Tailoring Graphene with Metals on Top

We study the effects of metallic doping on the electronic properties of graphene using density functional theory in the local density approximation in the presence of a local charging energy (LDA+U). The electronic properties are sensitive to whether graphene is doped with alkali or transition metals. We estimate the the charge transfer from a single layer of Potassium on top of graphene in terms of the local charging energy of the graphene sheet. The coating of graphene with a non-magnetic layer of Palladium, on the other hand, can lead to a magnetic instability in coated graphene due to the hybridization between the transition-metal and the carbon orbitals.Comment: 5 pages, 4 figure

Mapping Materials and Molecules

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the “big data” revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities. It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses. The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Urine Proteomics Analysis of Patients with Neuronal Ceroid Lipofuscinoses

The Neuronal Ceroid Lipofuscinoses (NCL) are a group of 13 rare neurodegenerative disorders characterised by accumulation of cellular storage bodies. There are few therapeutic options and existing tests do not monitor disease progression and treatment response. However, urine biomarkers could address this need. Proteomic analysis of CLN2 patient urine revealed activation of immune response pathways and pathways associated with the unfolded protein response. Analysis of CLN5 and CLN6 sheep model urine showed subtle changes. To confirm and investigate the relevance of candidate biomarkers a targeted LC-MS/MS proteomic assay was created. We applied this assay to additional CLN2 samples as well as other NCL patients, (CLN1, CLN3, CLN5, CLN6 and CLN7) and demonstrated that Hexosaminidase-A, Aspartate Aminotransferase-1 and LAMP1, are increased in NCL samples and betaine-homocysteine S-methyltransferase-1 was specifically increased in CLN2 patients. These proteins could be used to monitor effectiveness of future therapies aimed at treating systemic NCL disease

Mapping Materials and Molecules.

The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them.This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields

Active control of strong plasmon-exciton coupling in biomimetic pigment-polymer antenna complexes grown by surface-initiated polymerisation from gold nanostructures

Plexcitonic antenna complexes, inspired by photosynthetic light-harvesting complexes, are formed by attachment of chlorophylls (Chl) to poly(cysteine methacrylate) (PCysMA) scaffolds grown by atom-transfer radical polymerisation from gold nanostructure arrays. In these pigment-polymer antenna complexes, localised surface plasmon resonances on gold nanostructures are strongly coupled to Chl excitons, yielding hybrid light-matter states (plexcitons) that are manifested in splitting of the plasmon band. Modelling of the extinction spectra of these systems using a simple coupled oscillator model indicates that their coupling energies are up to twice as large as those measured for LHCs from plants and bacteria. Coupling energies are correlated with the exciton density in the grafted polymer layer, consistent with the collective nature of strong plasmon-exciton coupling. Steric hinderance in fully-dense PCysMA brushes limits binding of bulky chlorophylls, but the chlorophyll concentration can be increased to ~2M, exceeding that in biological light-harvesting complexes, by controlling the grafting density and polymerisation time. Moreover, synthetic plexcitonic antenna complexes display pH- and temperature-responsiveness, facilitating active control of plasmon-exciton coupling. Because of the wide range of compatible polymer chemistries and the mild reaction conditions, plexcitonic antenna complexes may offer a versatile route to programmable molecular photonic materials

Suppression of electron-electron repulsion and superconductivity in Ultra Small Carbon Nanotubes

Recently, ultra-small-diameter Single Wall Nano Tubes with diameter of $\sim 0.4 nm$ have been produced and many unusual properties were observed, such as superconductivity, leading to a transition temperature $T_c\sim 15^oK$, much larger than that observed in the bundles of larger diameter tubes. By a comparison between two different approaches, we discuss the issue whether a superconducting behavior in these carbon nanotubes can arise by a purely electronic mechanism. The first approach is based on the Luttinger Model while the second one, which emphasizes the role of the lattice and short range interaction, is developed starting from the Hubbard Hamiltonian. By using the latter model we predict a transition temperature of the same order of magnitude as the measured one.Comment: 7 pages, 3 figures, to appear in J. Phys.-Cond. Ma

Safety and efficacy of GABAA α5 antagonist S44819 in patients with ischaemic stroke: a multicentre, double-blind, randomised, placebo-controlled trial

