Abstract: Indoor air pollution affects the global population, especially in developed countries where people spend around 90% of their time indoors. The recent pandemic exacerbated the exposure by relying on indoor spaces and a teleworking lifestyle. VOCs are a group of indoor air pollutants with harmful effects on human health at low concentrations. It is widespread that plants can remove indoor VOCs. To this day, research has combined principles of phytoremediation, biofiltration, and bioremediation into a holistic and sustainable technology called botanical biofiltration. Overall, it is sustained that its main advantage is the capacity to break down and biodegrade pollutants using low energy input. This differs from traditional systems that transfer VOCs to another phase. Furthermore, it offers additional benefits like decreased indoor air health costs, enhanced work productivity, and well-being. However, many disparities exist within the field regarding the role of plants, substrate, and phyllosphere bacteria. Yet their role has been theorized; its stability is poorly known for an engineering approach. Previous research has not addressed the bioaugmentation of the phyllosphere to increase the performance, which could boost the system. Moreover, most experiments have studied passive potted plant systems at a lab scale using small chambers, making it difficult to extrapolate findings into tangible parameters to engineer the technology. Active systems are believed to be more efficient yet require more maintenance and knowledge expertise; besides, the impact of the active flow on the long term is not fully understood. Besides, modeling the system has been oversimplified, limiting the understanding and optimization. This review sheds light on the field’s gains and gaps, like concepts, experiments, and modeling. We believe that embracing a multidisciplinary approach encompassing experiments, multiphysics modeling, microbial community analysis, and coworking with the indoor air sector will enable the optimization of the technology and facilitate its adoption.

Abstract: In contrast to conventional polycrystalline titanium oxide (TiO), it was found recently that the superconducting transition temperature Tc can be significantly enhanced from about 2 K to 7.4 K in cubic TiO thin films grown epitaxially on alpha-Al2O3 substrates. This kind of TiO film is also expected to have distinctive optoelectronic properties, which are still not very clear up to now. Herein, by using infrared (IR) reflection measurement we investigate the temperature-dependent optoelectronic response of a cubic TiO thin film, in which temperature induced phase transitions are observed. The semiconductor-, metallic- and semiconductor-like electronic phases of this superconducting film are found in the temperature regimes from 10 to 110 K, 110 to 220 K and above 220 K, respectively. The results obtained optically are consistent with those measured by transport experiment. Furthermore, based on an improved reflection model developed here, we extract the complex optical conductivity of the cubic TiO thin film. We are able to approximately determine the characteristic parameters (e.g., effective electron mass, carrier density, scattering time, etc.) for different electronic phases by fitting the optical conductivity with the modified Lorentz formula. These results not only deepen our understanding of the fundamental physics for cubic TiO thin films but also may find applications in optoelectronic devices based on superconductors.

Abstract: Multi-objective optimization is an important decision-making tool for energy processes, as multiple targets need to be achieved. These objectives are usually conflicting since a single solution cannot be optimal for all objectives, resulting in a set of Pareto-optimal solutions. Multiple indicators might be available to describe a sustainability objective, such as the environmental impact which is commonly evaluated by performing a life cycle assessment. In this study, Pareto aggregation is proposed as a method which employs a novel multi-objective optimization-based approach as an alternative to the classically used aggregation in life cycle assessment. This method identifies conflicting environmental indicators and performs an aggregation among those that require a trade-off. An environmental-economic optimization of a second-generation bioethanol plant is used to illustrate and evaluate the proposed method. Process parameters from a biochemical conversion pathway flowsheet simulation model are chosen as optimization variables. To reduce the computational time, surrogate models, based on artificial neural networks, are used. Out of the eighteen ReCiPe Midpoint environmental indicators, five were identified as conflicting, resulting in an aggregated environmental objective, which was then traded off with the economic objective function, chosen as the levelized cost of ethanol. Comparison with the widely used single-score EcoIndicator99 showed that the Pareto aggregation method can reduce most of the environmental indicators by up to 6.5%. This research provides an insight on non-redundant objective functions, aiming at reducing the dimensionality of multi-objective optimization problems, while taking into consideration decision-makers’ preferences.

