Insights into Electrolytic Pre-Lithiation: A Thorough Analysis Using Silicon Thin Film Anodes

Pre-lithiation via electrolysis, herein defined as electrolytic pre-lithiation, using cost-efficient electrolytes based on lithium chloride (LiCl), is successfully demonstrated as a proof-of-concept for enabling lithium-ion battery full-cells with high silicon content negative electrodes. An electrolyte for pre-lithiation based on γ-butyrolactone and LiCl is optimized using boron-containing additives (lithium bis(oxalato)borate, lithium difluoro(oxalate)borate) and CO₂ with respect to the formation of a protective solid electrolyte interphase (SEI) on silicon thin films as model electrodes. Reversible lithiation in Si||Li metal cells is demonstrated with Coulombic efficiencies (C_Eff) of 95–96% for optimized electrolytes comparable to 1 m LiPF₆/EC:EMC 3:7. Formation of an effective SEI is shown by cyclic voltammetry and X-ray photoelectron spectroscopy (XPS). electrolytic pre-lithiation experiments show that notable amounts of the gaseous product Cl₂ dissolve in the electrolyte leading to a self-discharge Cl₂/Cl⁻ shuttle mechanism between the electrodes lowering pre-lithiation efficiency and causing current collector corrosion. However, no significant degradation of the Si active material and the SEI due to contact with elemental chlorine is found by SEM, impedance, and XPS. In NCM111||Si full-cells, the capacity retention in the 100th cycle can be significantly increased from 54% to 78% by electrolytic pre-lithiation, compared to reference cells without pre-lithiation of Si.     

Grain Boundaries in Cu(In,Ga)Se2: A Review of Composition–Electronic Property Relationships by Atom Probe Tomography and Correlative Microscopy

Cu(In,Ga)Se₂ thin-film solar cells have attracted significant research interest in recent decades due to their high efficiency in converting solar energy into electricity for enabling a sustainable future. Although the Cu(In,Ga)Se₂ absorber can be grown as a single crystal, its polycrystalline form is dominating the market not only due to its lower costs, but also due to its unexpectedly higher cell efficiency. However, this absorber contains a high fraction of grain boundaries. These are structural defects where deep-trap states can be localized leading to an increase in recombination activity. This controversy is mirrored in the existing literature studies where two main contradictory believes exist: 1) to be crucial grain boundaries in Cu(In,Ga)Se₂ absorber are anomalous, being benign in terms of cell performance, and 2) grain boundaries are regions characterized by an increased recombination activity leading to deteriorated cell performance. Therefore, the present review tackles this issue from a novel perspective unraveling correlations between chemical composition of grain boundaries and their corresponding electronic properties. It is shown that features such as Cu depletion/In enrichment, segregation of 1-2at.% of alkali dopants, and passivation by a wide-bandgap or type inversion at grain boundaries are crucial ingredients for low open-circuit voltage loss and, hence, for superior cell performance.     

Irisin Protects the Human Placenta from Oxidative Stress and Apoptosis via Activation of the Akt Signaling Pathway

Irisin is a newly discovered exercise-mediated polypeptide hormone. Irisin levels increase during pregnancy however, women with preeclampsia (PE) have significantly lower levels of Irisin compared to women of healthy pregnancies. Even though many studies suggest a role of Irisin in pregnancy, its function in the human placenta is unclear. In the current study, we aimed to understand key roles of Irisin through its ability to protect against apoptosis is the preeclamptic placenta and in ex vivo and in vitro models of hypoxia/re-oxygenation (H/R) injury. Our studies show that Irisin prevents cell death by reducing pro-apoptotic signaling cascades, reducing cleavage of PARP to induce DNA repair pathways and reducing activity of Caspase 3. Irisin caused an increase in the levels of anti-apoptotic BCL2 to pro-apoptotic BAX and reduced ROS levels in an in vitro model of placental ischemia. Furthermore, we show that Irisin treatment acts through the Akt signaling pathway to prevent apoptosis and enhance cell survival. Our findings provide a novel understanding for the anti-apoptotic and pro-survival properties of Irisin in the human placenta under pathological conditions. This work yields new insights into placental development and disease and points towards intervention strategies for placental insufficiencies, such as PE, by protecting and maintaining placental function through inhibiting hypoxic ischemia-induced apoptosis.     

Multimodal Optically Nonlinear Nanoparticles Exhibiting Simultaneous Higher Harmonics Generation and Upconversion Luminescence for Anticounterfeiting and 8-bit Optical Coding

Nonlinear optical materials are essential in areas such as nanophotonics, optical information processing, and biomedical imaging. However, nanomaterials employed for these diverse applications to date are efficient only for one type of nonlinear optical activity. Herein, the first multimodal nonlinear optically active class of nanomaterials based on lanthanide-doped lithium niobate nanoparticles, which simultaneously exhibit unprecedentedly efficient second and third harmonic generation, as well as up-conversion photoluminescence, is reported. These dielectric nanoparticles retain their high nonlinear optical conversion efficiency both as powder and as aqueous colloidal solution. The high stability also allows for the fabrication of optically active biocompatible micron-sized fibers and polymer-based 3D-printable objects, as well as for fingerprint detection. Finally, the first 8-bit coding platform purely based on multimodal nonlinear optical activity originating from different parametric and nonparametric processes is demonstrated, showcasing the technological potential of these materials for both anti-counterfeiting and advanced optical information processing.     

