Formation of electron traps in semiconducting polymers via a slow triple-encounter between trap precursor particles.

Already in 2012, Blom et al. reported (Nature Materials 2012, 11, 882) in semiconducting polymers on a general electron-trap density of ≈3 × 1017 cm-3, centered at an energy of ≈3.6 eV below vacuum. It was suggested that traps have an extrinsic origin, with the water-oxygen complex [2(H2O)-O2] as a possible candidate, based on its electron affinity. However, further evidence is lacking and the origin of universal electron traps remained elusive. Here, in polymer diodes, the temperature-dependence of reversible electron traps is investigated that develop under bias stress slowly over minutes to a density of 2 × 1017 cm-3, centered at an energy of 3.6 eV below vacuum. The trap build-up dynamics follows a 3rd-order kinetics, in line with that traps form via an encounter between three diffusing precursor particles. The accordance between universal and slowly evolving traps suggests that general electron traps in semiconducting polymers form via a triple-encounter process between oxygen and water molecules that form the suggested [2(H2O)-O2] complex as the trap origin.

Formation of universal electron traps in polymer light-emitting diodes is a dynamic process that occurs via a slow triple-encounter between trap precursor species, with the water-oxygen [2(H₂O)-O₂] complex as a likely candidate.

Sustainable-Macromolecule-Assisted Preparation of Cross-linked, Ultralight, Flexible Graphene Aerogel Sensors toward Low-Frequency Strain/Pressure to High-Frequency Vibration Sensing.

Ultralight and highly flexible aerogel sensors, composed of reduced graphene oxide cross-linked by sustainable-macromolecule-derived carbon, are prepared via facile freeze-drying and thermal annealing. The synergistic combination of cross-linked graphene nanosheets and micrometer-sized honeycomb pores gives rise to the exceptional properties of the aerogels, including superior compressibility and resilience, good mechanical strength and durability, satisfactory fire-resistance, and outstanding electromechanical sensing performances. The corresponding aerogel sensors, operated at an ultralow voltage of 0.2 V, can efficiently respond to a wide range of strains (0.1-80%) and pressures (13-2750 Pa) even at temperatures beyond 300 °C. Moreover, the ultrahigh-pressure sensitivity of 10 kPa-1 and excellent sensing stability and durability are accomplished. Strikingly, the aerogel sensors can also sense the vibration signals with ultrahigh frequencies of up to 4000 Hz for >1 000 000 cycles, significantly outperforming those of other sensors. These enable successful demonstration of the exceptional performance of the cross-linked graphene-based biomimetic aerogels for sensitive monitoring of mechanical signals, e.g., acting as wearable devices for monitoring human motions, and for nondestructive monitoring of cracks on engineering structures, showing the great potential of the aerogel sensors as next-generation electronics.

Probing the complexity of wood with computer vision: from pixels to properties.

We use data produced by industrial wood grading machines to train a machine learning model for predicting strength-related properties of wood lamellae from colour images of their surfaces. The focus was on samples of Norway spruce (Picea abies) wood, which display visible fibre pattern formations on their surfaces. We used a pre-trained machine learning model based on the residual network ResNet50 that we trained with over 15 000 high-definition images labelled with the indicating properties measured by the grading machine. With the help of augmentation techniques, we were able to achieve a coefficient of determination (R2) value of just over 0.9. Considering the ever-increasing demand for construction-grade wood, we argue that computer vision should be considered a viable option for the automatic sorting and grading of wood lamellae in the future.

Networking the forest infrastructure towards near real-time monitoring - A white paper.

Forests account for nearly 90 % of the world's terrestrial biomass in the form of carbon and they support 80 % of the global biodiversity. To understand the underlying forest dynamics, we need a long-term but also relatively high-frequency, networked monitoring system, as traditionally used in meteorology or hydrology. While there are numerous existing forest monitoring sites, particularly in temperate regions, the resulting data streams are rarely connected and do not provide information promptly, which hampers real-time assessments of forest responses to extreme climate events. The technology to build a better global forest monitoring network now exists. This white paper addresses the key structural components needed to achieve a novel meta-network. We propose to complement - rather than replace or unify - the existing heterogeneous infrastructure with standardized, quality-assured linking methods and interacting data processing centers to create an integrated forest monitoring network. These automated (research topic-dependent) linking methods in atmosphere, biosphere, and pedosphere play a key role in scaling site-specific results and processing them in a timely manner. To ensure broad participation from existing monitoring sites and to establish new sites, these linking methods must be as informative, reliable, affordable, and maintainable as possible, and should be supplemented by near real-time remote sensing data. The proposed novel meta-network will enable the detection of emergent patterns that would not be visible from isolated analyses of individual sites. In addition, the near real-time availability of data will facilitate predictions of current forest conditions (nowcasts), which are urgently needed for research and decision making in the face of rapid climate change. We call for international and interdisciplinary efforts in this direction.

Reconstructing radial stem size changes of trees with machine learning.

Like many scientists, ecologists depend heavily on continuous uninterrupted data in order to understand better the object of their study. Although this might be straightforward to achieve under controlled laboratory conditions, the situation is easily complicated under field conditions where sensors and data transmission are affected by harsh weather, living organisms, changes in atmospheric conditions etc. This often results in parts of the data being corrupted or missing altogether. We propose the use of the most recent machine-learning techniques to reverse such data losses in multi-channel time series. In particular, we focus on tree stem growth data obtained from the TreeNet project, which monitors the changes in stem radius and environmental conditions of a few hundred trees across Switzerland. In the first part of the study, we test the performance of five architectures based on encoders and recurrent and convolutional neural networks, and we show that a deep neural network combining long short-term memory with one-dimensional convolutional layers performs the best. In the second part, we adopt this model to reconstruct the original TreeNet dataset, which we then use in a separate classification problem to show the effect of the proposed gap-filling procedure.

