Scientific Challenges Research Articles

Deeper but smaller: Higher-order interactions increase linear stability but shrink basins.

A key challenge of nonlinear dynamics and network science is to understand how higher-order interactions influence collective dynamics. Although many studies have approached this question through linear stability analysis, less is known about how higher-order interactions shape the global organization of different states. Here, we shed light on this issue by analyzing the rich patterns supported by identical Kuramoto oscillators on hypergraphs. We show that higher-order interactions can have opposite effects on linear stability and basin stability: They stabilize twisted states (including full synchrony) by improving their linear stability, but also make them hard to find by markedly reducing their basin size. Our results highlight the importance of understanding higher-order interactions from both local and global perspectives.

Agglomeration phenomenon in graphene/polymer nanocomposites: Reasons, roles, and remedies

The addition of low-loading content of nanofillers may improve the material properties of polymer-based nanocomposites. This improvement directly corresponds to the density of well-dispersed nanofillers in the matrix. However, for higher nanofiller loadings, the nanocomposites' material properties not only may not be improved but also may be degraded due to agglomeration. This complex phenomenon, where nanofillers tend to form agglomerates with the enhancement of volume fraction, poses significant challenges in materials science and nanotechnology. It has been proven that agglomerations hinder the performance of the nanocomposites and thwart the unique properties of nanofillers in most aspects. Graphene, one of the most used nanofillers, plays a remarkable role in nanotechnology. Therefore, the key focus of the current review is to provide insight into the impact of agglomeration on the various material properties such as tensile, flexural, fracture, fatigue, thermal, electrical, and barrier characteristics of the polymer nanocomposites reinforced by graphene-based structures. A comprehensive review of the factors leading to the agglomeration of graphene in the nanocomposites was presented. It was concluded that agglomeration could be a barrier to developing polymer-based nanocomposites, and the challenges of controlling the nanofiller agglomerations were discussed in depth, highlighting the issue's complexity.

Gate-set evaluation metrics for closed-loop optimal control on nitrogen-vacancy center ensembles in diamond

A recurring challenge in quantum science and technology is the precise control of their underlying dynamics that lead to the desired quantum operations, often described by a set of quantum gates. These gates can be subject to application-specific errors, leading to a dependence of their controls on the chosen circuit, the quality measure and the gate-set itself. A natural solution would be to apply quantum optimal control in an application-oriented fashion. In turn, this requires the definition of a meaningful measure of the contextual gate-set performance. Therefore, we explore and compare the applicability of quantum process tomography, linear inversion gate-set tomography, randomized linear gate-set tomography, and randomized benchmarking as measures for closed-loop quantum optimal control experiments, using a macroscopic ensemble of nitrogen-vacancy centers in diamond as a test-bed. Our work demonstrates the relative trade-offs between those measures and how to significantly enhance the gate-set performance, leading to an improvement across all investigated methods.

Diagnosis of human envenoming by terrestrial venomous animals: Routine, advances, and perspectives

Despite the development of new and advanced diagnostic approaches, monitoring the clinical evolution of accidents caused by venomous animals is still a challenge for science. In this review, we present the state of the art of laboratory tests that are routinely used for the diagnosis and monitoring of envenomings by venomous animals, as well as the use of new tools for more accurate and specific diagnoses. While a comprehensive range of tools is outlined, comprising hematological, biochemical, immunoassays, and diagnostic imaging tools, it is important to acknowledge their limitations in predicting the onset of clinical complications, since they provide an overview of organic damage after its development. Thus, the need for discovery, validation, and use of biomarkers that have greater predictive power, sensitivity and specificity is evident. This will help in the diagnosis, monitoring, and treatment of patients envenomated by venomous animals, consequently reducing the global burden of morbidity and mortality.

Using mangrove and field observation data to identify fine-scale species distributions: a case study in bockadams (Serpentes: Homalopsidae: Cerberus).

