Browse Articles

Mn–N–C with High‐Density Atomically Dispersed Mn Active Sites for the Oxygen Reduction Reaction

Abstract The utilization of transition metal–based catalysts as alternatives presents an attractive solution for enhancing the sluggish oxygen reduction reaction (ORR) and reducing costly platinum‐based electrocatalysts in hydrogen fuel cells. Manganese‐based nitrogen–carbon (Mn–N–C) is anticipated to exhibit durability due to its weaker Fenton reaction propensity. However, a key obstacle lies in boosting intrinsic electrocatalytic activity and increasing the density of Mn active sites, crucial for practical integration into fuel cell operations. Herein, a three‐step method is developed to synthesize atomically dispersed Mn–N–C materials with a rich mesoporous structure as highly effective ORR catalysts. The high Mn loading (3.42 wt%) promotes the generation of Duo‐MnN 4 active sites, demonstrating outstanding performance and durability for fuel cells. Specifically, the exceptional performance of proton exchange membrane fuel cells (PEMFC) reaches 649 mW cm −2 and anion exchange membrane fuel cells (AEMFC) achieves 770 mW cm −2 . Notably, the durability of the Mn–N–C catalyst in PEMFC is reported for the first time, showing only 18.4% decay after 30 000 square‐wave cycles. This work provides a unique perspective and a systematic design strategy for building feasible nonprecious metal catalysts with a high active site density, addressing the challenges of inefficiency and performance limitations across various electrocatalytic applications.

The oncogene SLC35F2 is a high-specificity transporter for the micronutrients queuine and queuosine

The nucleobase queuine (q) and its nucleoside queuosine (Q) are micronutrients derived from bacteria that are acquired from the gut microbiome and/or diet in humans. Following cellular uptake, Q is incorporated at the wobble base (position 34) of tRNAs that decode histidine, tyrosine, aspartate, and asparagine codons, which is important for efficient translation. Early studies suggested that cytosolic uptake of queuine is mediated by a selective transporter that is regulated by mitogenic signals, but the identity of this transporter has remained elusive. Here, through a cross-species bioinformatic search and genetic validation, we have identified the solute carrier family member SLC35F2 as a unique transporter for both queuine and queuosine in Schizosaccharomyces pombe and Trypanosoma brucei . Furthermore, gene disruption in human HeLa cells revealed that SLC35F2 is the sole transporter for queuosine (K m 174 nM) and a high-affinity transporter for the queuine nucleobase (K m 67 nM), with the additional presence of second low-affinity queuine transporter (K m 259 nM). Ectopic expression of labeled SLC35F2 reveals localization to the cell membrane and Golgi apparatus via immunofluorescence. Competition uptake studies show that SLC35F2 is not a general transporter for other canonical ribonucleobases or ribonucleosides but selectively imports q and Q. The identification of SLC35F2, an oncogene, as the transporter of both q and Q advances our understanding of how intracellular levels of queuine and queuosine are regulated and how their deficiency contributes to a variety of pathophysiological conditions, including neurological disorders and cancer.

Thionated Perylenebisimides as Heavy‐Atom‐Free Triplet Photosensitizers: Intersystem Crossing, Electron Spin Dynamics, and Application in Photodynamic Therapy

Abstract The thionation induced intersystem crossing (ISC) in perylenebisimide (PBI) derivatives was studied. The triplet state lifetimes of the thionated PBIs are substantially shortened (0.12–0.78 µs) as compared to the unsubstituted PBI (ca. 130 µs). Moreover, there is a clear trend of shortening of the triplet excited state lifetimes and the degree of thionation of the carbonyl groups in PBI. These findings rationalize the previous report of the contradictory lower 1 O 2 quantum yields for the PBI derivatives with more carbonyl groups thionated. Time‐resolved electron paramagnetic resonance (TREPR) spectral study shows that the zero field splitting parameters D (1625–1992 MHz) of the lowest triplet states of thionated PBI are larger than that of the pristine PBI chromophore ( D  = 1166 MHz), which is attributed to the influence of sulfur atoms. Interestingly, the electron spin polarization (ESP) pattern of the TREPR spectra is inverted at longer delay time. Importantly we observed ISC for the dianion of the thionated PBI derivatives, and photoexcited dianion species can be used as super strong reductant for photocatalysis with the advantage of long excited state lifetimes (18–85 µs), and the excited state oxidation potentials are −1.47 to −1.77 V (versus Fc/Fc + ).