Identification and Affinity Determination of Protein-Antibody and Protein-Aptamer Epitopes by Biosensor-Mass Spectrometry Combination

Analytical methods for molecular characterization of diagnostic or therapeutic targets have recently gained high interest. This review summarizes the combination of mass spectrometry and surface plasmon resonance (SPR) biosensor analysis for identification and affinity determination of protein interactions with antibodies and DNA-aptamers. The binding constant (K_D) of a protein–antibody complex is first determined by immobilizing an antibody or DNA-aptamer on an SPR chip. A proteolytic peptide mixture is then applied to the chip, and following removal of unbound material by washing, the epitope(s) peptide(s) are eluted and identified by MALDI-MS. The SPR-MS combination was applied to a wide range of affinity pairs. Distinct epitope peptides were identified for the cardiac biomarker myoglobin (MG) both from monoclonal and polyclonal antibodies, and binding constants determined for equine and human MG provided molecular assessment of cross immunoreactivities. Mass spectrometric epitope identifications were obtained for linear, as well as for assembled (“conformational”) antibody epitopes, e.g., for the polypeptide chemokine Interleukin-8. Immobilization using protein G substantially improved surface fixation and antibody stabilities for epitope identification and affinity determination. Moreover, epitopes were successfully determined for polyclonal antibodies from biological material, such as from patient antisera upon enzyme replacement therapy of lysosomal diseases. The SPR-MS combination was also successfully applied to identify linear and assembled epitopes for DNA–aptamer interaction complexes of the tumor diagnostic protein C-Met. In summary, the SPR-MS combination has been established as a powerful molecular tool for identification of protein interaction epitopes.     

Measuring spatial variations of weak layer and slab properties with regard to snow slope stability

Spatial variations of weak layer and slab properties are believed to affect snow slope stability. To quantify spatial variability at the slope scale, penetration resistance was measured with a high-resolution snow micro-penetrometer (SMP) in a partly randomized grid pattern. The grid design consisted of 46 SMP measurement locations. In addition, a full snow profile and 20 compression tests as well as a Rutschblock test at the snow profile location were performed within the grid. Fifteen slopes of different aspects were sampled of which 11 could be analysed. Weak layer and slab properties were characterised using non-spatial as well as spatial statistics and results were related to slope stability. The geostatistical analysis revealed that in more than half of the cases a range could be determined. Slab layers tended to have more spatial structure than the weak layer. Though some trends are apparent, firm conclusions on the dependence of slope stability on spatial variations were not possible due to the limited range of snow conditions in the dataset, and the fact that the definition of slope stability remains elusive. Based on our limited data set, we can therefore not specify the conditions when spatial variations of weak layer and slab properties are most relevant for snow slab release.     

A Framework for the Verification of Certifying Computations

Formal verification of complex algorithms is challenging. Verifying their implementations goes beyond the state of the art of current automatic verification tools and usually involves intricate mathematical theorems. Certifying algorithms compute in addition to each output a witness certifying that the output is correct. A checker for such a witness is usually much simpler than the original algorithm—yet it is all the user has to trust. The verification of checkers is feasible with current tools and leads to computations that can be completely trusted. We describe a framework to seamlessly verify certifying computations. We use the automatic verifier VCC for establishing the correctness of the checker and the interactive theorem prover Isabelle/HOL for high-level mathematical properties of algorithms. We demonstrate the effectiveness of our approach by presenting the verification of typical examples of the industrial-level and widespread algorithmic library LEDA.     

Efficient filtering for the Resource-Cost AllDifferent constraint

This paper studies a family of optimization problems where a set of items, each requiring a possibly different amount of resource, must be assigned to different slots for which the price of the resource can vary. The objective is then to assign items such that the overall resource cost is minimized. Such problems arise commonly in domains such as production scheduling in the presence of fluctuating renewable energy costs or variants of the Travelling Salesman Problem. In Constraint Programming, this can be naturally modeled in two ways: (a) with a sum of element constraints; (b) with a MinimumAssignment constraint. Unfortunately the sum of element constraints obtains a weak filtering and the MinimumAssignment constraint does not scale well on large instances. This work proposes a third approach by introducing the ResourceCostAllDifferent constraint and an associated incremental and scalable filtering algorithm, running in \(\mathcal {O}(n \cdot m)\), where n is the number of unbound variables and m is the maximum domain size of unbound variables. Its goal is to compute the total cost in a scalable manner by dealing with the fact that all assignments must be different. We first evaluate the efficiency of the new filtering on a real industrial problem and then on the Product Matrix Travelling Salesman Problem, a special case of the Asymmetric Travelling Salesman Problem. The study shows experimentally that our approach generally outperforms the decomposition and the MinimumAssignment ones for the problems we considered.     

Distributed coordination of emergency medical service for angioplasty patients

In this paper we study the coordination of Emergency Medical Service (EMS) for patients with acute myocardial infarction with ST-segment elevation (STEMI). This is a health problem with high associated mortality. A “golden standard” treatment for STEMI is angioplasty, which requires a catheterization lab and a highly qualified cardiology team. It should be performed as soon as possible since the delay to treatment worsens the patient’s prognosis. The decrease of the delay is achieved by coordination of EMS, which is especially important in the case of multiple simultaneous patients. Nowadays, this process is based on the First-Come-First-Served (FCFS) principle and it heavily depends on human control and phone communication with high proneness to human error and delays. The objective is, therefore, to automate the EMS coordination while minimizing the time from symptom onset to reperfusion and thus to lower the mortality and morbidity resulting from this disease. In this paper, we present a multi-agent decision-support system for the distributed coordination of EMS focusing on urgent out-of-hospital STEMI patients awaiting angioplasty. The system is also applicable to emergency patients of any pathology needing pre-hospital acute medical care and urgent hospital treatment. The assignment of patients to ambulances and angioplasty-enabled hospitals with cardiology teams is performed via a three-level optimization model. At each level, we find a globally efficient solution by a modification of the distributed relaxation method for the assignment problem called the auction algorithm. The efficiency of the proposed model is demonstrated by simulation experiments.