Experimental Discovery Research Articles

Robust half-metallicity and tunable ferromagnetism in two-dimensional VClI2

Recent theoretical and experimental discoveries of two-dimensional (2D) ferromagnetic (FM) materials have sparked intense interest for their potential applications in spintronics. 2D FM materials with high spin polarization are extremely desirable for future low-dimensional spintronics. Half-metallicity plays a key role in the development of such devices. Here, we reported a new 2D nanomagnet VClI2 using the first-principles based density functional theory calculations. VClI2 shows an exciting half-metallic character with a wide half-metallic gap of 0.4 eV. The ground state favors ferromagnetic coupling with a Curie temperature Tc of 21 K. The half-metallicity with a FM ground state is further achieved by the application of an external strain and by the combined effects of the strain and the electric field. A phase transition from a half-metallic → semiconductor → metal was further observed under different stimuli with an antiferromagnetic ground state. At Ez=7.5 V/nm and in the presence of η=5% strain, the calculated Tc is estimated at 35 K, which shows a 67% increment than the Tc observed in the unstrained condition. The fascinating and unique properties suggest that VClI2 is a promising two-dimensional ferromagnetic half-metal, which can be useful for applications in future memory devices to enrich the 2D magnetic materials library.

On the ac magnetic susceptibility of a room temperature superconductor: anatomy of a probable scientific fraud

In Snider et al. (2020) (Ref. [1]), Snider et al announced the experimental discovery of room temperature superconductivity in a carbonaceous sulfur hydride under high pressure, hereafter called CSH. The paper reported sharp drops in the measured magnetic susceptibility as a function of temperature for five different pressures, that were claimed to be a superior test signaling a superconducting transition. Here I present several arguments indicating that the susceptibility data published in Snider et al. (2020) were probably fraudulent. This calls into question the validity of the entire paper and its claim of detection of room temperature superconductivity. I also describe the roadblocks that I have encountered in reaching this conclusion. A variety of implications of this situation are discussed.

Disease-gene prediction based on preserving structure network embedding.

Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the disease-gene relationship network to predict disease genes better is still an open problem. In this paper, a disease-gene-prediction method based on preserving structure network embedding (PSNE) is introduced. In order to predict pathogenic genes more effectively, a heterogeneous network with multiple types of bio-entities was constructed by integrating disease-gene associations, human protein network, and disease-disease associations. Furthermore, the low-dimension features of nodes extracted from the network were used to reconstruct a new disease-gene heterogeneous network. Compared with other advanced methods, the performance of PSNE has been confirmed more effective in disease-gene prediction. Finally, we applied the PSNE method to predict potential pathogenic genes for age-associated diseases such as AD and PD. We verified the effectiveness of these predicted potential genes by literature verification. Overall, this work provides an effective method for disease-gene prediction, and a series of high-confidence potential pathogenic genes of AD and PD which may be helpful for the experimental discovery of disease genes.

Square-Root Higher-Order Topology in Rectangular-Lattice Acoustic Metamaterials

Square-root topology offers a distinctive scheme towards topological phases with unconventional spectral properties. In many cases, the formation of square-root topology is governed by the inherent lattice symmetry, with honeycomb lattices being prominent examples. Here, we report on the experimental discovery of square-root higher-order topological insulator phases in an acoustic metamaterial with decorated rectangular lattice. Through acoustic pump-probe techniques, we explore the boundary-dependent topological edge and corner states in both the parent and square-root acoustic metamaterials. We further validate their higher-order topologies as well as their spectral connections via various simulations. Our work not only substantiates the square-root higher-order topology in rectangular-lattice systems from the experimental aspect, but also serves as a step towards versatile higher-order topological phenomena in photonic, acoustic, and mechanical metamaterials.

Abiotic Methane Reservoirs in the Western Tianshan HP–UHP Metamorphic Belt, China

AbstractNatural gas, consisting primarily of methane (CH4), has become a major source of clean energy in modern society in many parts of the globe. Recent experimental observations and discoveries of deep‐sourced abiotic CH4 in cold subduction zones indicate the important ability of cold subducted slabs to generate natural gas reservoirs. However, most CH4 flux and reservoirs remain unknown and their potential is overlooked in global carbon flux estimations. Massive abiotic CH4‐rich fluid inclusions (FIs) in garnet and omphacite from ultrahigh‐pressure (UHP) eclogites have been found in the Western Tianshan (WT) UHP metamorphic belt, which provides one ideal case for quantification of abiotic CH4 stored in the cold subducted crust. By two methods, we assess the abiotic CH4 content stored in the Chinese WT HP–UHP metamorphic belt. Our calculations show that at least 113 Mt CH4 is stored in the WT eclogites. We also discuss the implications for CH4 reservoirs in subduction zones worldwide and speculate that the cold subduction zones may represent one of the largest, yet overlooked, sources of abiotic CH4 on Earth, which should not be ignored in the global natural resource and carbon flux estimations.