Daily briefing: How to make America healthy — the real problems and how to fix them

Gut sulfide metabolism modulates behavior and brain bioenergetics

The host–microbiome interface is rich in metabolite exchanges and exquisitely sensitive to diet. Hydrogen sulfide (H 2 S) is present at high concentrations at this interface and is a product of both microbial and host metabolism. The mitochondrial enzyme, sulfide quinone oxidoreductase (SQOR), couples H 2 S detoxification to oxidative phosphorylation; its inherited deficiency presents as Leigh disease. Since an estimated two-thirds of systemic H 2 S metabolism originates in the gut, it raises questions as to whether impaired sulfide clearance in this compartment contributes to disease and whether it can be modulated by dietary sulfur content. In this study, we report that SQOR deficiency confined to murine intestinal epithelial cells perturbs colon bioenergetics that is reversed by antibiotics, revealing a significant local contribution of microbial H 2 S to host physiology. We also find that a 2.5-fold higher methionine intake, mimicking the difference between animal and plant proteins, synergizes with intestinal SQOR deficiency to adversely impact colon architecture and alter microbiome composition. In serum, increased thiosulfate, a biomarker of H 2 S oxidation, reveals that intestinal SQOR deficiency combined with higher dietary methionine affects sulfide metabolism globally and perturbs energy metabolism as indicated by higher ketone bodies. The mice exhibit lower exploratory locomotor activity while brain MRI reveals an atypical reduction in ventricular volume, which is associated with lower aquaporin 1 that is important for cerebrospinal fluid secretion. Our study reveals the dynamic interaction between dietary sulfur intake and sulfide metabolism at the host–microbe interface, impacting gut health, and the potential for lower dietary methionine intake to modulate pathology.

Terrestrial locomotion of microscopic robots enabled by 3D nanomembranes with nonreciprocal shape morphing

Microscopic robots exhibit efficient locomotion in liquids by leveraging fluid dynamics and chemical reactions to generate force asymmetry, thereby enabling critical applications in photonics and biomedicine. However, achieving controllable locomotion of such robots on terrestrial surfaces remains challenging because fluctuating adhesion on nonideal surfaces disrupts the necessary asymmetry for propulsion. Here, we present a microscopic robot composed of three-dimensional nanomembranes, which navigate diverse terrestrial surfaces with omnidirectional motion. We propose a general mechanism employing nonreciprocal shape morphing to generate stable asymmetric forces on surfaces. This nonreciprocal shape morphing is realized through a laser-actuated vanadium dioxide nanomembrane, leveraging the material's inherent hysteresis properties. We demonstrate that these robots can be fabricated in various shapes, ranging from simple square structures to bioinspired "bipedal" helical designs, enabling them to directionally navigate challenging surfaces such as paper, leaves, sand, and vertical walls. Furthermore, their omnidirectional motion facilitates applications in microassembly and microelectronic circuit integration. Additionally, we developed an artificial intelligence control algorithm based on reinforcement learning, enabling these robots to autonomously follow complex trajectories, such as tracing the phrase "hello world". Our study lays a theoretical and technological foundation for microscopic robots with terrestrial locomotion and paves a way for microscopic robots capable of operating on surfaces for advanced nanophotonic, microelectronic, and biomedical applications.

Meticulous Design of High‐Polarity Interface Material for FACsPbI ₃ Perovskite Solar Cells with Efficiency of 26.47%

Abstract Designing new interface materials with the multifunctions of upper film crystallization control, interfacial defects passivation, and interfacial energy level regulation is crucial for developing efficient and stable perovskite solar cells (PSCs). Herein, a high polarity interfacial material, 2‐cyano‐ N,N,N ‐trimethylammonium bromide (CNCB), was synthesized to engineer the buried interface between SnO 2 and perovskite of the PSCs. Comprehensive theoretical and experimental investigations demonstrate that CNCB interacts with perovskite precursors (PbI 2 and FAI) to regulate crystallization kinetics, yielding perovskite films with preferred orientation and reduced defects. Simultaneously, CNCB chemically interacts with both SnO 2 and perovskite surfaces, effectively passivating oxygen vacancies in SnO 2 and undercoordinated Pb 2 ⁺ defects at the perovskite buried surface. Furthermore, the high dipole moment of CNCB induces beneficial interfacial polarization, optimizing energy level alignment and suppressing non‐radiative recombination. The CNCB‐modified FACsPbI 3 PSCs achieve a champion power conversion efficiency (PCE) of 26.47% with exceptional operational stability, retaining 87.14% of their initial efficiency after 1000 h of continuous 1‐sun illumination. This work establishes a molecular design paradigm for multifunctional interfacial materials in perovskite optoelectronics, highlighting the synergistic roles of crystallization control, defect passivation, and dipole engineering in high‐performance devices.

