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Disturbance, vegetation productivity, and recovery are crucial for aboveground biomass carbon (AGC) dynamics. Here, we use multiple satellite-based datasets to analyze the drivers of AGC dynamics in Central Africa. During 2010–2019, deforestation induced a gross AGC loss of 102.2 ± 17.1 Tg C year−1, which was counterbalanced by an AGC increase of 116.9 ± 41.1 Tg C year−1, leading to a net gain of 14.6 ± 3.8 Tg C year−1. Compared to anthropogenic and soil factors, changes in climate-related factors (e.g., radiation) are more important for the non-deforestation AGC changes. A large AGC increase was found in the northern savannas. In moist forests, strong biomass recovery and growth largely compensated the carbon loss from deforestation and degradation. Considering the increasing resource demand due to rapid population growth, reconciling natural conservation and economic development in Central Africa remains challenging and depends on climate changes and country-specific social-economic conditions.

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr−1throughout the global tropics from 2010 to 2020. This arises from a gross loss of −1.79 PgC yr−1offset by a marked gain of 2.01 ± 0.06 PgC yr−1. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Quantum sensing with spin defects in diamond, such as the nitrogen vacancy (NV) center, enables the detection of various chemical species on the nanoscale. Molecules or ions with unpaired electronic spins are typically probed by their influence on the NV center’s spin relaxation. Whereas it is well-known that paramagnetic ions reduce the NV center’s relaxation time (T1), here we report on the opposite effect for diamagnetic ions. We demonstrate that millimolar concentrations of aqueous diamagnetic electrolyte solutions increase the T1time of near-surface NV center ensembles compared to pure water. To elucidate the underlying mechanism of this surprising effect, single and double quantum NV experiments are performed, which indicate a reduction of magnetic and electric noise in the presence of diamagnetic electrolytes. In combination with ab initiosimulations, we propose that a change in the interfacial band bending due to the formation of an electric double layer leads to a stabilization of fluctuating charges at the interface of an oxidized diamond. This work not only helps to understand noise sources in quantum systems but could also broaden the application space of quantum sensors toward electrolyte sensing in cell biology, neuroscience, and electrochemistry.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1–14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15–18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0–1,000 mm year−1rainfall zone by coupling field data19, machine learning20–22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha−1and 63 kg C tree−1in the arid zone to 3.7 Mg C ha−1and 98 kg tree−1in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24–29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Changes in terrestrial carbon storage under environmental and land-use changes remain a critical source of uncertainty in regional and global carbon budgets. We generated global maps of annual live vegetation biomass using L-band microwave vegetation optical depth. Globally, biomass carbon stocks increased from 2010 to 2019 at a rate of 0.50 ± 0.20 PgC yr−1with a year-to-year variability, closely mirroring the observations of the global atmospheric CO2growth rate. The main contributors to the global carbon sink are boreal and temperate forests, while wet tropical forests are small carbon sources, from deforestation and agriculture-related disturbances. We found that the tropical deforested and degraded old-growth forests (>140 yr) are nearly carbon neutral whereas temperate and boreal young (< 50 yr) and middle-aged (50–140 yr) forests are the largest sinks. By contrast, dynamic global vegetation models show that all old-growth forests are large sinks and largely ignore the impacts of deforestation and degradation on tropical biomass. Our findings highlight the importance of forest demography when predicting dynamics of future carbon sink under changing climate.

ABSTRACTUnderstory vegetation contributes considerably to biodiversity and total aboveground biomass of forest ecosystems. Whereas field inventories and LiDAR data are generally used to estimate understory vegetation density, methods for large-scale and spatially continuous estimation of understory vegetation density are still lacking. For an evergreen coniferous forest area in southern China, we developed and tested an effective and practical remote sensing-driven approach for mapping understory vegetation, based on phenological differences between over and understory vegetation. Specifically, we used plant area volume density (PAVD) calculations based on GEDI data to train a support vector regression model and subsequently estimated the understory vegetation density from Sentinel-2 derived metrics. We produced maps of PAVD for the growing and non-growing season respectively, both performing well compared against independent GEDI samples (R2 = 0.89 and 0.93, p< 0.01). Understory vegetation density was derived from the differences in PAVD between the growing and non-growing season. The understory vegetation density map was validated against field samples from 86 plots showing an overall R2of 0.52 (p< 0.01), rRMSE = 21%. Our study developed a tangible approach to map spatially continuous understory vegetation density with the combination of GEDI LiDAR data and Sentinel-2 imagery, showing the potential to improve the estimation of terrestrial carbon storage and better understand forest ecosystem processes across larger areas.

