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The expected energy transition to hydrogen gas as a greener energy vector has revived the interest in magnetic refrigeration at the cryogenic range, specifically between 20 and 80 K, with the vision to develop a new generation of hydrogen gas liquefiers. From the materials science point of view, the search for magnetocaloric materials containing mainly non-critical elements with a significant response in that temperature range, together with good cyclability and stability, is a challenging task. Given the increasing interest of the research community on this topic, we aim to establish a comprehensive catalog of the magnetocaloric compounds characterized so far, to be used as a starting point for further research. For this purpose, a systematic outlook of the state of the art is presented here, with the analysis and classification of more than 400 cryogenic magnetocaloric materials, divided into five large families according to their physicochemical properties. Moreover, we provide detailed information about their magnetocaloric properties, magnetic behavior, and transition characteristics together with criticality, which will facilitate the future search for optimal compounds.

Weconducteda theoreticalstudyofelectrontransportthroughjunctionsoftheblue-copperazurinfromPseudomonasaeruginosa.Wefoundthatsingle-sitehoppingcanleadtoeitherhigherorlowercurrentvaluescomparedtofullycoherenttransport.Thisdependsonthestructuraldetailsofthejunctionsaswellasthealignmentoftheproteinorbitals.Moreover,weshowhowtheasymmetryoftheIVcurvescanbeaffectedbythepositionofthetipinthejunctionandthat,underspecificconditions,sucha hoppingmechanismisconsistentwithafairlylowtemperaturedependenceofthecurrent.Finally,weshowthatincreasingthenumberofhoppingsitesleadstohigherhoppingcurrents.Ourfindings,fromfullyquantumcalculations,providedeepinsighttohelpguidetheinterpretationofexperimentalIVcurvesonhighlycomplexsystems.

In ITER, the metallic first mirrors (FMs) will undergo erosion due to their proximity to the fusion plasma and deposition of materials originated from the first walls (mainly beryllium). In-situ plasma cleaning is a promising technique to conserve the FMs optical properties by means of ion sputtering. In this work, the evolution of the optical properties of single-crystal (Sc) and nanocrystalline (Nc) molybdenum (Mo) and rhodium (Rh) mirrors were investigated up to 100 cycles of consecutive contamination and cleaning. Aluminum oxide (Al₂O₃) was used as contaminant to replace the toxic beryllium. The plasma cleaning was carried out using a capacitively coupled argon (Ar) plasma excited by a 60 MHz radio-frequency generator resulting in the formation of a self-bias applied on the mirrors of -280 V. The plasma potential being around 30 V, the Ar ion energy was about 310 eV. The optical properties of the mirrors were assessed using ex-situ reflectivity measurements. Moreover, the surface topography was characterized by means of scanning electron microscopy (SEM), focused ion beam (FIB) and roughness measurements using atomic force microscopy (AFM). ScMo and ScRh mirrors formerly exposed to 80 successful cleaning cycles using aluminum/tungsten (Al/W) deposits and air storage exhibit drastic changes in their optical properties after being subject to cleaning cycles using Al₂O₃ as contaminant. Additionally, freshly polished ScRh were exposed to identical cleaning cycles. All Sc mirrors exhibited pits induced by the polishing procedure using diamond paste in addition of mounds/wavy patterns. The carbon incorporated during the polishing process was demonstrated to be responsible for the pitting of the surface. The Nc mirrors preserved their initial reflectivities after up to 100 cycles. The surface topography was systematically characterized and an average erosion rate for NcRh mirrors of about 59 nm per cycle has been estimated from FIB cross-sections. The optical propertie

In most cases, substitution studies that aim to optimize magnetic properties are performed at the magnetic atomic site. However, in the case of MnB, magnetic substitutions at the Mn site significantly decrease the once promising magnetocaloric and magnetic properties. This study employs computationally directed search to optimize the magnetocaloric properties of MnB where partial substitutions of boron atoms (Mn50B50−xSix and Mn50B50−xGex where x = 3.125, 6.25, and 12.5) reveal new compounds with a greater magnetocaloric effect than pure MnB at the same Curie temperature. These new compounds were obtained by arc melting the pure elements and further characterized. The computationally driven screening process is based on density functional theory calculations that do not require large databases of known compounds. This work demonstrates that using simple computational screening procedures to search for new magnetocaloric materials with improved properties can be done quickly, cost-effectively, and while maintaining reliability.