Capturing the Random Walk with Toys

The early 20th century marked a number of transformational experimental and theoretical discoveries in physics. Among them is one that is often neglected in the introductory physics curriculum, which revolutionized our understanding of the molecular world. Evidence for the thermal motions of atoms was first observed by Perrin in 1909, which had been based on the predictions outlined by Einstein in his doctoral dissertation in 1905. Perrin’s experiment was the first to establish a value for the fundamental constant Avogadro’s number, which is the number of atoms in a mole. The theoretical underpinnings of Einstein’s work are an elegant example of the connection between mechanics and thermodynamics and its application to atomic motion. The experiments of Perrin are easily accessible to undergraduate labs. The conceptual simplicity of the topic and its foundational application to chemical diffusion and thermodynamics advocate strongly for its incorporation into the introductory physics sequence and particularly for students in the health sciences.

Consciousness: Matter or EMF?

Conventional theories of consciousness (ToCs) that assume that the substrate of consciousness is the brain's neuronal matter fail to account for fundamental features of consciousness, such as the binding problem. Field ToC's propose that the substrate of consciousness is the brain's best accounted by some kind of field in the brain. Electromagnetic (EM) ToCs propose that the conscious field is the brain's well-known EM field. EM-ToCs were first proposed only around 20 years ago primarily to account for the experimental discovery that synchronous neuronal firing was the strongest neural correlate of consciousness (NCC). Although EM-ToCs are gaining increasing support, they remain controversial and are often ignored by neurobiologists and philosophers and passed over in most published reviews of consciousness. In this review I examine EM-ToCs against established criteria for distinguishing between ToCs and demonstrate that they outperform all conventional ToCs and provide novel insights into the nature of consciousness as well as a feasible route toward building artificial consciousnesses.

Dysregulation of the PRUNE2/PCA3 genetic axis in human prostate cancer: from experimental discovery to validation in two independent patient cohorts.

We have previously shown that the long non-coding (lnc)RNA prostate cancer associated 3 (PCA3; formerly prostate cancer antigen 3) functions as a trans-dominant negative oncogene by targeting the previously unrecognized prostate cancer suppressor gene PRUNE2 (a homolog of the Drosophila prune gene), thereby forming a functional unit within a unique allelic locus in human cells. Here, we investigated the PCA3/PRUNE2 regulatory axis from early (tumorigenic) to late (biochemical recurrence) genetic events during human prostate cancer progression. The reciprocal PCA3 and PRUNE2 gene expression relationship in paired prostate cancer and adjacent normal prostate was analyzed in two independent retrospective cohorts of clinically annotated cases post-radical prostatectomy: a single-institutional discovery cohort (n=107) and a multi-institutional validation cohort (n=497). We compared the tumor gene expression of PCA3 and PRUNE2 to their corresponding expression in the normal prostate. We also serially examined clinical/pathological variables including time to disease recurrence. We consistently observed increased expression of PCA3 and decreased expression of PRUNE2 in prostate cancer compared with the adjacent normal prostate across all tumor grades and stages. However, there was no association between the relative gene expression levels of PCA3 or PRUNE2 and time to disease recurrence, independent of tumor grades and stages. We concluded that upregulation of the lncRNA PCA3 and targeted downregulation of the protein-coding PRUNE2 gene in prostate cancer could be early (rather than late) molecular events in the progression of human prostate tumorigenesis but are not associated with biochemical recurrence. Further studies of PCA3/PRUNE2 dysregulation are warranted. We received support from the Human Tissue Repository and Tissue Analysis Shared Resource from the Department of Pathology of the University of New Mexico School of Medicine and a pilot award from the University of New Mexico Comprehensive Cancer Center. RP and WA were supported by awards from the Levy-Longenbaugh Donor-Advised Fund and the Prostate Cancer Foundation. EDN reports research fellowship support from the Brazilian National Council for Scientific and Technological Development (CNPq), Brazil, and the Associação Beneficente Alzira Denise Hertzog Silva (ABADHS), Brazil. This work has been funded in part by the NCI Cancer Center Support Grants (CCSG; P30) to the University of New Mexico Comprehensive Cancer Center (CA118100) and the Rutgers Cancer Institute of New Jersey (CA072720).

Engineering Second-Order Corner States in 2D Multiferroics.