•Comprehensive tree features comparison: GGW vs. Corporate vs. non-planted areas.•Rethinking reforestation's objectives: Quality-based reforestation over density.•Insights into great green wall initiative: Successes, challenges addressed.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Siberian forests are generally thought to have acted as an important carbon sink over recent decades, but exposure to severe droughts and fire disturbances may have impacted their carbon dynamics. Limited available forest inventories mean the carbon balance remains uncertain. Here we analyse annual live and dead above-ground carbon changes derived from low-frequency passive microwave observations from 2010 to 2019. We find that during this period, the carbon balance of Siberian forests was close to neutral, with the forests acting as a small carbon sink of +0.02+0.01+0.03PgC yr−1. Carbon storage in dead wood increased, but this was largely offset by a decrease in live biomass. Substantial losses of live above-ground carbon are attributed to fire and drought, such as the widespread fires in northern Siberia in 2012 and extreme drought in eastern Siberia in 2015. These live above-ground carbon losses contrast with ‘greening’ trends seen in leaf area index over the same period, a decoupling explained by faster post-disturbance recovery of leaf area than live above-ground carbon. Our study highlights the vulnerability of large forest carbon stores in Siberia to climate-induced disturbances, challenging the persistence of the carbon sink in this region of the globe.

Trees outside of forests are numerous and can be important carbon sinks, while also providing ecosystem services and benefits to livelihoods. New monitoring tools highlight the crucial contribution they can make to strategies for both mitigation and adaptation.

Spatial–temporal dynamics of aboveground biomass (AGB) and forest area affect the carbon cycle, climate and biodiversity in the Brazilian Amazon. Here we investigate interannual changes in AGB and forest area by analysing satellite-based annual AGB and forest area datasets. We found that the gross forest area loss was larger in 2019 than in 2015, possibly due to recent loosening of forest protection policies. However, the net AGB loss was three times smaller in 2019 than in 2015. During 2010–2019, the Brazilian Amazon had a cumulative gross loss of 4.45 Pg C against a gross gain of 3.78 Pg C, resulting in a net AGB loss of 0.67 Pg C. Forest degradation (73%) contributed three times more to the gross AGB loss than deforestation (27%), given that the areal extent of degradation exceeds that of deforestation. This indicates that forest degradation has become the largest process driving carbon loss and should become a higher policy priority.

A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees) grow in isolation, without canopy closure. These non-forest trees have a crucial role in biodiversity, and provide ecosystem services such as carbon storage, food resources and shelter for humans and animals1,2. However, most public interest relating to trees is devoted to forests, and trees outside of forests are not well-documented3. Here we map the crown size of each tree more than 3 m2in size over a land area that spans 1.3 million km2in the West African Sahara, Sahel and sub-humid zone, using submetre-resolution satellite imagery and deep learning4. We detected over 1.8 billion individual trees (13.4 trees per hectare), with a median crown size of 12 m2, along a rainfall gradient from 0 to 1,000 mm per year. The canopy cover increases from 0.1% (0.7 trees per hectare) in hyper-arid areas, through 1.6% (9.9 trees per hectare) in arid and 5.6% (30.1 trees per hectare) in semi-arid zones, to 13.3% (47 trees per hectare) in sub-humid areas. Although the overall canopy cover is low, the relatively high density of isolated trees challenges prevailing narratives about dryland desertification5–7, and even the desert shows a surprisingly high tree density. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty.

Accurate information on tree species diversity is critical for forest biodiversity, conservation and management, but mapping forest diversity over large and mixed forest areas using satellite remote sensing data remains a challenge because of scale- and ecosystem-dependent relationships between spectral heterogeneity and tree species diversity. In this study, three different diversity indices (Simpson (λ), Shannon (H’), and Pielou (J’)), were tested to characterize forest tree species diversity using individual monthly and multi-temporal Sentinel-1 and -2 images during 2021. The performance of three different machine learning models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN) were tested. A collection of 1,020 plot measurements (comprising 47 tree species and 28,122 trees), randomly collected in a mixed broadleaf-conifer forest area in northeast China, was used to train (n = 816) and validate (n = 204) the models. The models dependent on multi-temporal Sentinel-1/2 imagery were found to outperform the models based on individual monthly data, in predicting forest tree species diversity, with average accuracies of 78% for H’, 77% for λ and 77% for J’. The use of DNN performed marginally better than the XGB and RF models, with accuracies of 81% for H’, 80% for λ and 79% for J’, respectively. Finally, a boosted regression model, involving environmental variable predictors and the DNN-based estimated tree species diversity, showed that on average 63 ± 4% of the spatial variations of tree species diversity was explained by environmental variables, including annual temperature (29.30%), followed by soil fertility (27.03%), snow cover (13.63%) and a digital elevation model (12.33%). Our results highlight that an empirical approach based on machine learning and multi-temporal Sentinel-1/2 data can accurately predict forest tree species diversity and we further show the important roles of air temperature and soil fertility in governing the spatial variability of tree species diversity in a mixed broadleaf-conifer forest setting.

