational architecture approaches, the thermal energy storage material used and the influence of renewables in this kind of system. Additionally, the review summarises the latest enhancements to overcome computational issues and an analysis of the objective functions employed in each study, which were mainly focused to minimize the energy cost, the peak power and CO2 emissions. A discussion about the strengths and weaknesses of this technology is provided, highlighting the difficulty of the strategy to operate with complicated physical models as the key limitation to overcome. Finally, some future guidelines to enhance the application of this strategy to control different sort of systems are detailed.