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As biodiversity loss accelerates globally, understanding environmental influence over biodiversity–ecosystem functioning (BEF) relationships becomes crucial for ecosystem management. Theory suggests that resource supply affects the shape of BEF relationships, but this awaits detailed investigation in marine ecosystems. Here, we use deep-sea chemosynthetic methane seeps and surrounding sediments as natural laboratories in which to contrast relationships between BEF proxies along with a gradient of trophic resource availability (higher resource methane seep, to lower resource photosynthetically fuelled deep-sea habitats). We determined sediment fauna taxonomic and functional trait biodiversity, and quantified bioturbation potential (BPc), calcification degree, standing stock and density as ecosystem functioning proxies. Relationships were strongly unimodal in chemosynthetic seep habitats, but were undetectable in transitional ‘chemotone’ habitats and photosynthetically dependent deep-sea habitats. In seep habitats, ecosystem functioning proxies peaked below maximum biodiversity, perhaps suggesting that a small number of specialized species are important in shaping this relationship. This suggests that absolute biodiversity is not a good metric of ecosystem ‘value’ at methane seeps, and that these deep-sea environments may require special management to maintain ecosystem functioning under human disturbance. We promote further investigation of BEF relationships in non-traditional resource environments and emphasize that deep-sea conservation should consider ‘functioning hotspots' alongside biodiversity hotspots. 

Boron nitride nanotubes (BNNTs) have attracted attention for their predicted extraordinary properties; yet, challenges in synthesis and processing have stifled progress on macroscopic materials. Recent advances have led to the production of highly pure BNNTs. Here we report that neat BNNTs dissolve in chlorosulfonic acid (CSA) and form birefringent liquid crystal domains at concentrations above 170 ppmw. These tactoidal domains merge into millimeter-sized regions upon light sonication in capillaries. Cryogenic electron microscopy directly shows nematic alignment of BNNTs in solution. BNNT liquid crystals can be processed into aligned films and extruded into neat BNNT fibers. This study of nematic liquid crystals of BNNTs demonstrates their ability to form macroscopic materials to be used in high-performance applications.

 

This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of the machine learning model. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges. 

Carbon-based catalysts have been attracting extensive attention as viable candidates to replace platinum towards oxygen reduction reaction, a critical process at fuel cell cathode. An advancement has been the development of carbon-supported iron carbide (Fe3C/C) catalysts derived from the pyrolysis of metal organic frameworks (MOFs). In the present study, a series of Fe3C/C nanocomposites were prepared by controlled pyrolysis of FeMOF-NH2 with a systemic variation of the iron and zinc compositions in the MOF precursor. Scanning/transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopic measurements were carried out to examine the morphologies, structures, and elemental composition of the nanocomposites, while nitrogen adsorption/desorption and Raman studies were used to characterize the surface area and porosity. It was found that an optimal zinc to iron feeding ratio was required to produce a catalyst with a preferential pore size distribution. Electrochemical measurements revealed that the sample derived from 20% zinc replacement in the FeMOF-NH2 precursor exhibited the best electrocatalytic activity in alkaline media among the series, with the most positive onset potential and highest limiting current, which coincided with the highest surface area and porosity. The results suggest that deliberate structural engineering is critical in manipulating and optimizing the electrocatalytic activity of metal,nitrogen-codoped carbon nanocomposites. 

Plasma wakefield accelerators offer accelerating and focusing electric fields three to four orders of magnitude larger than state‐of‐the‐art radiofrequency cavity‐based accelerators. Plasma photocathodes can release ultracold electron populations within such plasma waves and thus open a path toward tunable production of well‐defined, compact electron beams with normalized emittance and brightness many orders of magnitude better than state‐of‐the‐art. Such beams will have far‐reaching impact for applications such as light sources, but also open up new vistas on high energy and high field physics. This paper reviews the innovation of plasma photocathodes, and reports on the experimental progress, challenges, and future prospects of the approach. Details of the proof‐of‐concept demonstration of a plasma photocathode in 90° geometry at SLAC FACET within the E‐210: Trojan Horse program are described. Using this experience, alongside theoretical and simulation‐supported advances, an outlook is given on future realizations of plasma photocathodes such as the upcoming E‐310: Trojan Horse‐II program at FACET‐II with prospects toward excellent witness beam parameter quality, tunability, and stability. Future installations of plasma photocathodes also at compact, hybrid plasma wakefield accelerators, will then boost capacities and open up novel capabilities for experiments at the forefront of interaction of high brightness electron and photon beams.