Criticality and transmission of information in a swarm of cooperative units.

We show that the intelligence of a swarm of cooperative units (birds) emerges at criticality, as an effect of the joint action of frequent organizational collapses and of spatial correlation as extended as the flock size. The organizational collapses make the birds become independent of one another, thereby allowing the flock to follow the direction of the lookout birds. Long-range correlation violates the principle of locality, making the lookout birds transmit information on either danger or resources with a time delay determined by the time distance between two consecutive collapses.

Complexity and synchronization.

We study a fully connected network (cluster) of interacting two-state units as a model of cooperative decision making. Each unit in isolation generates a Poisson process with rate g . We show that when the number of nodes is finite, the decision-making process becomes intermittent. The decision-time distribution density is characterized by inverse power-law behavior with index mu=1.5 and is exponentially truncated. We find that the condition of perfect consensus is recovered by means of a fat tail that becomes more and more extended with increasing number of nodes N . The intermittent dynamics of the global variable are described by the motion of a particle in a double well potential. The particle spends a portion of the total time tau(S) at the top of the potential barrier. Using theoretical and numerical arguments it is proved that tau(S) is proportional to (1/g)ln(const x N) . The second portion of its time, tau(K), is spent by the particle at the bottom of the potential well and it is given by tau(K)=(1/g)exp(const x N) . We show that the time tau(K) is responsible for the Kramers fat tail. This generates a stronger ergodicity breakdown than that generated by the inverse power law without truncation. We establish that the condition of partial consensus can be transmitted from one cluster to another provided that both networks are in a cooperative condition. No significant information transmission is possible if one of the two networks is not yet self-organized. We find that partitioning a large network into a set of smaller interacting clusters has the effect of converting the fat Kramers tail into an inverse power law with mu=1.5 .

Bioinspired Struvite Mineralization for Fire-Resistant Wood.

High-performance wood materials have attracted significant attention in recent years because of excellent property profiles achieved by relatively easy top-down processing of a renewable resource. A crucial flaw of the renewable wood scaffolds is the low flame retardancy, which we tackled by bioinspired mineralization in an eco-friendly processing step. The formation of the biomineral struvite, commonly found in urinary tract stones, was used for the infiltration of hierarchical wood structures with the necessary ions followed by an in situ synthesis of struvite by ammonium steam fumigation. Struvite decomposes prior to wood, which absorbs heat and releases nonflammable gas and amorphous MgHPO4 resulting from the degradation, which promotes insulating char formation. As a result, the mineralized wood can hardly be ignited and the treatment strongly suppresses the heat release rate and smoke production.

Hierarchical Porous Wood Cellulose Scaffold with Atomically Dispersed Pt Catalysts for Low-Temperature Ethylene Decomposition.

Despite the excellent catalytic properties of individual nanoparticles and atomic clusters, the current capabilities to assemble them into a complex system are insufficient for many practical applications. An objective of this work is to develop a fabrication technology that allows for the simultaneous control of the nanoparticle surface chemistry, elemental distribution, microscale geometry, and large-scale assembly. Using a cellulose structure derived from wood, we fabricate hierarchical porous cellulose scaffolds combining with cerium-doped TiO2. This hybrid material serves as the support for atomically dispersed Pt catalysts and is used to successfully decompose ethylene at 0 °C. The fabrication concept developed in this work would allow mitigating the conflict between the required large active surfaces and the difficulties in handling nanopowders in environmental catalysis, including food preservation and indoor air purification. We thus discover a promising route to manufacture multifunctional materials with complex structures by combining a controllable chemical synthesis with the nature-designed wood scaffold.

Power spectra for both interrupted and perennial aging processes.

We study the power spectrum of a random telegraphic noise with the distribution density of waiting times tau given by psi(tau) proportional to 1tau(mu), with mu approximately 2. The condition mu<2 violates the ergodic hypothesis, and in this case the adoption of Wiener-Khintchine (WK) theorem for the spectrum evaluation requires some caution. We study this problem theoretically and numerically and we prove that the power spectrum obeys the prescription S(f)=Kf(eta), with eta=3-mu, namely, the 1f noise lives at border between the ergodic mu>2 and nonergodic mu<2 condition. We study sequences with the finite length L. In the case mu<2 the adoption of WK theorem is made legitimate by two different kinds of truncation effects: the physical and observation-induced effect. In the former case psi(tau) is truncated at tau approximately T(max) and L>>T(max) ensures the condition of interrupted aging. In this case, we find that K is a number independent of L. The latter case, L<2. We do not limit our treatment to the time asymptotic case, thereby producing a prediction that accounts for the transition from the 1f(eta) to the 1f(2) regime, recently observed in an experiment on blinking quantum dots. Our theoretical approach allows us to discuss some other recent experiments on molecular intermittent fluorescence and affords indications that should help to assess whether the spectrum is determined by the L<>T(max) condition.

Influence of scene structure and content on visual search strategies.

When searching for a target within an image, our brain can adopt different strategies, but which one does it choose? This question can be answered by tracking the motion of the eye while it executes the task. Following many individuals performing various search tasks, we distinguish between two competing strategies. Motivated by these findings, we introduce a model that captures the interplay of the search strategies and allows us to create artificial eye-tracking trajectories, which could be compared with the experimental ones. Identifying the model parameters allows us to quantify the strategy employed in terms of ensemble averages, characterizing each experimental cohort. In this way, we can discern with high sensitivity the relation between the visual landscape and the average strategy, disclosing how small variations in the image induce changes in the strategy.