Changes in terrestrial tropical carbon stocks have an important role in the global carbon budget. However, current observational tools do not allow accurate and large-scale monitoring of the spatial distribution and dynamics of carbon stocks1. Here, we used low-frequency L-band passive microwave observations to compute a direct and spatially explicit quantification of annual aboveground carbon (AGC) fluxes and show that the tropical net AGC budget was approximately in balance during 2010 to 2017, the net budget being composed of gross losses of −2.86 PgC yr−1offset by gross gains of −2.97 PgC yr−1between continents. Large interannual and spatial fluctuations of tropical AGC were quantified during the wet 2011 La Niña year and throughout the extreme dry and warm 2015–2016 El Niño episode. These interannual fluctuations, controlled predominantly by semiarid biomes, were shown to be closely related to independent global atmospheric CO2growth-rate anomalies (Pearson’s r= 0.86), highlighting the pivotal role of tropical AGC in the global carbon budget. Tropical carbon stocks are essential for proper accounting of global carbon budgets, but difficult to monitor on a large scale. L-band microwave observations used here enable direct and spatially explicit accounting of annual carbon fluxes from different types of land surface.

Projection of future woody cover is essential to understand potential changes in structure and functioning of terrestrial ecosystems. Previous studies mapped woody cover during historical periods observed by satellites, however, it remains unclear how woody cover is expected to change in response to future climate change. Here, we develop data‐driven models to predict woody cover in Africa using multiple environmental predictors and show that woody cover can be accurately modeled using Random Forest. Empirically‐based simulations forced by precipitation from CMIP6 project an overall increase in woody cover at the continental scale by 2100. However, increases are mainly occurring in regions with annual precipitation less than ∼1,600 mm y−1, whereas woody cover is predicted to decrease in areas of higher rainfall. Our results suggest that climate change may alter the functioning of dryland ecosystems by continued woody encroachment and cause a loss of carbon stocks in humid areas. Future climate changes pose a severe threat on the structure and functioning of terrestrial ecosystems and thereby threaten human well‐beings. Forests play a critical role in mitigating climate changes by absorbing atmospheric CO2, but undergo large losses during last three decades due to a range of disturbance, such as wildfires and extreme climate events. In particular, forest and non‐forest trees are largely impacted by changing precipitation regimes in Africa, but the knowledge on this is limited to current periods observed by satellites. Here, we establish a data‐driven model by linking woody cover to environmental variables, the developed model was further forced by future precipitation from the Sixth Coupled Model Intercomparison Project. We predict an increase in woody cover in drylands by the year 2100, while a decrease in humid areas across African continent. This study thus provides a feasible data‐driven approach for the prediction of woody cover without the temporal limitation of satellites and allows for a better understanding of changes in ecosystem structure, composition and functioning in the context of future climate change. Woody cover can be accurately predicted with environmental variablesMean annual precipitation explains the most spatial variability in woody cover in AfricaPredicted precipitation changes will increase woody cover in drylands but decrease woody cover in humid areas by 2100 Woody cover can be accurately predicted with environmental variables Mean annual precipitation explains the most spatial variability in woody cover in Africa Predicted precipitation changes will increase woody cover in drylands but decrease woody cover in humid areas by 2100

Woody vegetation in farmland acts as a carbon sink and provides ecosystem services for local people, but no macroscale assessments of the impact of management and climate on woody cover exist for drylands. Here we make use of very high spatial resolution satellite imagery to derive wall-to-wall woody cover patterns in tropical West African drylands. Our study reveals that mean woody cover in farmlands along all semi-arid and sub-humid rainfall zones is 16%, on average only 6% lower than in savannahs. In semi-arid Sahel, farmland management promotes woody cover around villages (11%), while neighbouring savannahs had on average less woody cover. However, farmlands in sub-humid zones have a greatly reduced woody cover (21%) as compared with savannahs (33%). In the region as a whole, rainfall, terrain and soil are the most important (80%) determinants of woody cover, while management factors play a smaller (20%) role. We conclude that agricultural expansion causes a considerable reduction of trees in woodlands, but observations in Sahel indicate that villagers safeguard trees on nearby farmlands which contradicts simplistic ideas of a high negative correlation between population density and woody cover. Farmland management promotes tree cover around villages in the semi-arid Sahel of West Africa, according to analyses of satellite imagery. This implies that a higher population density does not always lead to reduced tree cover.

