Mesoscopic 2D molecular self-assembly on an insulator.

Two-dimensional (2D) nanostructures and nanomaterials offer potential for a wide range of technological applications in electronics, optoelectronics, data storage, sensing and catalysis. On-surface molecular self-assembly-where organic molecules act as building blocks and where surfaces play the role of supporting templates-allows for the bottom-up synthesis of such 2D systems with tuneable atomically precise morphologies and tailored electronic properties. These self-assembly protocols are well established on metal surfaces, but remain limited on electronically gapped substrates (insulators, semiconductors). The latter are useful for preventing electronic coupling (that is, hybridization between molecular assembly and underlying surface) and for avoiding quenching of optical processes, necessary for prospective electronic and optoelectronic applications. In particular, molecular self-assembly on surfaces other than weakly interacting metals can be challenging due to substrate reactivity, defects and inhomogeneities, resulting in intricate energy landscapes that limit the growth kinetically and hampers the synthesis of large-area defect-free 2D systems. Here, we demonstrate the self-assembly of a 2D, atomically thin organic molecular film on a model wide bandgap 2D insulator, single-layer hexagonal boron nitride (hBN) on Cu(111). The molecular film consists of flat, aromatic 9,10-di-cyano-anthracene (DCA) molecules. Our low-temperature scanning tunnelling microscopy and spectroscopy measurements revealed mesoscopic (>100 × 100 nm2), topographically homogeneous crystalline molecular domains resulting from flat molecular adsorption and noncovalent in-plane cyano-ring bonding, with electronically decoupled molecular orbitals (MOs) lying within the hBN electronic gap. These MOs exhibit an energy level spatial modulation (∼300 meV) that follows the moiré work function variation of hBN on Cu(111). These findings pave the way for large-area, atomically precise, highly crystalline 2D organic (and metal-organic) nanomaterials on electronically functional wide bandgap insulators.

COVID-19 pandemic impacts on global inland fisheries.

The COVID-19 pandemic has led to environmental recovery in some ecosystems from a global "anthropause," yet such evidence for natural resources with extraction or production value (e.g., fisheries) is limited. This brief report provides a data-driven global snapshot of expert-perceived impacts of COVID-19 on inland fisheries. We distributed an online survey assessing perceptions of inland fishery pressures in June and July 2020 to basin-level inland fishery experts (i.e., identified by the Food and Agriculture Organization of the United Nations across the global North and South); 437 respondents from 79 countries addressed 93 unique hydrological basins, accounting for 82.1% of global inland fish catch. Based on the responses analyzed against extrinsic fish catch and human development index data, pandemic impacts on inland fisheries 1) add gradation to the largely positive environmental narrative of the global pandemic and 2) identify that basins of higher provisioning value are perceived to experience greater fishery pressures but may have limited compensatory capacity to mitigate COVID-19 impacts along with negative pressures already present.

Selective Activation of Aromatic C-H Bonds Catalyzed by Single Gold Atoms at Room Temperature.

Selective activation and controlled functionalization of C-H bonds in organic molecules is one of the most desirable processes in synthetic chemistry. Despite progress in heterogeneous catalysis using metal surfaces, this goal remains challenging due to the stability of C-H bonds and their ubiquity in precursor molecules, hampering regioselectivity. Here, we examine the interaction between 9,10-dicyanoanthracene (DCA) molecules and Au adatoms on a Ag(111) surface at room temperature (RT). Characterization via low-temperature scanning tunneling microscopy, spectroscopy, and noncontact atomic force microscopy, supported by theoretical calculations, revealed the formation of organometallic DCA-Au-DCA dimers, where C atoms at the ends of the anthracene moieties are bonded covalently to single Au atoms. The formation of this organometallic compound is initiated by a regioselective cleaving of C-H bonds at RT. Hybrid quantum mechanics/molecular mechanics calculations show that this regioselective C-H bond cleaving is enabled by an intermediate metal-organic complex which significantly reduces the dissociation barrier of a specific C-H bond. Harnessing the catalytic activity of single metal atoms, this regioselective on-surface C-H activation reaction at RT offers promising routes for future synthesis of functional organic and organometallic materials.