The metal-organic framework MOF-808 contains Zr6O8nodes with a high density of vacancy sites, which can incorporate carboxylate-containing functional groups to tune chemical reactivity. Although the postsynthetic methods to modify the chemistry of the Zr6O8nodes in MOFs are well known, tackling these alterations from a structural perspective is still a challenge. We have combined infrared spectroscopy experiments and first-principles calculations to identify the presence of node vacancies accessible for chemical modifications within the MOF-808. We demonstrate the potential of our approach to assess the decoration of MOF-808 nodes with different catechol-benzoate ligands. Furthermore, we have applied advanced synchrotron characterization tools, such as pair distribution function analyses and X-ray absorption spectroscopy, to resolve the atomic structure of single metal sites incorporated into the catechol groups postsynthetically. Finally, we demonstrate the catalytic activity of these MOF-808 materials decorated with single copper sites for 1,3-dipolar cycloadditions.

Azurin proteins are the workhorse of protein electronics. This is a branch of biomolecular electronics, a recent research field which investigates electronics based on biomolecules such as proteins, peptides, amino acids, bacterial nanowires or DNA. In general, the possibility of including biosystems in solid-state junctions has opened the way to the development of novel electrical devices, and proteins have attracted enormous attention thanks to their many interesting properties. In the particular case of metal-azurin-metal junctions, experimental measurements have revealed extremely efficient electron transport over large distances, showing conductance values which are higher than certain conjugated molecules of similar lengths. Moreover, the electrical current has often been found to be temperature-independent, which has been used as an evidence of coherent transport or quantum tunneling. Interesting effects have been observed, moreover, upon insertion of single amino-acid mutations. In spite of a huge amount of work, the exact mechanism for the charge flow through these systems is still under debate. In this review, we will revise the recent advances made in the electron-transport measurements of azurin-based junctions as well as the corresponding theoretical modelling. We will discuss the interpretation of the currently-available experimental results as well as the open issues which still remain to be clarified.

The metal-organic framework MOF-808 contains Zr₆O₈ nodes with a high density of vacancy sites, which can incorporate carboxylate-containing functional groups to tune chemical reactivity. Although the postsynthetic methods to modify the chemistry of the Zr₆O₈ nodes in MOFs are well known, tackling these alterations from a structural perspective is still a challenge. We have combined infrared spectroscopy experiments and first-principles calculations to identify the presence of node vacancies accessible for chemical modifications within the MOF-808. We demonstrate the potential of our approach to assess the decoration of MOF-808 nodes with different catechol–benzoate ligands. Furthermore, we have applied advanced synchrotron characterization tools, such as pair distribution function analyses and X-ray absorption spectroscopy, to resolve the atomic structure of single metal sites incorporated into the catechol groups postsynthetically. Finally, we demonstrate the catalytic activity of these MOF-808 materials decorated with single copper sites for 1,3-dipolar cycloadditions.

This paper introduces Quasar Science Resources–Autonomous University of Madrid atomic force microscopy image data set (QUAM-AFM), the largest data set of simulated atomic force microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule, 24 3D image stacks, each consisting of constant-height images simulated for 10 tip–sample distances with a different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256 × 256 pixels. The 3D stacks are especially appropriate to tackle the goal of the chemical identification within AFM experiments by using deep learning techniques. The data provided for each molecule include, besides a set of AFM images, ball-and-stick depictions, IUPAC names, chemical formulas, atomic coordinates, and map of atom heights. In order to simplify the use of the collection as a source of information, we have developed a graphical user interface that allows the search for structures by CID number, IUPAC name, or chemical formula.

In spite of the unprecedented resolution provided by non-contact atomic force microscopy (AFM) with CO-functionalized and advances in the interpretation of the observed contrast, the unambiguous identification of molecular systems solely based on AFM images, without any prior information, remains an open problem. This work presents a first step towards the automatic classification of AFM experimental images by a deep learning model trained essentially with a theoretically generated dataset. We analyze the limitations of two standard models for pattern recognition when applied to AFM image classification and develop a model with the optimal depth to provide accurate results and to retain the ability to generalize. We show that a variational autoencoder (VAE) provides a very efficient way to incorporate, from very few experimental images, characteristic features into the training set that assure a high accuracy in the classification of both theoretical and experimental images.

We studied the coherent electron transport through metal–protein–metal junctions based on a blue copper azurin, in which the copper ion was replaced by three different metal ions (Co, Ni and Zn). Our results show that neither the protein structure nor the transmission at the Fermi level change significantly upon metal replacement. The discrepancy with previous experimental observations suggests that the transport mechanism taking place in these types of junctions is probably not fully coherent.