Charge trapping due to defects in semiconductor quantum dots (QDs) is expected to challenge the applicability of QDs in future technologies. The efficient elimination of defects from QDs demands an understanding of their origin and of their impact on (photo)electronic properties. Here, we identify the presence of two charge states of a defect in CdSe QDs using electron paramagnetic resonance (EPR), combined with electronic tuning of QDs via chemically induced Ag doping. From light-induced EPR, we show that these defects have a central role in Fermi level pinning in ensembles of intrinsic QDs. By analyzing the dependence of the EPR signal of the defects on the concentration of Ag dopants, we further demonstrate that the defects act as effective electron traps in the QDs. Our study also provides support to the proposed behavior of Ag dopants in CdSe QDs, according to which Ag atoms are n-type dopants at concentrations below 2 Ag atoms per QD and become p-type dopants for higher Ag concentrations. From temperature-dependent EPR, we estimate a lower limit for the ionization energy of the studied defects. Based on the characteristics of the EPR spectrum, we propose that these defects are associated with Se vacancies with the paramagnetic state being the positively charged state of the defect.

Here we present the realization of efficient and sustainable silicon‐based thermoelectric materials from nanoparticles. We employ a gas phase synthesis for the nanoparticles which is capable of producing doped silicon (Si) nanoparticles, doped alloy nanoparticles of silicon and germanium (Ge), SixGe1–x, and doped composites of Si nanoparticles with embedded metal silicide precipitation phases. Hence, the so‐called “nanoparticle in alloy” approach, theoretically proposed in the literature, forms a guideline for the material development. For bulk samples, a current‐activated pressure‐assisted densification process of the nanoparticles was optimized in order to obtain the desired microstructure. For thin films, a laser annealing process was developed. Thermoelectric transport properties were characterized on nanocrystalline bulk samples and laser‐sintered‐thin films. Devices were produced from nanocrystalline bulk silicon in the form of p–n junction thermoelectric generators, and their electrical output data were measured up to hot side temperatures of 750 °C. In order to get a deeper insight into thermoelectric properties and structure forming processes, a 3D‐Onsager network model was developed. This model was extended further to study the p–n junction thermoelectric generator and understand the fundamental working principle of this novel device architecture. Gas phase synthesis of composite nanoparticles; nanocrystalline bulk with optimized composite microstructure; laser‐annealed thin film. The authors fabricated thermoelectric nanomaterials from doped silicon and silicon and germanium alloy nanoparticles, as well as composites of Si nanoparticles with embedded metal silicide nanoparticles. Processing was performed applying a current‐activated pressure‐assisted densification process for bulk samples and a laser annealing process for thin film samples. Devices were produced in the form of pn junction thermoelectric generators. A 3D‐Onsager network model was used to understand the fundamental working principle of this novel device architecture.

Damage to the surface of sensitive paper and board in the form of marks, scratches and bruising is a severe problem faced by converters. Caused by physical interaction in the [...]

Supplemental Digital Content is available in the text.

EZH2/PRC2 catalyzes transcriptionally repressive methylation at lysine 27 of histone H3 and has been associated with numerous cancer types. Point mutations in EZH2 at Tyr641 and Ala677 identified in non-Hodgkin lymphomas alter substrate specificity and result in increased trimethylation at histone H3K27. Interestingly, EZH2/PRC2 is activated by binding H3K27me3 marks on histones, and this activation is proposed as a mechanism for self-propagation of gene silencing. Recent work has identified GSK126 as a potent, selective, SAM-competitive inhibitor of EZH2 capable of globally decreasing H3K27 trimethylation in cells. Here we show that activation of PRC2 by an H3 peptide trimethylated at K27 is primarily an effect on the rate-limiting step (kcat) with no effect on substrate binding (Km). Additionally, GSK126 is shown to have a significantly longer residence time of inhibition on the activated form of EZH2/PRC2 as compared to unactivated EZH2/PRC2. Overall inhibition constant (Ki*) values for GSK126 were determined to be as low as 93 pM and appear to be driven by slow dissociation of inhibitor from the activated enzyme. The data suggest that activation of EZH2 allows the enzyme to adopt a conformation that possesses greater affinity for GSK126. The long residence time of GSK126 may be beneficial in vivoand may result in durable target inhibition after drug systemic clearance.