Single antisense oligonucleotides correct diverse splicing mutations in hotspot exons

Mutations that impact splicing play a significant role in disease etiology but are not fully understood. To characterize the impact of exonic variants on splicing in 71 clinically actionable disease genes in asymptomatic people, we analyzed 32,112 exonic mutations from ClinVar and Geisinger MyCode using a minigene reporter assay. We identify 1,733 splice-disrupting mutations, with the most extreme variants likely being deleterious. We report that these variants are not distributed evenly across exons but are mostly concentrated in the ~8% of exons that are most susceptible to splicing mutations (i.e., hotspot exons). We demonstrate how multiple, splice-disrupting mutations in these exons can be reverted by the same ASOs targeting the splice sites of either their upstream or downstream flanking exons. This finding supports the feasibility of developing single therapeutic ASOs that could revert all splice-altering variants localized to a particular exon.

Unnatural Triggers Converted From Tetrazine‐Attached Sialic Acid for Activation of Optoacoustic Imaging‐Guided Cancer Theranostics

Abstract Constructing chemical groups on cell membranes through metabolic glycoengineering of unnatural sugars is an effective means to solve the issue of insufficient or even lack of targets in cancer theranostics. Herein, we address the limitations by developing a tetrazine precursor (SiaTz) based on a non O ‐acetylated sialic acid scaffold and then utilizing it to create unnatural tetrazine triggers on the surface of cancer cells. SiaTz exhibits a good balance between the stability and reaction kinetics under physiological conditions and can be efficiently converted into corresponding tetrazine trigger through bypassing several size‐limiting steps in metabolic glycoengineering process. We also prepare a proof‐of‐concept theranostic combination of a trans ‐cyclooctene derivative (CyTCO) and a thermal‐sensitive drug 2,2′‐azobis[2‐(2‐imidazolin‐2‐yl) propane]‐dihydrochloride (AIPH) to verify the activation function of tetrazine triggers in theranostics of orthotopic and metastatic tumors. In the presence of tetrazine triggers, CyTCO can be activated via bio‐orthogonal reaction to induce optoacoustic signal enhancement, enabling high‐contrast diagnostic imaging and precise tumor localization to guide subsequent treatments. Tetrazine trigger‐activated CyTCO displays high photo‐to‐heat conversion efficiency, which can cause an obvious increase in temperature under laser irradiation and then initiate AIPH decomposition to produce toxic radicals for combined therapy.

Cryo-EM structures of GnRHR: Foundations for next-generation therapeutics

Gonadotropin-releasing hormone receptor (GnRHR) is critical for reproductive health and a key therapeutic target for endocrine disorders and hormone-responsive cancers. Using high-resolution cryoelectron microscopy, we determined the structures of Sus scrofa and Xenopus laevis GnRHRs bound to mammal GnRH, uncovering conserved and species-specific mechanisms of receptor activation and G protein coupling. The conserved “U”-shaped GnRH conformation mediates high-affinity binding through key interactions with residues such as K 3.32 , Y 6.51 , and Y 6.52 . Species-specific variations in extracellular loops and receptor–ligand contacts fine-tune receptor function, while ligand binding induces structural rearrangements, including N terminus displacement and TM6 rotation, critical for signaling. Structure–activity relationship analysis demonstrates how D-amino acid substitutions in GnRH analogs enhance stability and receptor affinity. Distinct binding modes of agonists and antagonists elucidate mechanisms of ligand-dependent activation and inactivation. These insights lay the groundwork for designing next-generation GnRHR therapeutics with enhanced specificity and efficacy for conditions like endometriosis, prostate cancer, and infertility.

Deep mechanism design: Learning social and economic policies for human benefit

Human society is coordinated by mechanisms that control how prices are agreed, taxes are set, and electoral votes are tallied. The design of robust and effective mechanisms for human benefit is a core problem in the social, economic, and political sciences. Here, we discuss the recent application of modern tools from AI research, including deep neural networks trained with reinforcement learning (RL), to create more desirable mechanisms for people. We review the application of machine learning to design effective auctions, learn optimal tax policies, and discover redistribution policies that win the popular vote among human users. We discuss the challenge of accurately modeling human preferences and the problem of aligning a mechanism to the wishes of a potentially diverse group. We highlight the importance of ensuring that research into “deep mechanism design” is conducted safely and ethically.

