Polylactic acid as biobased binder for the production of 3D printing filaments for Ti6Al4V alloy manufacturing via bound metal deposition

In this paper, a biobased binder mainly composed of polylactic acid (PLA) was developed for the production of Ti6Al4V feedstock suitable for 3D printing via material extrusion. 3D printed samples were debound via solvent and thermal treatments and successfully sintered in reducing atmosphere obtaining dense metallic components. The designed and produced bio-binder is completely eliminated during the debinding processes leading to sintered samples showing a high densification (93–94%), with a microstructure composed of primary alpha phase with segregated beta phase at grain boundaries and having average grain size of 70 μm. 3D printed sintered samples show good mechanical properties (yield strength (σy) = 662 MPa, ultimate tensilte strength (UTS) = 743 MPa, elongation at break (εmax) = 12%, hardness = 5.15 GPa) influenced by the sintering parameters and the presence of some degree of micro-porosity in the final structure

Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids

In this work, the concentration of nine cannabinoids, six neutral cannabinoids (THC, CBD, CBC, CBG, CBN and CBDV) and three acidic cannabinoids (THCA CBGA and CBDA), was used to identify the Italian retailers of Cannabis sativa L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatography/high-resolution mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis with the aim to identify the hemp retailers without any other information on the hemp samples like Cannabis strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. To evaluate the real efficiency of the discrimination among the four hemp retailers a partial least squares discriminant analysis (PLS-DA) was applied. The PLS-DA results showed a very good discrimination between the four hemp retailers with an explained variance of 100% and low classification errors in both calibration (5%) and cross validation (6%). A total of 92% of the hemp samples were correctly classified by the cannabinoid variables in both fitting and cross validation. This work contributed to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes

Hybrid cellulose–Basalt polypropylene composites with enhanced compatibility. The role of coupling agent

This study deals with the development and optimization of hybrid composites integrating microcrystalline cellulose and short basalt fibers in a polypropylene (PP) matrix to maximize the mechanical properties of resulting composites. To this aim, the effects of two different coupling agents, endowed with maleic anhydride (MA-g(grafted)-PP) and acrylic acid (AA-g-PP) functionalities, on the composite properties were investigated as a function of their amount. Tensile, flexural, impact and heat deflection temperature tests highlighted the lower reactivity and effectiveness of AA-g-PP, regardless of reinforcement type. Hybrid formulations with basalt/cellulose (15/15) and with 5 wt. % of MA-g-PP displayed remarkable increases in tensile strength and modulus, flexural strength and modulus, and notched Charpy impact strength, of 45% and 284%, 97% and 263%, and 13%, in comparison with neat PP, respectively. At the same time, the thermo-mechanical stability was enhanced by 65% compared to neat PP. The results of this study, if compared with the ones available in the literature, reveal the ability of such a combination of reinforcements to provide materials suitable for automotive applications with environmental benefits

A New Geographic Information System (GIS) Tool for Hydrogen Value Chain Planning Optimization: Application to Italian Highways

Optimizing the hydrogen value chain is essential to ensure hydrogen market uptake in replacing traditional fossil fuel energy and to achieve energy system decarbonization in the next years. The design of new plants and infrastructures will be the first step. However, wrong decisions would result in temporal, economic losses and, in the worst case, failures. Because huge investments are expected, decision makers have to be assisted for its success. Because no tools are available for the optimum design and geographical location of power to gas (P2G) and power to hydrogen (P2H) plants, the geographic information system (GIS) and mathematical optimization approaches were combined into a new tool developed by CNR-ITAE and the University of Bologna in the SuperP2G project, aiming to support the interested stakeholders in the investigation and selection of the optimum size, location, and operations of P2H and P2G industrial plants while minimizing the levelized cost of hydrogen (LCOH). In the present study, the tool has been applied to hydrogen mobility, specifically to investigate the conversion of the existing refuelling stations on Italian highways to hydrogen refuelling stations (HRSs). Middle-term (2030) and long-term (2050) scenarios were investigated. In 2030, a potential demand of between 7000 and 10,000 tons/year was estimated in Italy, increasing to between 32,600 and 72,500 tons/year in 2050. The optimum P2H plant configuration to supply the HRS was calculated in different scenarios. Despite the optimization, even if the levelized cost of hydrogen (LCOH) reduces from 7.0-7.5 euro/kg in 2030 to 5.6-6.2 euro/kg in 2050, the results demonstrate that the replacement of the traditional fuels, i.e., gasoline, diesel, and liquefied petroleum gases (LPGs), will be disadvantaged without incentives or any other economic supporting schemes