The article focuses on a need to develop strategies to process additional naphthas and light components in refineries. It states that refiners should upgrade their naphtha processing trains in order to handle the added light material and to adjust to changes in demand for gasoline and distillate. Other topics include synthetic crude oil (SCO) from Alberta-based upgraders, external factors that can affect naphtha processing and the changing demand for gasoline.

We demonstrate the functionalization of n-type (100) and (111) 3C-SiC surfaces with organosilanes. Self-assembled monolayers (SAMs) of amino-propyldiethoxymethylsilane (APDEMS) and octadecyltrimethoxysilane (ODTMS) are formed via wet chemical processing techniques. Their structural, chemical, and electrical properties are investigated using static water contact angle measurements, atomic force microscopy, and X-ray photoelectron spectroscopy, revealing that the organic layers are smooth and densely packed. Furthermore, combined contact potential difference and surface photovoltage measurements demonstrate that the heterostructure functionality and surface potential can be tuned by utilizing different organosilane precursor molecules. Molecular dipoles are observed to significantly affect the work functions of the modified surfaces. Furthermore, the magnitude of the surface band bending is reduced following reaction of the hydroxylated surfaces with organosilanes, indicating that partial passivation of electrically active surface states is achieved. Micropatterning of organic layers is demonstrated by lithographically defined oxidation of organosilane-derived monolayers in an oxygen plasma, followed by visualization of resulting changes of the local wettability, as well as fluorescence microscopy following immobilization of fluorescently labeled BSA protein.

Porous, highly doped semiconductors are potential candidates for thermoelectric energy conversion elements. We report on the fabrication of thin films of Ge via short‐pulse laser‐sintering of Ge nanoparticles (NPs) in vacuum and study the macroporous morphology of the samples by secondary electron microscopy (SEM) imaging. The temperature dependence of the electrical conductivity and the Seebeck coefficient of undoped Ge is discussed in conjunction with the formation of a defect band near the valence band. We further introduce a versatile method of doping the resulting films with a variety of common dopant elements in group‐IV semiconductors by using a liquid containing the dopant atoms. This method is fully compatible with laser‐direct writing and suited to fabricate small scale thermoelectric generators. The incorporation of the dopants is verified by X‐ray photoelectron spectroscopy (XPS) and their electrical activation is studied by conductivity and thermopower measurements.

Histone methyltransferases (HMT) catalyze the methylation of histone tail lysines, resulting in changes in gene transcription. Misregulation of these enzymes has been associated with various forms of cancer, making this target class a potential new area for the development of novel chemotherapeutics. EZH2 is the catalytic component of the polycomb group repressive complex (PRC2), which selectively methylates histone H3 lysine 27 (H3K27). EZH2 is overexpressed in prostate, breast, bladder, brain, and other tumor types and is recognized as a molecular marker for cancer progression and aggressiveness. Several new reagents and assays were developed to aid in the identification of EZH2 inhibitors, and these were used to execute two high-throughput screening campaigns. Activity assays using either an H3K27 peptide or nucleosomes as substrates for methylation are described. The strategy to screen EZH2 with either a surrogate peptide or a natural substrate led to the identification of the same tractable series. Compounds from this series are reversible, are [3H]-S-adenosyl-L-methionine competitive, and display biochemical inhibition of H3K27 methylation.

The primacy effect in memory for repetitions describes the phenomenon that participants, when presented with repeated items, are more likely to remember attributes of the first presentation than attributes of later repetitions (DiGirolamo & Hintzman, 1997). Interestingly, this effect is assumed to be due to less thorough encoding of the second and later repetitions. This account resembles the explanation typically invoked to explain spacing effects in item memory. Based on the similarity between the accounts of the spacing effect and the primacy effect in memory for repetitions, we suggest that the primacy effect of repetitions should also be moderated by the lag between repetitions. This prediction is tested in two experiments using a source memory paradigm that allows for studying the primacy effect of repetitions, spacing effects in item memory, and source memory in a unified paradigm. The results show that the primacy effect for memory of repetitions occurs at short lags between repetitions but not under long lags.