Characterization of species distributions is a fundamental challenge in biodiversity science, with particular significance for downstream evolutionary studies, conservation efforts, field-based faunal studies and estimates of species diversity. Checklists and phylogenetic studies often focus on poorly known, rare taxa with limited ranges. However, studies of widely distributed, ecologically important species that are abundant in their preferred microhabitats are also important for systematics and local conservation efforts, but less often studied. We collected novel natural history data during fieldwork (2019-2023) for Philippine populations of bockadams (Homalopsidae: Cerberus), one of the most abundant vertebrates in Southeast Asian aquatic systems. Considered a coastal snake, many studies report Cerberus inland. We report the frequency of encounters of Cerberus schneiderii, and the IUCN data-deficient, Philippine-endemic Cerberus microlepis during six expeditions (62 days; 1041 person-hours). We report new occurrence data for 69 C. schneiderii and 6 C. microlepis for coastal and inland populations, water measurements and dietary observations. Regression analyses and ecological niche models show the importance of coastal and mangrove habitats for Cerberus. Our study is the most comprehensive assessment of Philippine Cerberus populations to date and provides critical baseline natural history data for downstream research on widespread and range-restricted species of Southeast Asian snakes.

Spontaneous Alignment of Boron Nitride Nanotubes into Polycrystalline Film Arrays for Enhanced Piezoelectric Nanogeneration

1D nanomaterials feature distinct physical properties owing to their characteristic anisotropy and size‐induced quantization; yet, creating an ordered ensemble of the nanomaterials is nontrivial, marking it as a grand challenge in materials science. Herein, the spontaneous alignment of a model 1D nanomaterial, boron nitride nanotube (BNNT), into a highly ordered multidomain film is presented. A conventional, benchtop gravity filtration is employed, during which thickening of the BNNT dispersion leads to a formation of the liquid crystal (LC) phase that is readily isolatable. The spontaneity of the phase separation excludes notoriously persisting synthetic impurities to a degree >99%, from ≈50% of the initial material purity. The LC‐based thin film features an aligned multidomain with an exceptionally low angular deviation of <2.5° and a domain size of >50 μm. A piezoelectric nanogenerator based on the thin film records a 1000‐fold increase in the piezocurrent compared to its random analog. This work demonstrates the first example of the spontaneous alignment of an unconventional nanotube system and the realization of the macroscopic properties therefrom, which can be readily expanded to 1D materials in general.

Chiral Mesostructure‐Induced Ferroelectricity and Photoelectric Chiral Anisotropy of MnWO4

Creating ferroelectricity in centrosymmetric and paramagnetic materials through nanostructures presents a challenge in materials science due to their high symmetry and paired spins. Herein, the induction of chirality‐induced ferroelectricity and photoelectric chiral anisotropy in chiral mesostructured MnWO4 films (CMWFs) resulting from spin chiral anisotropy in chiral mesostructures is reported. The CMWFs, prepared via a hydrothermal route induced by chiral molecules, consists of densely packed MnWO4 nanoparticle crystals exhibiting long‐range ordered helical lattice distortion grown perpendicularly along the 010 axis on the substrates. These CMWFs exhibit ferroelectric properties with a Curie temperature of ≈60 °C and photoelectric chiral anisotropy with an anisotropy factor of ≈0.5. The findings pave the way for ferroelectricity through long‐range spin current via chirality.

An Expedient Route to Bio‐based Polyacrylate Alternatives with Inherent Degradation Capabilities by Organic Catalysis for Polymerization of Muconate Esters

The quest for polymers that would be at the same time bio‐based and degradable after usage, in addition to offering chemical post‐modification options, remains a daunting challenge in contemporary polymer science. Despite advances in polymer chemistry, attempts at controlling the chain‐growth polymerization of muconate esters remain unexplored. Here we show that dialkyl muconates can be rapidly polymerized by organocatalyzed group transfer polymerization (O‐GTP). O‐GTP is conducted to completion at room temperature in toluene within a few minutes, using 1‐ethoxy‐1‐(trimethylsiloxy)‐1,3‐butadiene (ETSB) as initiator and 1‐tert‐butyl‐4,4,4‐tris(dimethylamino)‐2,2‐bis[tris(dimethylamino)‐phosphoranylidenamino]‐25,45 catenadi(phosphazene) (P4‐t­‐Bu) as catalyst. Chain extension experiments and synthesis of all muconate‐type block copolymers can also be achieved. Furthermore, polymuconates are amenable to facile post‐polymerization modification reactions. This is showcased through the hydrolysis of the ester side chains leading to well‐defined poly(muconic acid), and by epoxidation of the C=C double bonds of the main chain. Last but not least, these internal alkene groups can be selectively cleaved by ozonolysis, demonstrating the upcyclability of polymuconates under oxidative conditions. This work demonstrates that polymuconates constitute a unique platform of bio‐based polymers, easily modifiable in addition to being chemically degradable under user friendly experimental conditions.