Desulfurizative Fluorination of <i>N</i> ‐CF ₃ Thioformamides for the Efficient Synthesis of <i>N</i> (CF ₃ )(CF ₂ H) Amines with Enhanced Stability

Abstract With poor metabolic stability being a major cause of failure in drug development, there is a pressing need for strategic molecular modifications to optimize for desired properties and function. N ‐substitution has emerged as a powerful approach, with N ‐CF 3 amines previously demonstrating enhanced lipophilicity and reduced susceptibility to oxidation, albeit inherent instability to hydrolysis. This report discloses the further evolution of this motif—the introduction of an additional N ‐difluoromethyl unit, resulting in an extraordinary 2000‐fold increase in stability. We present the first general synthetic strategy for accessing N (CF 3 )(CF 2 H) amines. The method relies on an operationally simple desulfurization–fluorination strategy of N ‐CF 3 thioformamides and is characterized by broad functional group tolerance.

Heterogeneity, reinforcement learning, and chaos in population games

Inspired by the challenges at the intersection of Evolutionary Game Theory and Machine Learning, we investigate a class of discrete-time multiagent reinforcement learning (MARL) dynamics in population/nonatomic congestion games, where agents have diverse beliefs and learn at different rates. These congestion games, a well-studied class of potential games, are characterized by individual agents having negligible effects on system performance, strongly aligned incentives, and well-understood advantageous properties of Nash equilibria. Despite the presence of static Nash equilibria, we demonstrate that MARL dynamics with heterogeneous learning rates can deviate from these equilibria, exhibiting instability and even chaotic behavior and resulting in increased social costs. Remarkably, even within these chaotic regimes, we show that the time-averaged macroscopic behavior converges to exact Nash equilibria, thus linking the microscopic dynamic complexity with traditional equilibrium concepts. By employing dynamical systems techniques, we analyze the interaction between individual-level adaptation and population-level outcomes, paving the way for studying heterogeneous learning dynamics in discrete time across more complex game scenarios.

Mechanistic Insights: Correspondence on “Tuning Co‐Operative Energy Transfer in Copper(I) Complexes Using Two‐Photon Absorbing Diimine‐Based Ligand Sensitizers”

Abstract In a recent communication, Collins and coworkers presented a Cu(I) complex with photocatalytic activity under red light LED conditions, mainly for singlet oxygen‐driven reactions. Guided by steady‐state emission measurements with 800 nm excitation, the authors suggested that the underlying mechanism for the generation of the photoexcited key species is a simultaneous two‐photon absorption via a virtual state. However, such a mechanism requires pulsed laser excitation and cannot compete when a conventional one‐photon excitation is also feasible with the selected excitation wavelength range. Using several spectroscopic techniques and reactivity assays under different light color and intensity conditions, we unambiguously demonstrate that a conventional one‐photon excitation followed by rather inefficient singlet oxygen generation (quantum yield &lt;5%) is responsible for the observed photoreactivity of the Cu(I) complex. In addition, we briefly summarize general mechanistic considerations, estimate typical photon densities required for a variety of two‐photon mechanisms, highlight the importance of optical filters and impurities to avoid artifacts in the emission spectra, and present some guidelines for the differentiation between one‐ and two‐photon mechanisms.

Integrating Multifunctionalities into a 3D Covalent Organic Framework for Efficient CO ₂ Photoreduction

Abstract Fabrication of highly efficient photocatalysts for CO 2 conversion is still challenging. Herein, integrating nitrogen‐rich organic cages and the photoactive porphyrin moieties together, a 3D covalent organic framework (COF), Cage‐PorCOF, is successfully synthesized. After incorporating metal ions (Co 2+ and Ni 2+ ) into the cage‐based COF, Cage‐PorCOF(Co) and Cage‐PorCOF(Ni) are subsequently constructed for the CO 2 photoreduction. Catalytic experiments show impressive performance in CO 2 photoreduction with CO generation rates of up to 48 748 and 28 446 µmol g −1  h −1 in the first initiating hour for Cage‐PorCOF(Co) and Cage‐PorCOF(Ni), respectively, which is attributed to the synergistic effects from CO 2 ‐affinity of the porous frameworks and incorporated metal atoms, the light‐absorption and charge separation ability of metalloporphyrin groups as well as the fully exposed single‐atomic catalytic sites confirmed by both experimental and theoretical analyses. This study demonstrates that by the integration of multiple functionalities into 3D porous solids, highly effective photocatalysts for CO 2 conversion can be achieved.