Freestanding silicon nanocrystals (Si‐ncs) offer unique optical and electronic properties for new photovoltaic, thermoelectric, and other electronic devices. A method to fabricate Si‐ncs which is scalable to industrial usage has been developed in recent years. However, barriers to the widespread utilization of these nanocrystals are the presence of charge‐trapping defects and an oxide shell formed upon ambient atmosphere exposure hindering the charge transport. Here, we exploit low‐cost post‐growth treatment routes based on wet‐etching in hydrofluoric acid plus surface hydrosilylation or annealing enabling a complete native oxide removal and a reduction of the defect density by up to two orders of magnitude. Moreover, when compared with only H‐terminated Si‐ncs we report an enhancement of the conductivity by up to a factor of 400 for films of HF etched and annealed Si‐ncs, which retain a defect density below that of untreated Si‐ncs even after several months of air exposure. Further, we demonstrate that HF etched and hydrosilylated Si‐ncs are extremely stable against oxidation and maintain a very low defect density after a long‐term storage in air, opening the possibility of device processing in ambient atmosphere.

Hybrid organic-inorganic solar cells from poly(3-hexylthiophene) (P3HT) and freestanding silicon nanocrystals (Si-ncs) combine the advantages of silicon-based photovoltaics with the cost-efficient solution processing technique. At present, the microwave-plasma synthesis of Si-ncs that allows for a future upscaling to industrial demands is at the expense of the Si-nc surface quality and the number of charge-trapping defects. Here, we present an enhancement of the solar cell performance by identifying the major factors which are limiting the device efficiency. With the help of low-cost post-growth treatments of the Si-ncs and the optimization of various device parameters, P3HT:Si-ncs bulk heterojunction solar cells with an efficiency up to 1.1% are achieved. In particular, etching of the Si-ncs with hydrofluoric acid to remove the surface oxide shells and surface defects has a strong impact on the solar cell performance. An intermediate Si weight ratio of around 60% is found to lead to the highest current densities. For Si-ncs with very small diameters, an additional enhancement of the open circuit voltage was observed. Moreover, we show that the structural order of P3HT has a strong influence on the efficiency, which can be explained by an improved charge carrier separation at the P3HT/Si-ncs interface in combination with an enhanced charge transport in the P3HT phase.

In this paper, we describe the size reduction and phosphorus doping of silicon nanocrystals (SiNCs) fabricated using a very high frequency (VHF) plasma deposition system. The size reduction of SiNCs is achieved by changing the VHF plasma power. The size of SiNCs changes from 10 to 5 nm. We discuss the relationship between VHF plasma power and the size of SiNCs in terms of radicals in the VHF plasma, such as SiH3, SiH2, SiH, and H. On the other hand, we have fabricated phosphorus-doped SiNCs by adding PH3gas diluted with Ar gas. To confirm where phosphorus atoms are located, electrically detected magnetic resonance (EDMR) measurements are conducted. We have observed a hyperfine interaction between unpaired electrons and phosphorus atoms and enhanced hyperfine splitting owing to a quantum size effect. As a result, we can conclude that phosphorus atoms exist at substitutional sites of SiNCs and they act as donors.

In addition to selecting molecules of pharmacological interest, high-throughput screening campaigns often generate hits of undesirable mechanism, which cannot be exploited for drug discovery as they lead to obvious problems of specificity and developability. Examples of undesirable mechanisms are target alkylation/acylation and compound aggregation. Both types of “promiscuous” mechanisms have been described in the literature, as have methods for their detection. In addition to these mechanisms, compounds can also inhibit by oxidizing susceptible enzyme targets, such as metalloenzymes and cysteine-using enzymes. However, this redox phenomenon has been documented infrequently, and an easy method for detecting this behavior is missing. In this article, the authors describe direct proof of small-molecule oxidation of a cysteine protease by liquid chromatography/tandem mass spectrometry, develop a simple assay to predict this oxidizing behavior by compounds, and show the utility of this assay by demonstrating its ability to distinguish nuisance redox compounds from well-behaved inhibitors in 3 historical GlaxoSmithKline drug discovery efforts.

Light-induced dielectrophoretic movement of polystyrene beads and λ-DNA is studied using thin films of amorphous hydrogenated silicon as local photoaddressable electrodes with a diameter of 4μm. Positive (high-field seeking) dielectrophoretic movement is observed for both types of objects. The absence of strong negative (low-field seeking) dielectrophoresis of DNA at high frequencies is in agreement with the similarity of the dielectric constants of DNA and water, the real part of the dielectric function. The corresponding imaginary part of the dielectric function governed by the conductivity of DNA can be determined from a comparison of the frequency dependence of the dielectrophoretic drift velocity with the Clausius-Mossotti relation.