Abstract: Oxocarbons, known for over two centuries, have recently revealed a long-awaited facet: two-dimensional crystalline structures. Employing an intelligent global optimization algorithm (IGOA) alongside densityfunctional calculations, we unearthed a quasi -flat oxocarbon (C 6 0 6 ), featuring an oxygen -decorated hole, and a novel 3D-borocarbon. Comparative analyses with recently synthesized isostructures, such as 2D -porous carbon nitride (C 6 N 6 ) and 2D -porous boroxine (B 6 0 6 ), highlight the unique attributes of these compounds. All structures share a common stoichiometry of X 6 Y 6 (which we call COF-66), where X = B, C, and Y = B, N, O (with X not equal Y), exhibiting a 2D -crystalline structure, except for borocarbon C 6 B 6 , which forms a 3D crystal. In our comprehensive study, we conducted a detailed exploration of the electronic structure of X 6 Y 6 compounds, scrutinizing their thermodynamic properties and systematically evaluating phonon stability criteria. With expansive surface areas, diverse pore sizes, biocompatibility, pi-conjugation, and distinctive photoelectric properties, these structures, belonging to the covalent organic framework (COF) family, present enticing prospects for fundamental research and hold potential for biosensing applications.

Abstract: Photocathodes play a vital role in photoelectrocatalytic water splitting by acting as catalysts for reducing protons to hydrogen gas when exposed to light. Recent advancements in photocathodes have focused on addressing the limitations of noble metal-based materials. These noble metal-based photocathodes rely on expensive and scarce metals such as platinum and gold as cocatalysts or ohmic back contacts, respectively, rendering the final system less sustainable and costly when applied at scale. This mini-review summarizes the important recent progress in the development of non-noble metal-based photocathodes and their performance in the hydrogen evolution reaction during photoelectrochemical (PEC) water splitting. These advancements bring non-noble metal-based photocathodes closer to their noble metal-based counterparts in terms of performance, thereby paving the way forward toward industrial-scale photoelectrolyzers or PEC cells for green hydrogen production.

Abstract: Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H2O2, glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions.

Abstract: The overexpression of voltage dependent anion channels (VDACs), particularly VDAC1, in cancer cells compared to normal cells, plays a crucial role in cancer cell metabolism, apoptosis regulation, and energy homeostasis. In this study, we used molecular dynamics (MD) simulations to investigate the effect of a low level of VDAC1 oxidation (induced e.g., by cold atmospheric plasma (CAP)) on the pyruvate (Pyr) uptake by VDAC1. Inhibiting Pyr uptake through VDAC1 can suppress cancer cell proliferation. Our primary target was to study the translocation of Pyr across the native and oxidized forms of hVDAC1, the human VDAC1. Specifically, we employed MD simulations to analyze the hVDAC1 structure by modifying certain cysteine residues to cysteic acids and methionine residues to methionine sulfoxides, which allowed us to investigate the effect of oxidation. Our results showed that the free energy barrier for Pyr translocation through the native and oxidized channel was approximately 4.3 +/- 0.7 kJ mol-1 and 10.8 +/- 1.8 kJ mol-1, respectively. An increase in barrier results in a decrease in rate of Pyr permeation through the oxidized channel. Thus, our results indicate that low levels of CAP oxidation reduce Pyr translocation, resulting in decreased cancer cell proliferation. Therefore, low levels of oxidation are likely sufficient to treat cancer cells given the inhibition of Pyr uptake.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: Mixed-halide perovskites show promising applications in tandem solar cells owing to their adjustable bandgap. One major obstacle to their commercialization is halide phase segregation, which results in large open-circuit voltage deficiency and J-V hysteresis. However, the ambiguous interplay between structural origin and phase segregation often results in aimless and unspecific optimization strategies for the device's performance and stability. An atomic scale is directly figured out the abundant Ruddlesden-Popper anti-phase boundaries (RP-APBs) within a CsPbIBr2 polycrystalline film and revealed that phase segregation predominantly occurs at RP-APB-enriched interfaces due to the defect-mediated lattice strain. By compensating their structural lead halide, such RP-APBs are eliminated, and the decreasing of strain can be observed, resulting in the suppression of halide phase segregation. The present work provides the deciphering to precisely regulate the perovskite atomic structure for achieving photo-stable mixed halide wide-bandgap perovskites of high-efficiency tandem solar cell commercial applications. The phase segregation in mixed halide perovskite film predominantly occurs at Ruddlesden-Popper anti-phase boundaries (RP-APBs)-enriched interfaces due to the defect-mediated lattice strain. The RP-APBs defects can be eliminated by compensating for their structural lead halide deficiency, resulting in the suppression of halide phase segregation. image