MitoCellPhe reveals mitochondrial morphologies in single fibroblasts and clustered stem cells.

Mitochondria are dynamic organelles that differ significantly in their morphologies across cell types, reflecting specific cellular needs and stages in development. Despite the wide biological significance in disease and in health, delineating mitochondrial morphologies in complex systems remains challenging. Here, we present the Mitochondrial Cellular Phenotype (MitoCellPhe) tool developed for quantifying mitochondrial morphologies and demonstrate its utility in delineating differences in mitochondrial morphologies in a human fibroblast and human induced pluripotent stem cell (hiPSC) line. MitoCellPhe generates 24 parameters, allowing for a comprehensive analysis of mitochondrial structures and importantly allows for quantification to be performed on mitochondria in images containing single cells or clusters of cells. With this tool, we were able to validate previous findings that show networks of mitochondria in healthy fibroblast cell lines and a more fragmented morphology in hiPSCs. Using images generated from control and diseased fibroblasts and hiPSCs, we also demonstrate the efficacy of the toolset in delineating differences in morphologies between healthy and the diseased state in both stem cell (hiPSC) and differentiated fibroblast cells. Our results demonstrate that MitoCellPhe enables high-throughput, sensitive, detailed, and quantitative mitochondrial morphological assessment and thus enables better biological insights into mitochondrial dynamics in health and disease.

Shifting from volume to economic value in virtual water allocation problems: A proposed new framework and methodology.

PURPOSE: The water footprint provided a full methodology to operationalise the virtual water concept (the volume of water used along a supply chain to produce products and services). A key theme in the water footprint literature is the efficient allocation of water resources at the global scale given the feasibility of trading water intensive commodities from water rich to water poor areas: this is an economic problem of resource allocation between alternative and competing demands, albeit with a novel international component. Moreover, given that price signals indicating relative scarcity are usually either absent or distorted for water, it is also a problem that can be seen through the lens of environmental (or non-market) valuation. However, to date environmental valuation has not been used to inform the efficient use and allocation of water within and between the different locations encompassed by international supply chains. METHODS: Drawing on an agri-food supply chain framework that we propose in this paper, we begin by conceptualising the economic values that accrue to water consumption (blue and green water) and degradation (grey water) at different points along a supply chain. Based on this conceptualisation, we assess the extent to which it is possible to approximate these economic values by relying on existing secondary data on the shadow value of water in different contexts. The use of secondary data in this way is known as benefit (or value) transfer. To achieve this, 706 unit estimates of the economic value of water are collected, standardised and reviewed encompassing off-stream water applications (agriculture, industry and municipal) and in-stream ecosystem services (waste assimilation, wildlife habitat, recreation, hydrological functions and passive uses). From this, a proposed methodology for valuing virtual water is presented and illustrated using the case study of global durum wheat pasta production. RESULTS: The case study shows the total value of the virtual water used to produce one tonne of durum wheat pasta ($212). More importantly, the case study also highlights how variations in economic value between multiple locations where durum wheat is cultivated (Saskatchewan $0.10 m3, Arizona $0.08 m3 and Baja California $0.24 m3) indicate relative water scarcity and thus impact, as well as the potential for a more efficient allocation of virtual water. CONCLUSIONS: The main conclusion from this research is that when geographical disparities in the economic value of water use within a supply chain are accounted for, what was perhaps considered sustainable in volume terms, might not, in fact, represent the optimal allocation. However, future research opportunities highlight the need for additional data collection on the economic value of water in several contexts. This additional data would help the environmental valuation community to undertake a more comprehensive and robust approach to virtual water valuation. This paper is accompanied by the Data in Brief article entitled "Dataset on the in-stream and off-stream economic value of water."

Field-effect sensors - from pH sensing to biosensing: sensitivity enhancement using streptavidin-biotin as a model system.