Two types of formal memory models can be distinguished: measurement models and computational models. Measurement models aim at measuring memory processes. In contrast, computational models focus on explaining the structures and processes underlying memory performance. Whereas measurement models involve a statistical framework for testing the model's assumptions, computational models often cannot be applied to empirical data directly. On the other hand, measurement models usually lack psychological theory, whereas computational models are formalized theories allowing for precise predictions. Using the computational model MINERVA2 as an example, it is shown that the gap between both types of formal models can be bridged quite easily by developing closed-form equations for the model's predictions. The resulting multinomial model combines the advantages of measurement models and computational models. The benefits are demonstrated by applying the model to two experiments on the global similarity effect and the list-length effect. The MINERVA 2-inspired multinomial model provides an excellent fit to the data, a coherent explanation of the effects, and statistically sound measures of the processes involved.

The covalent attachment of small organic molecules and larger functional biomolecules such as DNA, enzymes, or other proteins on semiconductors is a new field of basic research with a pronounced interdisciplinary flavour. The ultimate goal of this endeavour is the creation of novel organic/inorganic heterostructures which can provide a direct link between the complex worlds of biology and digital electronics on a nanometer scale. The purpose of the present survey is to provide an overview over basic physics aspects, preparation methods, as well as possible applications of biofunctionalized semiconductors based on different material systems. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Meier and Perrig (2000) as well as Buchner and Wippich (2000) have shown that simple dissociations between explicit and implicit memory measures need not reflect functional dissociations of hypothetical underlying memory systems. Instead, such dissociations may also result from the fact that some widely used implicit memory measures are simply less reliable than the explicit measures with which they have been compared. We extend this argument in two ways. First, we show that illusion-based memory measures such as the priming measures derived from fame and preference judgement tasks are also subject to the reliability problem. Second, we show that yes—no and two-alternative forced-choice paradigms should, and in fact do, yield virtually identical results as far as the reliability of the memory tests is concerned.

Recent experiments on the DX behaviour of Si in AlGaN alloys are reviewed. From persistent photoconductivity, light-induced electron spin resonance, and thermal quenching experiments as well as from the thermal activation of the generation-recombination noise, a quantitative configuration coordinate diagram is constructed for pure AlN and Al0.75Ga0.25N. The effects of DX centres on the properties of AlGaN alloys with lower Al concentration are discussed.

Summary.:  Epitaxial thin films of Ca(Si 1 − x Ge x )2 with 0 < x ≤ 1 are found to react with the moisture of ambient atmosphere to form new Ca-Si-Ge-O-H compounds which were studied by X-ray diffraction, energy dispersive X-ray analysis, infrared absorption, and thermally induced hydrogen desorption measurements. Pure CaGe2 forms the polygermyne calcium hydroxide intercalation compound Ca(OH)2(GeH)2 upon exposure to humidity, with a trigonal tr6 crystal lattice with a = 4.00(1) and c = 65.3(1)Å. In mixed Ca(Si 1 − x Ge x )2 with smaller Ge content, the group-14 layers are subject to intense oxidation leading to decreased crystallinity. The products exhibit characteristic colours and intense photoluminescence, the peak luminescence varying from 1.35 eV for the reaction product of Ca(Si0.3Ge0.7)2 to 2.6 eV for that of Ca(Si0.5Ge0.5)2.

In the bacterial type II fatty acid synthase system, β-ketoacyl-acyl carrier protein (ACP) synthase III (FabH) catalyzes the condensation of acetyl-CoA with malonyl-ACP. We have identified, expressed, and characterized theStreptococcus pneumoniaehomologue of Escherichia coliFabH. S. pneumoniaeFabH is ∼41, 39, and 38% identical in amino acid sequence to Bacillus subtilis,E. coli,and Hemophilus influenzaeFabH, respectively. The His-Asn-Cys catalytic triad present in other FabH molecules is conserved in S. pneumoniaeFabH. The apparentKmvalues for acetyl-CoA and malonyl-ACP were determined to be 40.3 and 18.6 µm, respectively. PurifiedS. pneumoniaeFabH preferentially utilized straight short-chain CoA primers. Similar to E. coliFabH, S. pneumoniaeFabH was weakly inhibited by thiolactomycin. In contrast, inhibition of S. pneumoniaeFabH by the newly developed compound SB418011 was very potent, with an IC50value of 0.016 µm. SB418011 also inhibited E. coliand H. influenzaeFabH with IC50values of 1.2 and 0.59 µm, respectively. The availability of purified and characterized S. pneumoniaeFabH will greatly aid in structural studies of this class of essential bacterial enzymes and facilitate the identification of small molecule inhibitors of type II fatty acid synthase with the potential to be novel and potent antibacterial agents active against pathogenic bacteria.