Abstract: Today, illicit drugs are omnipresent in society. Clandestine markets are growing faster than ever before, record amounts of cocaine are seized in seaports and airports, while the associated violence is spiralling out of control. In addition, drug monitoring centres worldwide are warning for the increasing complexity of the drug markets, as the traditionally popular drugs are joined by countless new synthetic variants, while medical drugs are also increasingly being abused. In order to provide services confronted with illicit drug samples (police, customs, forensic scientists, first responders, …) with important information on the identity of an unknown sample, suitable analytical tests are required. While these exist for laboratory environments, on-site applicable tests are important to accelerate the decision-making process. Electrochemical sensors have all the advantages required for such on-site tests: they are fast, portable, easy-to-use and reliable. Furthermore, they are not influenced by colours, which are frequently added to drug samples to deceive the existing tests. Previous work has mainly focussed on the detection of a single drug per analysis. However, many drugs could be encountered due to the diversity of the drug markets. Therefore, this project developed electrochemical strategies for the detection of multiple drugs simultaneously. First, the electrochemical behaviour of the individual drugs was studied in different measuring conditions (assessing the influence of pH, concentration and temperature). Then, all findings and strategies were combined to detect multiple targets simultaneously. An electrochemical sensor was developed for the four most popular drugs at music festivals: cocaine, MDMA, amphetamine and ketamine. This sensor generates a so-called ‘superfingerprint’ of the sample, which is then automatically interpreted by a developed algorithm in order to produce a straightforward output. Finally, a pill analysis sensor was developed in the context of drug checking services, where a consumer can anonymously have a sample chemically analysed to obtain information on the composition, dose and potentially harmful additives. The sensor achieved an outstanding accuracy in identifying the main component and provided the option to quantify, as well as an indication on the presence of other substances in the sample. The project’s findings demonstrate the potential for electrochemistry in illicit drug detection and provide a basis for the development of new sensors, targeting other drug combinations.

Abstract: Tropical rainforests host an exceptional biodiversity and play a fundamental role in the regulation of global climatic cycles. Soil fungi and bacteria are key players in the transformation and processing of nutrients in terrestrial ecosystems while having an essential role as tree mutualists or antagonists. Still, there are gaps in our understanding of the main variables driving soil microbes on these forests and it is unclear how future climate change scenarios may impact soil microbes and further affect the ecosystem. In this thesis, we first explored the drivers of the microbial community composition in two pristine forests in French Guiana by using amplicon DNA sequencing. The neighboring tree species were found to be a crucial factor influencing the fungal and bacterial community composition at our sites regardless of the season. Additionally, within the environmental factors explored, soil moisture, phosphorus (P) and nitrogen (N) availability were consistently the main soil properties controlling the composition of soil microbial communities. Secondly, as increased nutrient deposition due to anthropogenic activities are expected to affect tropical forests ecosystems N and P availability, a factorial N and P nutrient addition experiment in the same sites was used to assess the effects of changes in the soil nutrient stoichiometry on the soil microbial communities. These results showed that after 3 years of nutrient additions, the bacterial and fungal community composition was affected by both the N and P additions. Besides, the fungal community composition had a stronger response to the nutrient addition, especially when P was added. Moreover, when the nutrient addition effect was assessed in bacteria and fungi with different life strategies, we found different nutrient optima between them. Furthermore, to study the effect of the connection to an existing mycorrhizal mycelium on tree seedlings, I established a mycelium exclusion experiment. Interestingly, we could not detect an effect of the mycorrhizal mycelium exclusion on the seedling N uptake, performance, or fungal community composition in roots after one year. All together this work provides a deeper understanding of the factors influencing the soil microbial communities on these lowland tropical forests, demonstrating that the tree community composition exerts a higher influence on the soil microbial community composition than previously expected. Moreover, our results show that the fungal and bacterial community composition and its relationship with trees in the vicinity is highly dependent on the ecosystem nutrient availability.

