A Mott insulator continuously connected to iron pnictide superconductors

Effect of Angiogenesis in Bone Tissue Engineering

The reconstruction of large skeletal defects is still a tricky challenge in orthopedics. The newly formed bone tissue migrates sluggishly from the periphery to the center of the scaffold due to the restrictions of exchange of oxygen and nutrition impotent cells osteogenic differentiation. Angiogenesis plays an important role in bone reconstruction and more and more studies on angiogenesis in bone tissue engineering had been published. Promising advances of angiogenesis in bone tissue engineering by scaffold designs, angiogenic factor delivery, in vivo prevascularization and in vitro prevascularization are discussed in detail. Among all the angiogenesis mode, angiogenic factor delivery is the common methods of angiogenesis in bone tissue engineering and possible research directions in the future.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

Pseudogap and Weak Multifractality in 2D Disordered Mott Charge-Density-Wave Insulator

We investigate electronic states of Se-substituted 1T-TaS₂ by scanning tunneling microscopy/spectroscopy (STM/STS), where superconductivity emerges from the unique Mott-charge-density-wave (Mott-CDW) state. Spatially resolved STS measurements reveal that a pseudogap replaces the Mott gap with the CDW gaps intact. The pseudogap has little correlation with the unit-cell-to-unit-cell variation in the local Se concentration but appears globally. The correlation length of the local density of states (LDOS) is substantially enhanced at the Fermi energy and decays rapidly at high energies. Furthermore, the statistical analysis of LDOS indicates the weak multifractal behavior of the wave functions. These findings suggest a correlated metallic state induced by disorder and provide a new insight into the emerging superconductivity in two-dimensional materials.

Coexistence of Ferromagnetic and Stripe Antiferromagnetic Spin Fluctuations in SrCo_{2}As_{2}

We use inelastic neutron scattering to study energy and wave vector dependence of spin fluctuations in SrCo_{2}As_{2}, derived from SrFe_{2-x}Co_{x}As_{2} iron pnictide superconductors. Our data reveal the coexistence of antiferromagnetic (AF) and ferromagnetic (FM) spin fluctuations at wave vectors Q_{AF}=(1,0) and Q_{FM}=(0,0)/(2,0), respectively. By comparing neutron scattering results with those of dynamic mean field theory calculation and angle-resolved photoemission spectroscopy experiments, we conclude that both AF and FM spin fluctuations in SrCo_{2}As_{2} are closely associated with a flatband of the e_{g} orbitals near the Fermi level, different from the t_{2g} orbitals in superconducting SrFe_{2-x}Co_{x}As_{2}. Therefore, Co substitution in SrFe_{2-x}Co_{x}As_{2} induces a t_{2g} to e_{g} orbital switching, and is responsible for FM spin fluctuations detrimental to the singlet pairing superconductivity.

Intertwined Magnetic and Nematic Orders in Semiconducting KFe_{0.8}Ag_{1.2}Te_{2}

Superconductivity in the iron pnictides emerges from metallic parent compounds exhibiting intertwined stripe-type magnetic order and nematic order, with itinerant electrons suggested to be essential for both. Here we use x-ray and neutron scattering to show that a similar intertwined state is realized in semiconducting KFe_{0.8}Ag_{1.2}Te_{2} (K_{5}Fe_{4}Ag_{6}Te_{10}) without itinerant electrons. We find that Fe atoms in KFe_{0.8}Ag_{1.2}Te_{2} form isolated 2×2 blocks, separated by nonmagnetic Ag atoms. Long-range magnetic order sets in below T_{N}≈35  K, with magnetic moments within the 2×2 Fe blocks ordering into the stripe-type configuration. A nematic order accompanies the magnetic transition, manifest as a structural distortion that breaks the fourfold rotational symmetry of the lattice. The nematic orders in KFe_{0.8}Ag_{1.2}Te_{2} and iron pnictide parent compounds are similar in magnitude and in how they relate to the magnetic order, indicating a common origin. Since KFe_{0.8}Ag_{1.2}Te_{2} is a semiconductor without itinerant electrons, this indicates that local-moment magnetic interactions are integral to its magnetic and nematic orders, and such interactions may play a key role in iron-based superconductivity.

DEMAND, a Dimensional Extreme Magnetic Neutron Diffractometer at the High Flux Isotope Reactor

A two-dimensional (2D) Anger camera detector has been used at the HB-3A four-circle single-crystal neutron diffractometer at the High Flux Isotope Reactor (HFIR) since 2013. The 2D detector has enabled the capabilities of measuring sub-mm crystals and spin density maps, enhanced the efficiency of data collection and phase transition detection, and improved the signal-to-noise ratio. Recently, the HB-3A four-circle diffractometer has been undergoing a detector upgrade towards a much larger area, magnetic-field-insensitive, Anger camera detector. The instrument will become capable of doing single-crystal neutron diffraction under ultra-low temperatures (50 mK), magnetic fields (up to 8 T), electric fields (up to 11 kV/mm), and hydrostatic high pressures (up to 45 GPa). Furthermore, half-polarized neutron diffraction is also available to measure weak ferromagnetism and local site magnetic susceptibilities. With the new high-resolution 2D detector, the four-circle diffractometer has become more powerful for studying magnetic materials under extreme sample environment conditions; hence, it has been given a new name: DEMAND.

Disorder induced power-law gaps in an insulator-metal Mott transition

A correlated material in the vicinity of an insulator-metal transition (IMT) exhibits rich phenomenology and a variety of interesting phases. A common avenue to induce IMTs in Mott insulators is doping, which inevitably leads to disorder. While disorder is well known to create electronic inhomogeneity, recent theoretical studies have indicated that it may play an unexpected and much more profound role in controlling the properties of Mott systems. Theory predicts that disorder might play a role in driving a Mott insulator across an IMT, with the emergent metallic state hosting a power-law suppression of the density of states (with exponent close to 1; V-shaped gap) centered at the Fermi energy. Such V-shaped gaps have been observed in Mott systems, but their origins are as-yet unknown. To investigate this, we use scanning tunneling microscopy and spectroscopy to study isovalent Ru substitutions in Sr3(Ir1-xRux)2O7 (0 ≤ x ≤ 0.5) which drive the system into an antiferromagnetic, metallic state. Our experiments reveal that many core features of the IMT, such as power-law density of states, pinning of the Fermi energy with increasing disorder, and persistence of antiferromagnetism, can be understood as universal features of a disordered Mott system near an IMT and suggest that V-shaped gaps may be an inevitable consequence of disorder in doped Mott insulators.