The understanding and manipulate of the second-order corner states are central to both fundamental physics and future topotronics applications. Despite the fact that numerous second-order topological insulators (SOTIs) are achieved, the efficient engineering in a given material remains elusive. Here, the emergence of 2D multiferroics SOTIs in SbAs and BP5 monolayers is theoretically demonstrated, and an efficient and straightforward way for engineering the nontrivial corner states by ferroelasticity and ferroelectricity is remarkably proposed. With ferroelectric polarization of SbAs and BP5 monolayers, the nontrivial corner states emerge in the mirror symmetric corners and are perpendicular to orientations of the in-plane spontaneous polarization. And remarkably the spatial distribution of the corner states can be effectively tuned by a ferroelastic switching. At the intermediate states of both ferroelectric and ferroelastic switchings, the corner states disappear. These finding not only combines exotic SOTIs with multiferroics but also pave the way for experimental discovery of 2D tunableSOTIs.

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

Modern computational studies of the glass transition

The physics of the glass transition and amorphous materials continues to attract the attention of a wide research community after decades of effort. Supercooled liquids and glasses have been studied numerically since the advent of molecular dynamics and Monte Carlo simulations, and computer studies have greatly enhanced both experimental discoveries and theoretical developments. In this Review, we provide a modern perspective on this area. We describe the need to go beyond canonical methods when studying the glass transition — a problem that is notoriously difficult in terms of timescales, length scales and physical observables. We summarize recent algorithmic developments to achieve enhanced sampling and faster equilibration by using replica-exchange methods, cluster and swap Monte Carlo algorithms, and other techniques. We then review some major advances afforded by these tools regarding the statistical mechanical description of the liquid-to-glass transition, and the mechanical, vibrational and thermal properties of the glassy solid. Computer simulations may unlock crucial aspects of how a liquid transforms into a glass, but are hampered by rapidly growing relaxation times near the transition. This Review summarizes progress towards overcoming this problem and creating realistic in silico glasses, and discusses what understanding has been enabled.

Capturing 2D van der Waals magnets with high probability for experimental demonstration from materials science literature

Abstract2D van der Waals (vdW) magnets have opened intriguing prospects for next‐generation spintronic nanodevices. Machine learning techniques and density functional theory calculations enable the discovery of 2D vdW magnets to be accelerated; however, current computational frameworks based on these state‐of‐the‐art approaches cannot offer probability analysis on whether a 2D vdW magnet can be experimentally demonstrated. Herein, a new framework can be established to overcome this challenge. Via the framework, 2D vdW magnets with high probability for experimental demonstration are captured from materials science literature. The key to the successful establishment is the introduction of the theory of mutual information. Historical validation of predictions substantiates the high reliability of the framework. For example, half of the 30 2D vdW magnets discovered in the literature published prior to 2017 have been experimentally demonstrated in the subsequent years. This framework has the potential to become a revolutionary force for progressing experimental discovery of 2D vdW magnets.image

Transport properties of GNR-C60 single-molecule devices

The transport properties of a series of double-layered GNR-C60 devices are investigated, revealing deep insights into experimental discoveries, and provides theoretical guidance for optimizing the transport properties of GNR-C60 devices.

Photo-induced defluorination acyl fluoride exchange as a fluorogenic photo-click reaction for photo-affinity labeling.

Photo-click chemistry has emerged as a powerful tool for revolutionizing bioconjugation technologies in pharmacological and various biomimetic applications. However, enriching the photo-click reactions to expand the bioconjugation toolkit remains challenging, especially when focusing on spatiotemporal control endowed by light activation. Herein, we describe a photo-induced defluorination acyl fluoride exchange (photo-DAFEx) as a novel type of photo-click reaction that is mediated through acyl fluorides produced by the photo-defluorination of m-trifluoromethylaniline to covalently conjugate with primary/secondary amines and thiols in an aqueous environment. (TD)-DFT calculations, together with experimental discovery, indicate that the m-NH2PhF2C(sp3)-F bond in the excited triplet state is cleaved by water molecules, which is key to inducing defluorination. Intriguingly, the benzoyl amide linkages built by this photo-click reaction exhibited a satisfactory fluorogenic performance, which allowed visualization of its formation in situ. Accordingly, this photo-controlled covalent strategy was exploited not only for the decoration of small molecules, peptide cyclization and functionalization of proteins in vitro, but also for designing photo-affinity probes targeting endogenous carbonic anhydrase II (hCA-II) in living cells.

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening.

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

Splay and polar order in a system of hard pear-like molecules: confrontation of Monte Carlo numerical simulations with density functional theory calculations.