An Expedient Route to Bio-Based Polyacrylate Alternatives with Inherent Post-Chemical Modification and Degradation Capabilities by Organic Catalysis for Polymerization of Muconate Esters.

The quest for polymers that would be at the same time bio-based and degradable after usage, in addition to offering chemical post-modification options, remains a daunting challenge in contemporary polymer science. Despite advances in polymer chemistry, attempts at controlling the chain-growth polymerization of muconate esters remain unexplored. Here we show that dialkyl muconates can be rapidly polymerized by organocatalyzed group transfer polymerization (O-GTP). O-GTP is conducted to completion at room temperature in toluene within a few minutes, using 1-ethoxy-1-(trimethylsiloxy)-1,3-butadiene (ETSB) as initiator and 1-tert-butyl-4,4,4-tris(dimethylamino)-2,2-bis[tris(dimethylamino)-phosphoranylidenamino]-2 5,4 5 catenadi(phosphazene) (P4-t-Bu) as catalyst. Chain extension experiments and synthesis of all muconate-type block copolymers can also be achieved. Furthermore, polymuconates are amenable to facile post-polymerization modification reactions. This is showcased through the hydrolysis of the ester side chains leading to well-defined poly(muconic acid), and by epoxidation of the C=C double bonds of the main chain. Last but not least, these internal alkene groups can be selectively cleaved by ozonolysis, demonstrating the upcyclability of polymuconates under oxidative conditions. This work demonstrates that polymuconates constitute a unique platform of bio-based polymers, easily modifiable in addition to being chemically degradable under user friendly experimental conditions.

Accelerating optimal elemental configuration search in crystal using Ising machine

This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. Using the third-generation Fujitsu's Digital Annealer, we have successfully obtained reasonable solutions for crystal structures consisting of over 10000 atoms. Notably, we have also obtained plausible solutions for optimization problems with constrained solutions, such as situations where the composition ratio of atomic species is predetermined. These findings suggest that Ising machines can be valuable tools for addressing materials science challenges. Published by the American Physical Society 2024

Plasmonic Particle Integration into Near-Infrared Photodetectors and Photoactivated Gas Sensors: Toward Sustainable Next-Generation Ubiquitous Sensing.

Current challenges in environmental science, medicine, food chemistry as well as the emerging use of artificial intelligence for solving problems in these fields require distributed, local sensing. Such ubiquitous sensing requires components with 1) high sensitivity, 2) power efficiency, 3) miniaturizability, and 4) the ability to directly interface with electronic circuitry, i.e., electronic readout of sensing signals. Over the recent years, several nanoparticle-based approaches have found their way into this field and have demonstrated high performance. However, challenges remain, such as the toxicity of many of today's narrow bandgap semiconductors for NIR detection and the high energy consumption as well as low selectivity of state-of-the-art commercialized gas sensors. With their unique light-matter interaction and ink-based fabrication schemes, plasmonic nanostructures provide potential technological solutions to these challenges, leading also to better environmental performance. In this perspective recent approaches of using plasmonic nanoparticles are discussed for the fabrication of NIR photodetectors and light-activated, energy-efficient gas sensing devices. In addition, new strategies implying computational approaches are pointed out for miniaturizable spectrometers, exploiting the wide spectral tunability of plasmonic nanocomposites, and for selective gas sensors, utilizing dynamic light activation. The benefits of colloidal approaches for device fabrication are discussed with regard to technological advantages and environmental aspects, which are barely considered so far.