Background: S44819, a selective GABAA α5 receptor antagonist, reduces tonic post-ischaemic inhibition of the peri-infarct cortex. S44819 improved stroke recovery in rodents and increased cortical excitability in a transcranial magnetic stimulation study in healthy volunteers. The Randomized Efficacy and Safety Trial of Oral GABAA α5 antagonist S44819 after Recent ischemic Event (RESTORE BRAIN) aimed to evaluate the safety and efficacy of S44819 for enhancing clinical recovery of patients with ischaemic stroke. Methods: RESTORE BRAIN was an international, randomised, double-blind, parallel-group, placebo-controlled, multicentre phase 2 trial that evaluated the safety and efficacy of oral S44189 in patients with recent ischaemic stroke. The study was done in specialised stroke units in 92 actively recruiting centres in 14 countries: ten were European countries (Belgium, Czech Republic, France, Germany, Hungary, Italy, Netherlands, Poland, Spain, and the UK) and four were non-European countries (Australia, Brazil, Canada, and South Korea). Patients aged 18–85 years with acute ischaemic stroke involving cerebral cortex (National Institute of Health Stroke Scale [NIHSS] score 7–20) without previous disability were eligible for inclusion. Participants were randomly assigned to receive 150 mg S44819 twice a day, 300 mg S44819 twice a day, or placebo twice a day by a balanced, non-adaptive randomisation method with a 1:1:1 ratio. Treatment randomisation and allocation were centralised via the interactive web response system using computer-generated random sequences with a block size of 3. Blinding of treatment was achieved by identical appearance and taste of all sachets. Patients, investigators and individuals involved in the analysis of the trial were masked to group assignment. The primary endpoint was the modified Rankin Scale (mRS) score 90 days from onset of treatment, evaluated by shift analysis (predefined main analysis) or by dichotomised analyses using 0–1 versus 2–6 and 0–2 versus 3–6 cutoffs (predefined secondary analysis). Secondary endpoints were the effects of S44819 on the NIHSS and Montreal Cognitive Assessment (MoCA) scores, time needed to complete parts A and B of the Trail Making Test, and the Barthel index. Efficacy analyses were done on all patients who received at least one dose of treatment and had at least one mRS score taken after day 5 (specifically, on or after day 30). Safety was compared across treatment groups for all patients who received at least one dose of treatment. The study was registered at ClinicalTrials.gov, NCT02877615. Findings: Between Dec 19, 2016, and Nov 16, 2018, 585 patients were enrolled in the study. Of these, 197 (34%) were randomly assigned to receive 150 mg S44819 twice a day, 195 (33%) to receive 300 mg S44819 twice a day, and 193 (33%) to receive placebo twice a day. 189 (96%) of 197 patients in the 150 mg S44819 group, 188 (96%) of 195 patients in the 300 mg S44819 group, and 191 (99%) patients in the placebo group received at least one dose of treatment and had at least one mRS score taken after day 5, and were included in efficacy analyses. 195 (99%) of 197 patients in the 150 mg S44819 group, 194 (99%) of 195 patients in the 300 mg S44819 group, and 193 (100%) patients in the placebo group received at least one dose of treatment, and were included in safety analyses. The primary endpoint of mRS at day 90 did not differ between each of the two S44819 groups and the placebo group (OR 0·91 [95% CI 0·64–1·31]; p=0·80 for 150 mg S44819 compared with placebo and OR 1·17 [95% CI 0·81–1·67]; p=0·80 for 300 mg S44819 compared with placebo). Likewise, dichotomised mRS scores at day 90 (mRS 0–2 vs 3–6 or mRS 0–1 vs 2–6) did not differ between groups. Secondary endpoints did not reveal any significant group differences. The median NIHSS score at day 90 did not differ between groups (4 [IQR 2–8] in 150 mg S44819 group, 4 [2–7] in 300 mg S44819 group, and 4 [2–6] in placebo group), nor did the number of patients at day 90 with an NIHSS score of up to 5 (95 [61%] of 156 in 150 mg S44819 group, 106 [66%] of 161 in 300 mg S44819 group, and 104 [66%] of 157 in placebo group) versus more than 5 (61 [39%] in 150 mg S44819 group, 55 [34%] in 300 mg S44819 group, and 53 [34%] in placebo group). Likewise, the median MoCA score (22·0 [IQR 17·0–26·0] in 150 mg S44819 group, 23·0 [19·0–26·5] in 300 mg S44819 group, and 22·0 [17·0–26·0] in placebo group), time needed to complete parts A (50 s [IQR 42–68] in 150 mg S44819 group, 49 s [36–63] in 300 mg S44819 group, and 50 s [38–68] in placebo group) and B (107 s [81–144] in 150 mg S44819 group, 121 s [76–159] in 300 mg S44819 group, and 130 s [86–175] in placebo group) of the Trail Making Test, and the Barthel index (90 [IQR 60–100] in 150 mg S44819 group, 90 [70–100] in 300 mg S44819 group, and 90 [70–100] in placebo group) were similar in all groups. Number and type of adverse events were similar between the three groups. There were no drug-related adverse events and no drug-related deaths. Interpretation: There was no evidence that S44819 improved clinical outcome in patients after ischaemic stroke, and thus S44819 cannot be recommended for stroke therapy. The concept of tonic inhibition after stroke should be re-evaluated in humans. Funding: Servier