Abstract: Recent experiments have revealed that the macroscopic Kelvin equation remains surprisingly accurate even for nanoscale capillaries. This phenomenon was so far explained by the oscillatory behavior of the solid−liquid interfacial free energy. We here demonstrate thermodynamic and capillarity inconsistencies with this explanation. After revising the Kelvin equation, we ascribe its validity at nanoscale confinement to the effect of disjoining pressure.
To substantiate our hypothesis, we employed molecular dynamics simulations to evaluate interfacial heat transfer and wetting properties. Our assessments unveil a breakdown in a previously established proportionality between the work of adhesion and the Kapitza conductance at capillary heights below 1.3 nm, where the dominance of the work of adhesion shifts primarily from energy to entropy. Alternatively, the peak density of the initial water layer can effectively probe the work of adhesion. Unlike under bulk conditions, high confinement renders the work of adhesion entropically unfavorable.

Abstract: A thorough and quantitative understanding of the fate of excitons in covalent–organic frameworks (COFs) after photoexcitation is essential for their augmented optoelectronic and photocatalytic applications via precise structure tuning. The synthesis of a library of COFs having identical chemical backbone with impeded conjugation, but varied morphology and surface topography to study the effect of these physical properties on the photophysics of the materials is herein reported. The variation of crystallite size and surface topography substantified different aggregation pattern in the COFs, which leads to disparities in their photoexcitation and relaxation properties. Depending on aggregation, an inverse correlation between bulk luminescence decay time and exciton binding energy of the materials is perceived. Further transient absorption spectroscopic analysis confirms the presence of highly localized, immobile, Frenkel excitons (of diameter 0.3–0.5 nm) via an absence of annihilation at high density, most likely induced by structural torsion of the COF skeletons, which in turn preferentially relaxes via long‐lived (nanosecond to microsecond) excimer formation (in femtosecond scale) over direct emission. These insights underpin the importance of structural and topological design of COFs for their targeted use in photocatalysis.

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image

Abstract: Complex transition-metal oxides exhibit a wide variety of chemical and physical properties which are a strong function the local electronic states of the transition-metal centres, as determined by a combination of metal oxidation state and local coordination environment. Topochemical reduction of the double perovskite oxide, LaSrCoRuO6, using Zr, yields LaSrCoRuO5. This reduced phase contains an ordered array of apex-linked square-based pyramidal Ru3+O5, square-planar Co1+O4 and octahedral Co3+O6 units, consistent with the coordination-geometry driven disproportionation of Co2+. Coordination-geometry driven disproportionation of d(7) transition-metal cations (e.g. Rh2+, Pd3+, Pt3+) is common in complex oxides containing 4d and 5d metals. However, the weak ligand field experienced by a 3d transition-metal such as cobalt leads to the expectation that d(7+) Co2+ should be stable to disproportionation in oxide environments, so the presence of Co1+O4 and Co3+O6 units in LaSrCoRuO5 is surprising. Low-temperature measurements indicate LaSrCoRuO5 adopts a ferromagnetically ordered state below 120 K due to couplings between S=(1)/(2) Ru3+ and S=1 Co1+.