Climate change and greenhouse gas emissions from soils under the winter wheat agroecosystem in Ukraine

The issue of global climate change is one of the most important challenges for modern environmental science. In this regard, assessment of greenhouse gas emissions from soils under agroecosystems is an extremely important task. The main purpose of the study was to assess the potential emissions of greenhouse gases (CO2 and N2O) from the soils under the winter wheat agroecosystem during its spring-summer growing season under the climate change in Ukraine. A comprehensive model of greenhouse gas emissions from agroecosystem soils was used as a theoretical basis. The study considered the main agroclimatic regions of Ukraine, such as Polissia, Forest-Steppe, Northern Steppe, and Southern Steppe. The modelling was performed for average long-term conditions of 1981–2020 and under scenario conditions (using the RCP4.5 climate scenario) for the decades of 2021–2030, 2031–2040, and 2041–2050. Estimates of CO2 emissions were obtained for the agroclimatic regions of Ukraine. According to the results, the largest CO2 emissions (0.886-0.940 t C-CO2/ha) will be expected when the average productive moisture supplies in the arable soil layer for the spring-summer period of winter wheat vegetation are 20–40 mm and the air temperature is 12–15 °C. Rise in the level of moisture reserves, averaged for the period, for more than 40 mm and the air temperature of 11–12 °C is accompanied by decrease in the level of CO2 emission (0.645–0.658 t C-CO2/ha). In arid conditions, with a decrease in the period-average moisture supplies in the arable soil layer to less than 20 mm and heightened average air temperatures (14.5–15.2 °C), a decrease in CO2 emissions is expected (0.755–0.770 t C-CO2/ha). The study showed that the highest N2O emission (1.087–1.703 kg N-N2O/ha) is expected at a high level of moistening (Md being equal to 0.474–0.713 relative units and air temperature – 11.3–13.0 °C) with a high intensity of nitrification and denitrification processes. Conditions characterizing the average level of N2O emission (0.657–0.890 kg N-N2O/ha) are formed under the moistening at a level of Md equaling 0.296–0.447 relative units and air temperature of 13.4–14.7 °C. There is a slowdown in denitrification processes and an increase in the share of emissions owing to nitrification. It has been found that the lowest N2O emission (0.508–0.537 kg N–N2O/ha) is expected under conditions of severe and very severe drought (according to the Md criterion), with a heightened temperature averaged at the level of 14.5–15.2 °C during the spring-summer period of winter wheat vegetation.

How mentoring and a shift in mindset and practices can help navigate fast-paced science

Abstract In the competitive realm of academia, the pursuit of meaningful knowledge often collides with the imperative to publish quickly and frequently, sidelining patient investigation and deep understanding. This article aims to share critical insights into this issue, sparking further discussions about the challenges of fast-paced science and cultivating a more ethical scientific landscape, one that values exploration and ingenuity over rushed output and flashy headlines. The narrative traces the growing disillusionment of the first author with the prevailing publish-or-perish culture and her concerns about its impact on research quality and genuine collaboration. The article showcases how a mentoring programme for women postdoctoral researchers and network activities have aided her in navigating these challenges and carving out her career path. Her experiences are enriched by the perspectives of the second author, the coordinator of the mentoring programme. Through one-on-one mentoring, get-togethers, and workshops, the first author gained the inspiration, skills, and encouragement necessary to rekindle her commitment to science. The lesson learned is the importance of providing scientists with a guided, safe, and brave space for open dialogue about individual concerns and general working conditions in academia. Examples are provided that illustrate potential strategies and practices for challenging the status quo. By nurturing an environment that puts quality before quantity, academia can pave the way for transformative discoveries and profound understanding across all career levels.