Field-Effect Transistor sensors (FET-sensors) have been receiving increasing attention for biomolecular sensing over the last two decades due to their potential for ultra-high sensitivity sensing, label-free operation, cost reduction and miniaturisation. Whilst the commercial application of FET-sensors in pH sensing has been realised, their commercial application in biomolecular sensing (termed BioFETs) is hindered by poor understanding of how to optimise device design for highly reproducible operation and high sensitivity. In part, these problems stem from the highly interdisciplinary nature of the problems encountered in this field, in which knowledge of biomolecular-binding kinetics, surface chemistry, electrical double layer physics and electrical engineering is required. In this work, a quantitative analysis and critical review has been performed comparing literature FET-sensor data for pH-sensing with data for sensing of biomolecular streptavidin binding to surface-bound biotin systems. The aim is to provide the first systematic, quantitative comparison of BioFET results for a single biomolecular analyte, specifically streptavidin, which is the most commonly used model protein in biosensing experiments, and often used as an initial proof-of-concept for new biosensor designs. This novel quantitative and comparative analysis of the surface potential behaviour of a range of devices demonstrated a strong contrast between the trends observed in pH-sensing and those in biomolecule-sensing. Potential explanations are discussed in detail and surface-chemistry optimisation is shown to be a vital component in sensitivity-enhancement. Factors which can influence the response, yet which have not always been fully appreciated, are explored and practical suggestions are provided on how to improve experimental design.

Local gate control of Mott metal-insulator transition in a 2D metal-organic framework.

Electron-electron interactions in materials lead to exotic many-body quantum phenomena, including Mott metal-insulator transitions (MITs), magnetism, quantum spin liquids, and superconductivity. These phases depend on electronic band occupation and can be controlled via the chemical potential. Flat bands in two-dimensional (2D) and layered materials with a kagome lattice enhance electronic correlations. Although theoretically predicted, correlated-electron Mott insulating phases in monolayer 2D metal-organic frameworks (MOFs) with a kagome structure have not yet been realised experimentally. Here, we synthesise a 2D kagome MOF on a 2D insulator. Scanning tunnelling microscopy (STM) and spectroscopy reveal a MOF electronic energy gap of ∼200 meV, consistent with dynamical mean-field theory predictions of a Mott insulator. Combining template-induced (via work function variations of the substrate) and STM probe-induced gating, we locally tune the electron population of the MOF kagome bands and induce Mott MITs. These findings enable technologies based on electrostatic control of many-body quantum phases in 2D MOFs.

Imaging the Breakdown and Restoration of Topological Protection in Magnetic Topological Insulator MnBi2Te4.

Quantum anomalous Hall (QAH) insulators transport charge without resistance along topologically protected chiral 1D edge states. Yet, in magnetic topological insulators to date, topological protection is far from robust, with zero-magnetic field QAH effect only realized at temperatures an order of magnitude below the Néel temperature TN, though small magnetic fields can stabilize QAH effect. Understanding why topological protection breaks down is therefore essential to realizing QAH effect at higher temperatures. Here a scanning tunneling microscope is used to directly map the size of exchange gap (Eg,ex) and its spatial fluctuation in the QAH insulator 5-layer MnBi2Te4. Long-range fluctuations of Eg,ex are observed, with values ranging between 0 (gapless) and 70 meV, appearing to be uncorrelated to individual surface point defects. The breakdown of topological protection is directly imaged, showing that the gapless edge state, the hallmark signature of a QAH insulator, hybridizes with extended gapless regions in the bulk. Finally, it is unambiguously demonstrated that the gapless regions originate from magnetic disorder, by demonstrating that a small magnetic field restores Eg,ex in these regions, explaining the recovery of topological protection in magnetic fields. The results indicate that overcoming magnetic disorder is the key to exploiting the unique properties of QAH insulators.

Validating a minipig model of reversible cerebral demyelination using human diagnostic modalities and electron microscopy.