 

Invented in 2010, NanoCluster Beacons (NCBs) (1) are an emerging class of turn-on probes that show unprecedented capabilities in single-nucleotide polymorphism (2) and DNA methylation (3) detection. As the activation colors of NCBs can be tuned by a near-by, guanine-rich activator strand, NCBs are versatile, multicolor probes suitable for multiplexed detection at low cost. Whereas a variety of NCB designs have been explored and reported, further diversification and optimization of NCBs require a full scan of the ligand composition space. However, the current methods rely on microarray and multi-well plate selection, which only screen tens to hundreds of activator sequences (4, 5). Here we take advantage of the next-generation-sequencing (NGS) platform for high-throughput, large-scale selection of activator strands. We first generated a ~104 activator sequence library on the Illumina MiSeq chip. Hybridizing this activator sequence library with a common nucleation sequence (which carried a nonfluorescent silver cluster) resulted in hundreds of MiSeq chip images with millions of bright spots (i.e. light-up polonies) of various intensities and colors. With a method termed Chip-Hybridized Associated Mapping Platform (CHAMP) (6), we were able to map these bright spots to the original DNA sequencing map, thus recovering the activator sequence behind each bright spot. After assigning an “activation score” to each “light-up polony”, we used a computational algorithm to select the best activator strands and validate these strands using the traditional in-solution preparation and fluorometer measurement method. By exploring a vast ligand composition space and observing the corresponding activation behaviors of silver clusters, we aim to elucidate the design rules of NCBs. 

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single‐nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed polar opposite twins (POTs), it is difficult to discover new POT‐NCBs using the conventional low‐throughput characterization approaches. Here, a high‐throughput selection method is reported that takes advantage of repurposed next‐generation‐sequencing chips to screen the activation fluorescence of ≈40 000 activator sequences. It is found that the nucleobases at positions 7–12 of the 18‐nucleotide‐long activator are critical to creating bright NCBs and positions 4–6 and 2–4 are hotspots to generate yellow–orange and red POTs, respectively. Based on these findings, a “zipper‐bag” model is proposed that can explain how these hotspots facilitate the formation of distinct silver cluster chromophores and alter their chemical yields. Combining high‐throughput screening with machine‐learning algorithms, a pipeline is established to design bright and multicolor NCBs in silico.

 

We present images obtained with LABOCA on the APEX telescope of a sample of 22 galaxies selected via their red Herschel SPIRE 250-, 350- and $500\textrm{-}\mu\textrm{m}$ colors. We aim to see if these luminous, rare and distant galaxies are signposting dense regions in the early Universe. Our $870\textrm{-}\mu\textrm{m}$ survey covers an area of $\approx0.8\,\textrm{deg}^2$ down to an average r.m.s. of $3.9\,\textrm{mJy beam}^{-1}$, with our five deepest maps going $\approx2\times$ deeper still. We catalog 86 DSFGs around our 'signposts', detected above a significance of $3.5\sigma$. This implies a $100\pm30\%$ over-density of $S_{870}>8.5\,\textrm{mJy}$ DSFGs, excluding our signposts, when comparing our number counts to those in 'blank fields'. Thus, we are $99.93\%$ confident that our signposts are pinpointing over-dense regions in the Universe, and $\approx95\%$ confident that these regions are over-dense by a factor of at least $\ge1.5\times$. Using template SEDs and SPIRE/LABOCA photometry we derive a median photometric redshift of $z=3.2\pm0.2$ for our signposts, with an interquartile range of $z=2.8\textrm{-}3.6$. We constrain the DSFGs likely responsible for this over-density to within $|\Delta z|\le0.65$ of their respective signposts. These 'associated' DSFGs are radially distributed within $1.6\pm0.5\,\textrm{Mpc}$ of their signposts, have median SFRs of $\approx(1.0\pm0.2)\times10^3\,M_{\odot}\,\textrm{yr}^{-1}$ (for a Salpeter stellar IMF) and median gas reservoirs of $\sim1.7\times10^{11}\,M_{\odot}$. These candidate proto-clusters have average total SFRs of at least $\approx (2.3\pm0.5)\times10^3\,M_{\odot}\,\textrm{yr}^{-1}$ and space densities of $\sim9\times10^{-7}\,\textrm{Mpc}^{-3}$, consistent with the idea that their constituents may evolve to become massive ETGs in the centers of the rich galaxy clusters we see today.