Implementing reactivity in molecular dynamics simulations with harmonic force fields

The simulation of chemical reactions and mechanical properties including failure from atoms to the micrometer scale remains a longstanding challenge in chemistry and materials science. Bottlenecks include computational feasibility, reliability, and cost. We introduce a method for reactive molecular dynamics simulations using a clean replacement of non-reactive classical harmonic bond potentials with reactive, energy-conserving Morse potentials, called the Reactive INTERFACE Force Field (IFF-R). IFF-R is compatible with force fields for organic and inorganic compounds such as IFF, CHARMM, PCFF, OPLS-AA, and AMBER. Bond dissociation is enabled by three interpretable Morse parameters per bond type and zero energy upon disconnect. Use cases for bond breaking in molecules, failure of polymers, carbon nanostructures, proteins, composite materials, and metals are shown. The simulation of bond forming reactions is included via template-based methods. IFF-R maintains the accuracy of the corresponding non-reactive force fields and is about 30 times faster than prior reactive simulation methods.

Digital Microbe: a genome-informed data integration framework for team science on emerging model organisms

The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. “Digital Microbes” are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacterium Ruegeria pomeroyi DSS-3 with > 100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotroph Alteromonas containing 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings.

A Heterogeneous Acid-Base Organocatalyst For Cascade Deacetalisation-Knoevenagel Condensations.

Multifunctional heterogeneous catalysts are an effective strategy to drive chemical cascades, with attendant time, resource and cost efficiencies by eliminating unit operations arising in normal multistep processes. Despite advances in the design of such catalysts, the fabrication of proximate, chemically antagonistic active sites remains a challenge for inorganic materials science. Hydrogen-bonded organocatalysts offer new opportunities for the molecular level design of multifunctional structures capable of stabilising antagonistic active sites. We report the catalytic application of a charge-assisted, hydrogen-bonded crystalline material, bis(melaminium)adipate (BMA), synthesised from melamine and adipic acid, which possesses proximate acid-base sites. BMA exhibits high activity for the cascade deacetalisation-Knoevenagel condensation of dimethyl acetals to form benzylidenemalononitriles under mild conditions in water; BMA is amenable to large-scale manufacture and recycling with minimal deactivation. Computational modelling of the melaminium cation in protonated BMA explains the observed catalytic reactivity, and identifies the first demethoxylation step as rate-limiting, in good agreement with time-dependent 1H NMR and kinetic experiments. A broad substrate scope for the cascade transformation of aromatic dimethyl acetals is demonstrated.

On the predictability of possible storylines for forced complex systems

Abstract It is well-known that even for fairly simple deterministic nonlinear systems, exact prediction of future state is, on average, impossible beyond some small multiple of the Lyapunov time that quantifies the rate of separation of trajectories within an attractor. Nonetheless, it may be possible to find a physical measure that is the distribution of a trajectory within the attractor. In that sense, there can be a still weaker form of predictability. In this paper, we show that this can also fail but an even weaker form of predictability can appear for non-autonomous (i.e. forced) systems in the presence of tipping points. The {\em predictability of possible storylines} appears when one can interpret the frequencies of runs within an ensemble arriving at one of several possible future attractors (storylines) in a probabilistic manner.
As predictability is a major concern and a challenge in climate science, we illustrate this notion of predictability with two climate-related examples: a chaotic energy balance model and a global ocean model featuring a tipping point of the Atlantic Meridional Overturning Circulation.

Advances and challenges in ecological connectivity science.

Maintaining and restoring ecological connectivity will be key in helping to prevent and reverse the loss of biodiversity. Fortunately, a growing body of research conducted over the last few decades has advanced our understanding of connectivity science, which will help inform evidence-based connectivity conservation actions. Increases in data availability and computing capacity have helped to dramatically increase our ability to model functional connectivity using more sophisticated models. Keeping track of these advances can be difficult, even for connectivity scientists and practitioners. In this article, we highlight some key advances from the past decade and outline many of the remaining challenges. We describe the efforts to increase the biological realism of connectivity models by, for example, isolating movement behaviors, population parameters, directional movements, and the effects of climate change. We also discuss considerations of when to model connectivity for focal or multiple species. Finally, we reflect on how to account for uncertainty and increase the transparency and reproducibility of connectivity research and discuss situations where decisions may require forgoing sophistication for more simple approaches.

Preface to special issue: Private fusion research: Opportunities and challenges in plasma science

Preface to special issue: Private fusion research: Opportunities and challenges in plasma science

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.