Sustainable bioethanol production combining biorefinery principles using combined raw materials from wheat undersown with clover-grass

To obtain the best possible net energy balance of the bioethanol production the biomass raw materials used need to be produced with limited use of non-renewable fossil fuels. Intercropping strategies are known to maximize growth and productivity by including more than one species in the crop stand, very often with legumes as one of the components. In the present study clover-grass is undersown in a traditional wheat crop. Thereby, it is possible to increase input of symbiotic fixation of atmospheric nitrogen into the cropping systems and reduce the need for fertilizer applications. Furthermore, when using such wheat and clover-grass mixtures as raw material, addition of urea and other fermentation nutrients produced from fossil fuels can be reduced in the whole ethanol manufacturing chain. Using second generation ethanol technology mixtures of relative proportions of wheat straw and clover-grass (15:85, 50:50, and 85:15) were pretreated by wet oxidation. The results showed that supplementing wheat straw with clover-grass had a positive effect on the ethanol yield in simultaneous saccharification and fermentation experiments, and the effect was more pronounced in inhibitory substrates. The highest ethanol yield (80% of theoretical) was obtained in the experiment with high fraction (85%) of clover-grass. In order to improve the sugar recovery of clover-grass, it should be separated into a green juice (containing free sugars, fructan, amino acids, vitamins and soluble minerals) for direct fermentation and a fibre pulp for pretreatment together with wheat straw. Based on the obtained results a decentralized biorefinery concept for production of biofuel is suggested emphasizing sustainability, localness, and recycling principle

Nanoporous Solid-State Sensitization of Triplet Fusion Upconversion

Photochemical upconversion of green to blue light is demonstrated in thin films of nanostructured alumina stained with a metalloporphyrin sensitizer. The pores of the structure are filled with emitter molecules in a concentrated solution, allowing efficient upconversion within the solid-state scaffold. The photon generation quantum yield is measured to be 9.4%, which is nearly 40% of what is possible with a diphenylanthracene emitter. These results show that high-efficiency upconversion is possible with solid-state sensitization within a nanostructured thin-film architecture

Exciton Dissociation, Charge Transfer, and Exciton Trapping at the MoS<inf>2</inf>/Organic Semiconductor Interface

Hybrid inorganic-organic semiconducting devices consisting of monolayer transition metal dichalcogenides (TMDs) represent a new frontier in advanced optoelectronics due to their high radiative efficiencies and capacity to form flexible p-n junctions with inherent device tunability. However, understanding how excitons and charges behave at the interface between TMDs and organic systems, a key requirement to advance the field, remains underexplored. Herein, a heterostructure consisting of a highly conjugated organic system, 9-(2-naphthyl)-10-[4-(1-naphthyl)phenyl]anthracene (ANNP), and monolayer molybdenum disulfide (MoS2) on quartz is elucidated via transient absorption and photoluminescence spectroscopies. Upon direct excitation of MoS2at 532 nm, hole transfer to ANNP of ∼5 ps and a charge separation time constant of ∼2.4 ns are observed. When the sample is excited at 400 nm (where both ANNP and MoS2absorb), a self-trapped exciton within ANNP is formed. The emission of the self-trapped exciton is long-lived compared to the exciton lifetime of ANNP, decaying within 20 ns. The trapping of the ANNP exciton is caused by structural deformities of the ANNP crystal lattice when grown on MoS2, which are removed by annealing the film. These observations highlight how exciton dissociation and charge transfer dominate at the interface of ANNP and MoS2whereas the exciton dynamics within ANNP are prone to the formation of trap states brought about by crystal defects within the film. These insights will aid in future developments of TMD-containing optoelectronics

Singlet fission and tandem solar cells reduce thermal degradation and enhance lifespan

The economic value of a photovoltaic installation depends upon both its lifespan and power conversion efficiency. Progress toward the latter includes mechanisms to circumvent the Shockley-Queisser limit, such as tandem designs and multiple exciton generation (MEG). Here we explain how both silicon tandem and MEG-enhanced silicon cell architectures result in lower cell operating temperatures, increasing the device lifetime compared to standard c-Si cells. Also demonstrated are further advantages from MEG enhanced silicon cells: (i) the device architecture can completely circumvent the need for current-matching; and (ii) upon degradation, tetracene, a candidate singlet fission (a form of MEG) material, is transparent to the solar spectrum. The combination of (i) and (ii) mean that the primary silicon device will continue to operate with reasonable efficiency even if the singlet fission layer degrades. The lifespan advantages of singlet fission enhanced silicon cells, from a module perspective, are compared favorably alongside the highly regarded perovskite/silicon tandem and conventional c-Si modules

A Factorization Law for Entanglement Decay

We present a simple and general factorization law for quantum systems shared by two parties, which describes the time evolution of entanglement upon passage of either component through an arbitrary noisy channel. The robustness of entanglement-based quantum information processing protocols is thus easily and fully characterized by a single quantity.Comment: 4 pages, 5 figure

Shear strength of reinforced concrete dapped-end beams using mechanism analysis.

yesA mechanism analysis based on the upper-bound theorem of concrete plasticity is developed to predict the critical failure plane and corresponding shear capacity of reinforced concrete dapped-end beams. Failure modes observed in physical tests of reinforced concrete dapped-end beams are idealised as an assemblage of two moving blocks separated by a failure surface of displacement discontinuity. The developed mechanism analysis rationally represents the effect of different parameters on failure modes; as a result, the predicted shear capacity is in good agreement with test results. On the other hand, empirical equations specified in the Precast/Prestressed Concrete Institute design method and strutand-tie model based on ACI 318-05 highly underestimate test results. The shear capacity of dapped-end beams predicted by the mechanism analysis and strut-and-tie model decreases with the increase of shear span-to-full beam depth ratio when failure occurs along diagonal cracks originating at the bottom corner of the full-depth beam, although the shear span-to-full beam depth ratio is ignored in the Precast/Prestressed Concrete Institute design method

Heralded quantum entanglement between two crystals

Quantum networks require the crucial ability to entangle quantum nodes. A prominent example is the quantum repeater which allows overcoming the distance barrier of direct transmission of single photons, provided remote quantum memories can be entangled in a heralded fashion. Here we report the observation of heralded entanglement between two ensembles of rare-earth-ions doped into separate crystals. A heralded single photon is sent through a 50/50 beamsplitter, creating a single-photon entangled state delocalized between two spatial modes. The quantum state of each mode is subsequently mapped onto a crystal, leading to an entangled state consisting of a single collective excitation delocalized between two crystals. This entanglement is revealed by mapping it back to optical modes and by estimating the concurrence of the retrieved light state. Our results highlight the potential of rare-earth-ions doped crystals for entangled quantum nodes and bring quantum networks based on solid-state resources one step closer.Comment: 10 pages, 5 figure

Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm

BACKGROUND: Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers. METHODS: We performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients' background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1-2 whilst a poor outcome was defined as CPC 3-5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets. RESULTS: AUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% (p < 0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions. CONCLUSIONS: In this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance