Flow electrolysers for reaction scaling
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Rapid urbanization and rising inequality are pressing global concerns, yet inequality is an ancient trait of city life that may be intrinsically connected to urbanism itself. Here we investigate how elite wealth scales with urban population size across culture and time by analyzing ancient Roman and modern cities. Using Bayesian models to address archeological uncertainties, we uncovered a consistent correlation between population size and physical expressions of elite wealth in urban spaces. These patterns suggest the presence of an ancient, enduring mechanism underlying urban inequality. Supported by an agent-based network simulation and informed by the settlement scaling theory, we propose that the observed patterns arise from common preferential attachment in social networks—a simple, yet powerful, driver of unequal access to interaction potential. Our findings open up new directions in urban scaling research and underscore the importance of understanding long-term urban dynamics to chart a course toward a fairer urban future.
An active representation learning method for reaction yield prediction with small-scale data
Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions. In this paper, we present an efficient machine learning tool called “RS-Coreset”, where the key idea is to take advantage of deep representation learning techniques to guide an interactive procedure for representing the full reaction space. Our proposed tool only uses small-scale data, say 2.5% to 5% of the instances, to predict the yields of the reaction space. We validate the performance on three public datasets and achieve state-of-the-art results. Moreover, we apply this tool to assist the realistic exploration of the Lewis base-boryl radicals enabled dechlorinative coupling reactions in our lab. The tool can help us to effectively predict the yields and even discover several feasible reaction combinations that were overlooked in previous articles.
Cytoplasmic flow is a cell size sensor that scales anaphase
During early embryogenesis, fast mitotic cycles without interphase lead to a decrease in cell size, while scaling mechanisms must keep cellular structures proportional to cell size. For instance, as cells become smaller, if the position of nuclear envelope reformation (NER) did not adapt, NER would have to occur beyond the cell boundary. Here we found that NER position in anaphase scales with cell size via changes in chromosome motility, mediated by cytoplasmic flows that themselves scale with cell size. Flows are a consequence of friction between viscous cytoplasm and bulky cargo transported by dynein on astral microtubules. As an emerging property, confinement in cells of different sizes yields scaling of cytoplasmic flows. Thus, flows behave like a cell geometry sensor: astral microtubules approach the boundary causing flow velocity changes, which then affect the velocity of chromosome separation, thus scaling NER.
Collective quantum enhancement in critical quantum sensing
Critical systems represent a valuable resource in quantum sensing and metrology. Critical quantum sensing (CQS) protocols can be realized using finite-component phase transitions, where criticality arises from the rescaling of system parameters rather than the thermodynamic limit. Here, we show that a collective quantum advantage can be achieved in a multipartite CQS protocol using a chain of parametrically coupled critical resonators in the weak-nonlinearity limit. We derive analytical solutions for the low-energy spectrum of this unconventional quantum many-body system, which is composed of locally critical elements. We then assess the scaling of the quantum Fisher information with respect to fundamental resources. We demonstrate that the coupled chain outperforms an equivalent ensemble of independent critical sensors, achieving quadratic scaling in the number of resonators. Finally, we show that even with finite Kerr nonlinearity or Markovian dissipation, the critical chain retains its advantage, making it relevant for implementing quantum sensors with current microwave superconducting technologies.
Brine management with zero and minimal liquid discharge
Zero liquid discharge (ZLD) and minimal liquid discharge (MLD) are brine management approaches that aim to reduce the environmental impacts of brine discharge and recover water for reuse. ZLD maximizes water recovery and avoids the needs for brine disposal, but is expensive and energy-intensive. MLD (which reduces the brine volume and recovers some water) has been proposed as a practical and cost-effective alternative to ZLD, but brine disposal is needed. In this Review, we examine the concepts, technologies and industrial applications of ZLD and MLD. These brine management strategies have current and potential applications in the desalination, energy, mining and semiconductor industries, all of which produce large volumes of brine. Brine concentration and crystallization in ZLD and MLD often rely on mechanical vapour compression and thermal crystallizers, which are effective but energy-intensive. Novel engineered systems for brine volume reduction and crystallization are under active development to achieve MLD and/or ZLD. These emerging systems, such as membrane distillation, electrodialytic crystallization and solvent extraction desalination, still face challenges to outcompete mechanical vapour compression and thermal crystallizers, underscoring the critical need to maximize the full potential of reverse osmosis to attain ultrahigh water recovery. Brine valorization has potential to partially offset the cost of ZLD and MLD, provided that resource recovery can be integrated into treatment trains economically and in accordance with regulations.
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