BACKGROUND: Inflammatory demyelinating diseases of the central nervous system, such as multiple sclerosis, are significant sources of morbidity in young adults despite therapeutic advances. Current murine models of remyelination have limited applicability due to the low white matter content of their brains, which restricts the spatial resolution of diagnostic imaging. Large animal models might be more suitable but pose significant technological, ethical and logistical challenges. METHODS: We induced targeted cerebral demyelinating lesions by serially repeated injections of lysophosphatidylcholine in the minipig brain. Lesions were amenable to follow-up using the same clinical imaging modalities (3T magnetic resonance imaging, 11C-PIB positron emission tomography) and standard histopathology protocols as for human diagnostics (myelin, glia and neuronal cell markers), as well as electron microscopy (EM), to compare against biopsy data from two patients. FINDINGS: We demonstrate controlled, clinically unapparent, reversible and multimodally trackable brain white matter demyelination in a large animal model. De-/remyelination dynamics were slower than reported for rodent models and paralleled by a degree of secondary axonal pathology. Regression modelling of ultrastructural parameters (g-ratio, axon thickness) predicted EM features of cerebral de- and remyelination in human data. INTERPRETATION: We validated our minipig model of demyelinating brain diseases by employing human diagnostic tools and comparing it with biopsy data from patients with cerebral demyelination. FUNDING: This work was supported by the DFG under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198) and TRR 274/1 2020, 408885537 (projects B03 and Z01).

Same but different: The latency of a shared expectation signal interacts with stimulus attributes.

Predictive coding theories assert that perceptual inference is a hierarchical process of belief updating, wherein the onset of unexpected sensory data causes so-called prediction error responses that calibrate erroneous inferences. Given the functionally specialised organisation of visual cortex, it is assumed that prediction error propagation interacts with the specific visual attribute violating an expectation. We sought to test this within the temporal domain by applying time-resolved decoding methods to electroencephalography (EEG) data evoked by contextual trajectory violations of either brightness, size, or orientation within a bound stimulus. We found that following ∼170 ms post stimulus onset, responses to both size violations and orientation violations were decodable from physically identical control trials in which no attributes were violated. These two violation types were then directly compared, with attribute-specific signalling being decoded from 265 ms. Temporal generalisation suggested that this dissociation was driven by latency shifts in shared expectation signalling between the two conditions. Using a novel temporal bias method, we then found that this shared signalling occurred earlier for size violations than orientation violations. To our knowledge, we are among the first to decode expectation violations in humans using EEG and have demonstrated a temporal dissociation in attribute-specific expectancy violations.

Angry and fearful compared to happy or neutral faces as conditional stimuli in human fear conditioning: A systematic review and meta-analysis.

Some previous research has shown stronger acquisition and impaired extinction of fear conditioned to angry or fearful compared to happy or neutral face conditional stimuli (CS) - a difference attributed to biological 'preparedness'. A systematic review and meta-analysis of fear conditioning studies comparing face CSs of differing expressions identified thirty studies, eighteen of which were eligible for meta-analysis. Skin conductance responses were larger to angry or fearful faces compared to happy or neutral faces during habituation, acquisition and extinction. Significant differences in differential conditioning between angry, fearful, neutral, and happy face CSs were also found, but differences were more prominent between angry and neutral faces compared to angry/fearful and happy faces. This is likely due to lower arousal elicited by neutral compared to happy faces, which may be more salient as CSs. The findings suggest there are small to moderate differences in differential conditioning when angry or fearful compared to happy or neutral faces are used as CSs. These findings have implications for fear conditioning study design and the preparedness theory.

Direct observation of narrow electronic energy band formation in 2D molecular self-assembly.

Surface-supported molecular overlayers have demonstrated versatility as platforms for fundamental research and a broad range of applications, from atomic-scale quantum phenomena to potential for electronic, optoelectronic and catalytic technologies. Here, we report a structural and electronic characterisation of self-assembled magnesium phthalocyanine (MgPc) mono and bilayers on the Ag(100) surface, via low-temperature scanning tunneling microscopy and spectroscopy, angle-resolved photoelectron spectroscopy (ARPES), density functional theory (DFT) and tight-binding (TB) modeling. These crystalline close-packed molecular overlayers consist of a square lattice with a basis composed of a single, flat-adsorbed MgPc molecule. Remarkably, ARPES measurements at room temperature on the monolayer reveal a momentum-resolved, two-dimensional (2D) electronic energy band, 1.27 eV below the Fermi level, with a width of ∼20 meV. This 2D band results from in-plane hybridization of highest occupied molecular orbitals of adjacent, weakly interacting MgPc's, consistent with our TB model and with DFT-derived nearest-neighbor hopping energies. This work opens the door to quantitative characterisation - as well as control and harnessing - of subtle electronic interactions between molecules in functional organic nanofilms.