Epitaxial growth of thin CaSi2films on various silicon surfaces is described. Transforming the silicide into siloxene leads to high quality epitaxial siloxene films with different orientations. The structural quality of CaSi2and siloxene is investigated by transmission and scanning electron microscopy as well as X-ray diffraction analysis. Siloxene on (110)-Si enables direct observation of the anisotropic vibrational properties by IR transmission measurements. A diode structure was realized with siloxene by using contacts with different work functions as charge carrier injectors. Das epitaktische Wachstum dünner CaSi2-Filme auf verschiedenen Siliziumoberflächen wird beschrieben. Die Umwandlung des Silizids in Siloxen ergibt unterschiedlich orientierte epitaktische Siloxenfilme von hoher Schichtqualität. Die strukturelle Qualität von CaSi2und Siloxen wurde mit Hilfe von Transmissionselektronen- und Rastertunnelmikroskopie sowie Röntgendiffraktion untersucht. Siloxen auf (110)-Si ermöglicht einen direkten Nachweis der anisotropen Schwingungseigenschaften mittels IR-Transmissionsmessungen. Mit Siloxen und Kontakten mit unterschiedlicher Austrittsarbeit zur Ladungsträgerinjektion wurde eine Diodenstruktur hergestellt.

A spin-dependent component of the space-charge limited current in nin-structures is found at room temperature, which resonantly increases the SCLC at g= 2.0055. Trapping and reemission at dangling bonds are discussed as the possible origins of this resonance. The most dominant transport process, the hopping of electrons in the conduction band tail, cannot be observed due to the short spin-lattice relaxation time of this state at 300 K.

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Steroidal 3-carboxy-20-ketones have been prepared within two structural series, the androsta-3,5-dienes and the estra-1,3,5-trienes, as potential inhibitors of types 1 and 2 steroid 5α-reductase, the enzyme activity responsible for the final step in biosynthesis of dihydrotestosterone. These compounds are shown to be potent uncompetitive inhibitors of both human recombinant enzyme activities, defining a novel class of dual steroid 5α-reductase inhibitors.

Introduction

We demonstrate optical single electron spin-initialization, -storage and -readout in a single self-assembled InGaAs quantum dot using a spin memory device. Single electron spin relaxation is monitored over timescales exceeding ≥30s, defined only by extrinsic experimental parameters such as the optical detection efficiency. The selective generation of a single electron in the dot is performed by resonant optical excitation and subsequent partial exciton ionization; the hole is removed from the dot whilst the electron remains stored. When subject to a magnetic field applied in Faraday geometry, we show how the spin of the electron can be prepared with a well defined spin projection relative to the light propagation direction simply by controlling the voltage applied to the gate electrode. The spin is stored then in the dot before being read out using an optical implementation of spin to charge conversion, whereby the spin projection of the electron is mapped onto a more robust variable, the charge state of the dot. After spin to charge conversion, we show how the charge occupancy can be repeatedly and non-perturbatively measured by pumping a luminescence recycling transition. The approach is shown to provide a readout signal 104times stronger per spinwhen compared to previous methods. In combination with spin manipulation using the optical Stark effect or microwaves, our approach provides an ideal basis for probing spin coherence in single self-assembled quantum dots over long timescales and the development of methods for coherent spin control.

The linewidth of the low-field electrically detected magnetic resonance (LFEDMR) of phosphorus electrons in silicon is investigated using samples with various 29Si nuclear spin fractions and is compared to that of X-band electron paramagnetic resonance (EPR). The linewidths of LFEDMR and EPR are the same even though LFEDMR signals are obtained based on spin-dependent recombination, suggesting that the interaction between electron spins of phosphorus and recombination centers is strong enough for the LFEDMR detection but weak enough not to affect the linewidths. This favorable balance makes LFEDMR an attractive method to elucidate the low-field behavior of paramagnetic defects in semiconductors.

For Abstract see ChemInform Abstract in Full Text.

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Spin-dependent transport processes in hydrogenated amorphous silicon (a-Si:H) solar cells at high frequencies are studied with capacitively-detected magnetic resonance (CDMR). A resonant increase of the capacitance at room temperature is found with a g-factor of 2.0055, characteristic for the spin-dependent trapping of electrons at neutral dangling bonds. It is shown that with the help of spin-dependent capacitance measurements quantitative information on the defect density in the device can be obtained, a particular advantage over conventional electrically detected magnetic resonance (EDMR) where resonant changes of the dc conductivity are measured.