Tabula rasa agents display emergent in-group behavior

Theories on group-bias often posit an internal preparedness to bias one’s cognition to favor the in-group (often envisioned as a product of evolution). In contrast, other theories suggest that group-biases can emerge from nonspecialized cognitive processes. These perspectives have historically been difficult to disambiguate given that observed behavior can often be attributed to innate processes, even when groups are experimentally assigned. Here, we use modern techniques from the field of AI that allow us to ask what group biases can be expected from a learning agent that is a pure blank slate without any intrinsic social biases, and whose lifetime of experiences can be tightly controlled. This is possible because deep reinforcement-learning agents learn to convert raw sensory input (i.e. pixels) to reward-driven action, a unique feature among cognitive models. We find that blank slate agents do develop group biases based on arbitrary group differences (i.e. color). We show that the bias develops as a result of familiarity of experience and depends on the visual patterns becoming associated with reward through interaction. The bias artificial agents display is not a static reflection of the bias in their stream of experiences. In this minimal environment, the bias can be overcome given enough positive experiences, although unlearning the bias takes longer than acquiring it. Further, we show how this style of tabula rasa group behavior model can be used to test fine-grained predictions of psychological theories.

Krylov diagonalization of large many-body Hamiltonians on a quantum processor

Isomerisation and Insertion Chemistry of Imidosilanes Enabled by Reversible Si(IV)/Si(II) Redox Shuttling

Abstract Spontaneous redox shuttling at silicon is very rare, primarily reflecting the thermodynamic challenges associated with reduction processes for lighter p ‐block elements. Here we show that the reactions of boryl‐substituted silylene {PhC( t BuN) 2 }Si{B(NDippCH) 2 } with an organo‐azide proceed through a Si(IV)‐Si(II)‐Si(IV) series of redox processes involving both oxidative and reductive ligand migration steps. Each of the isomeric compounds related through this reaction manifold is isolable (and can be structurally characterised by X‐ray crystallography), with the overall free energy profile being close to thermo‐neutral. Broader studies within group 14 imply that this redox shuttling is unique to silicon and that reductive ligand migration also plays a role in O‐atom insertion chemistry.

Enantioselective Abiotic Synthesis of Ribose on Chiral Mesostructured Hydroxyapatite

Abstract The prebiotic synthesis and symmetry breaking of ribose are crucial processes in the origin of life. However, the prebiotic emergence of enantiomerically enriched ribose on primitive Earth remains an unresolved challenge. Herein, we propose that the prebiotic enantioselective synthesis of ribose from glycolaldehyde and glyceraldehyde can be catalyzed by minerals naturally endowed with chiral structure. The chiral mesostructured hydroxyapatite films (CMHAPFs), which consist of lattice‐distorted helical nanorods, can be formed under a hydrothermal condition in the presence of l/d‐ malic acid (MA), a compound that may have been present during the early stages of life's emergence. An enantiomeric excess ( ee ) of 22.5% for d‐ ribose was achieved on the d‐ CMHAPFs formed by d‐ MA. The adsorption conformation of d ‐ribose on the surface of right‐handed hydroxyapatite is more stable than that of l ‐ribose. The different energy barriers for the transition states of ribose enantiomers result in the enantioselective synthesis of ribose, which is attributed to the similar conformation between ribose and the corresponding transition state. Our findings provide valuable insights into the possible role of chiral inorganics in the prebiotic synthesis and symmetry breaking of ribose.

Evolving general cooperation with a Bayesian theory of mind

Theories of the evolution of cooperation through reciprocity explain how unrelated self-interested individuals can accomplish more together than they can on their own. The most prominent theories of reciprocity, such as tit-for-tat or win-stay-lose-shift, are inflexible automata that lack a theory of mind—the human ability to infer the hidden mental states in others’ minds. Here, we develop a model of reciprocity with a theory of mind, the Bayesian Reciprocator. When making decisions, this model does not simply seek to maximize its own payoff. Instead, it also values the payoffs of others—but only to the extent it believes that those others are also cooperating in the same way. To compute its beliefs about others, the Bayesian Reciprocator uses a probabilistic and generative approach to infer the latent preferences, beliefs, and strategies of others through interaction and observation. We evaluate the Bayesian Reciprocator using a generator over games where every interaction is unique, as well as in classic environments such as the iterated prisoner’s dilemma. The Bayesian Reciprocator enables the emergence of both direct-reciprocity when games are repeated and indirect-reciprocity when interactions are one-shot but observable to others. In an evolutionary competition, the Bayesian Reciprocator outcompetes existing automata strategies and sustains cooperation across a larger range of environments and noise settings than prior approaches. This work quantifies the advantage of a theory of mind for cooperation in an evolutionary game theoretic framework and suggests avenues for building artificially intelligent agents with more human-like learning mechanisms that can cooperate across many environments.