Recent experimental discoveries of novel nematic types with polar order, including ferroelectric nematic and splay nematic, have brought the resurgence of the interest in polar and modulated phases. One of the most important factors that is widely believed to be crucial for the formation of new phases is the pear-like shape of mesogenic molecules. Such molecules were treated using second-virial density functional theory in [De Gregorio, P et al., Soft Matter, 2016, 12(23), 5188-5198], where the authors showed that the K11 splay elastic constant can become negative due to solely entropic reasons leading to long-range splay and polar correlations. To verify whether the predictions are correct, we performed Monte Carlo simulations of the same hard-core molecules used in the DFT study. As our results suggest, no polar or modulated liquid crystalline phases emerge; polar and splay correlations are either at most short-range or completely absent. On the other hand, a polar ferroelectric splay crystal was observed.

Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.

The reliability of organic molecular crystal structure prediction has improved tremendously in recent years. Crystal structure predictions for small, mostly rigid molecules are quickly becoming routine. Structure predictions for larger, highly flexible molecules are more challenging, but their crystal structures can also now be predicted with increasing rates of success. These advances are ushering in a new era where crystal structure prediction drives the experimental discovery of new solid forms. After briefly discussing the computational methods that enable successful crystal structure prediction, this perspective presents case studies from the literature that demonstrate how state-of-the-art crystal structure prediction can transform how scientists approach problems involving the organic solid state. Applications to pharmaceuticals, porous organic materials, photomechanical crystals, organic semi-conductors, and nuclear magnetic resonance crystallography are included. Finally, efforts to improve our understanding of which predicted crystal structures can actually be produced experimentally and other outstanding challenges are discussed.

Global polarization of hyperons and spin alignment of vector mesons in quark matters

<sec>Relativistic heavy ion collider (RHIC) as a dedicated nuclear facility has made a few major discoveries in physics. This year marks the 30th year STAR Collaboration formation and the 23th year of STAR detector operation and data collection at RHIC. In the last two decades, STAR has collected many datasets, exhibiting scientific versatility and flexibility of the RHIC facility. The total dataset in the first year is less than 1 million good events, and currently there are about 1 billion events per dataset.</sec><sec>The Global Hyperon Polarization was proposed in 2004. This immediately prompted the STAR Collaboration to search for this phenomenon from the early datasets. The null results were presented at Quark Matter Conference in Shanghai in 2006 and subsequently published. Although there were peripheral and continuous efforts in the following decade, no positive result has been observed experimentally. This situation changed in the following decade with the upgrade of high data rate and time-of-flight (TOF) detector and the progress of the Beam Energy Scan Phase I (BES-I). The experimental discoveries of the global polarization of hyperons in 2017 and the spin alignment of vector mesons in 2023 at RHIC-STAR confirm the theory which was established nearly twenty years ago. The theory and these measurements open the way to studying the properties of the hot and dense nuclear matter created in high-energy heavy ion collisions from a new degree of freedom, spin.</sec><sec>We briefly review these discoveries from the proposals of theory to the experimental measurements, and summarize the related measurements at the existing facilities and the theoretical explanations to the original proposal. The basic understanding and the original proposal are still valid and fundamental, that is, the angular momentum of system can transform into a spin effect observable in experiment. However, it appears that in each case a new model is needed to explain the new experimental observation. We need a more basic theory to help us unify all these spin related phenomena.</sec><sec>Over the past five years, STAR has successfully installed 3 new detectors and we have begun to see the physical analysis results from datasets with those new functions. What makes the STAR detector viable after 20 years of operation is its continuous evolution through successful upgrades, with new scientific programs added year by year. The next big thing is to forward upgrade a tracking system (3 layers of silicon strips and 4 layers of sTGC chambers) and a calorimetry system (electromagnetic and hadronic calorimeters). In addition to studying the spin structure of protons by using the polarized proton beams at RHIC, the upgrades also provide a unique ability to investigate the origin of Λ Global Polarization as a function of rapidity and rapidity (de-)correlations in Au+Au collisions.</sec>

Data analytics accelerates the experimental discovery of Cu1−xAgxGaTe2 based thermoelectric chalcogenides with high figure of merit

Employing an artificial intelligence (AI) approach, we have predicted and experimentally synthesized several high-performing p-type thermoelectric (TE) chalcogenides, Cu1−xAgxGaTe2, with the peak TE figure of merit (zT) of ∼1.90 at 770 K and x = 0.45.

Properties and Patterns of Polarized Gravitational Waves

We discuss the polarization of gravitational radiation within the standard framework of linearized general relativity. The recent experimental discovery of gravitational waves provides the impetus to revisit the implications of the spin-rotation-gravity coupling for polarized gravitational radiation; therefore, we consider the coupling of the helicity of gravitational waves to the rotation of an observer or the gravitomagnetic field of a rotating astronomical source. Observational possibilities regarding polarization-dependent effects in connection with future gravitational wave detectors are briefly explored.