Learning Medical Materials From Radiography Images.

Deep learning models have been shown to be effective for material analysis, a subfield of computer vision, on natural images. In medicine, deep learning systems have been shown to more accurately analyze radiography images than algorithmic approaches and even experts. However, one major roadblock to applying deep learning-based material analysis on radiography images is a lack of material annotations accompanying image sets. To solve this, we first introduce an automated procedure to augment annotated radiography images into a set of material samples. Next, using a novel Siamese neural network that compares material sample pairs, called D-CNN, we demonstrate how to learn a perceptual distance metric between material categories. This system replicates the actions of human annotators by discovering attributes that encode traits that distinguish materials in radiography images. Finally, we update and apply MAC-CNN, a material recognition neural network, to demonstrate this system on a dataset of knee X-rays and brain MRIs with tumors. Experiments show that this system has strong predictive power on these radiography images, achieving 92.8% accuracy at predicting the material present in a local region of an image. Our system also draws interesting parallels between human perception of natural materials and materials in radiography images.

A global dataset of inland fisheries expert knowledge.

Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments.

Dataset on the in-stream and off-stream economic value of water.

This dataset contains 706 estimates of the economic value of water; it has been compiled from published sources. Economic values are provided for three off-stream uses (agriculture/irrigation, industry, and municipal) and three in-stream ecosystem services (recreation, waste assimilation, and wildlife habitat). The dataset covers per period and capitalised asset values. All value estimates have been standardised in USD (2014) per acre-foot. The data accompany the research article entitled "Shifting from volume to economic value in virtual water allocation problems: a proposed new framework and methodology" [1]. The dataset can be used to facilitate benefits (or value) transfer.

Photochemical Creation of Covalent Organic 2D Monolayer Objects in Defined Shapes via a Lithographic 2D Polymerization.

In this work we prepare Langmuir-Blodgett monolayers with a trifunctional amphiphilic anthraphane monomer. Upon spreading at the air/water interface, the monomers self-assemble into 1 nm-thin monolayer islands, which are highly fluorescent and can be visualized by the naked eye upon excitation. In situ fluorescence spectroscopy indicates that in the monolayers, all the anthracene units of the monomers are stacked face-to-face forming excimer pairs, whereas at the edges of the monolayers, free anthracenes are present acting as edge groups. Irradiation of the monolayer triggers [4 + 4]-cycloadditions among the excimer pairs, effectively resulting in a two-dimensional (2D) polymerization. The polymerization reaction also completely quenches the fluorescence, allowing to draw patterns on the monomer monolayers. More interestingly, after transferring the monomer monolayer on a solid substrate, by employing masks or the laser of a confocal scanning microscope, it is possible to arbitrarily select the parts of the monolayer that one wants to polymerize. The unpolymerized regions can then be washed away from the substrate, leaving 2D macromolecular monolayer objects of the desired shape. This photolithographic process employs 2D polymerizations and affords 1 nm-thin coatings.

Acid-base dissociation mechanisms and energetics at the silica-water interface: An activationless process.

HYPOTHESIS: Silanol groups at the silica-water interface determine not only the surface charge, but also have an important role in the binding of ions and biomolecules. As the pH is increased above pH 2, the silica surface develops a net negative charge primarily due to deprotonation of the silanol group. An improved understanding of the energetics and mechanisms of this fundamentally important process would further understanding of the relevant dynamics. SIMULATIONS: Density Functional Theory ab initio molecular dynamics and geometry optimisations were used to investigate the mechanisms of surface neutralisation and charging in the presence of OH(-) and H3O(+) respectively. This charging mechanism has received little attention in the literature. FINDINGS: The protonation or deprotonation of isolated silanols in the presence of H3O(+) or OH(-), respectively, was shown to be a highly rapid, exothermic reaction with no significant activation energy. This process occurred via a concerted motion of the protons through 'water wires'. Geometry optimisations of large water clusters at the silica surface demonstrated proton transfer to the surface occurring via the rarely discussed 'proton holes' mechanism. This indicates that surface protonation is possible even when the hydronium ion is distant (at least 4 water molecules separation) from the surface.