Experimental characterization and numerical modelling of the impact behavior of PVC foams

Background Polyvinyl chloride (PVC) foams are widely used in crashworthiness and energy absorption applications due to their low density and the capability of crushing up to large deformations with limited loads. This property is due to their particular constitutive behavior: the stress-strain curve is characterized, after an initial yield or peak stress, by a relevant plateau region followed by a steep increase due to foam densification. Furthermore, the mechanical response of PVC foam is strongly strain rate dependent. Objective This work aims to characterize the mechanical behavior of PVC foams and to develop a complete constitutive model for impact and energy absorption applications. Methods Compressive tests are carried out at different speeds on PVC foam samples having different relative densities. Quasi-static and intermediate strain rate tests are performed by a pneumatic machine, while high strain rate tests are conducted by means of a Split Hopkinson Pressure Bar. The uniaxial stress-strain curves are used to calibrate the visco-elastic and visco-plastic constitutive model. In particular, the material behavior is divided into two parallel branches: the former describes the elasto-plastic behavior, while the latter accounts for the visco-elastic one; the plastic branch also includes a multiplicative term accounting for the strain rate sensitivity of the base material. Results The tests highlight a strong compressibility of the foam with negligible lateral expansion. The energy absorption efficiency, as well as the densification strain, is evaluated. The material model is also implemented in Finite Element (FE) simulations of puncture impact tests, validating the results of the calibration procedure. Conclusions The calibration of the visco-elasto-plastic material model offers a physically consistent identification of the constitutive response of the PVC foams, showing an effective characterization of the impact behavior of the material

PathVisio Analysis: An Application Targeting the miRNA Network Associated with the p53 Signaling Pathway in Osteosarcoma

MicroRNAs (miRNAs) are small single-stranded, non-coding RNA molecules involved in the pathogenesis and progression of cancer, including osteosarcoma. We aimed to clarify the pathways involving miRNAs using new bioinformatics tools. We applied WikiPathways and PathVisio, two open-source platforms, to analyze miRNAs in osteosarcoma using miRTar and ONCO.IO as integration tools. We found 1298 records of osteosarcoma papers associated with the word "miRNA". In osteosarcoma patients with good response to chemotherapy, miR-92a, miR- 99b, miR-193a-5p, and miR-422a expression is increased, while miR-132 is decreased. All identified miRNAs seem to be centered on the TP53 network. This is the first application of PathVisio to determine miRNA pathways in osteosarcoma. MiRNAs have the potential to become a useful diagnostic and prognostic tool in the management of osteosarcoma. PathVisio is a full pathway editor with the potentiality to illustrate the biological events, augment graphical elements, and elucidate all the physical structures and interactions with standard external database identifiers

Triple positive breast cancer. A distinct subtype?

Breast cancer is a heterogeneous disease, and within the HER-2 positive subtype this is highly exemplified by the presence of substantial phenotypical and clinical heterogeneity, mostly related to hormonal receptor (HR) expression. It is well known how HER-2 positivity is commonly associated with a more aggressive tumor phenotype and decreased overall survival and, moreover, with a reduced benefit from endocrine treatment. Preclinical studies corroborate the role played by functional crosstalks between HER-2 and estrogen receptor (ER) signaling in endocrine resistance and, more recently, the activation of ER signaling is emerging as a possible mechanism of resistance to HER-2 blocking agents. Indeed, HER-2 positive breast cancer heterogeneity has been suggested to underlie the variability of response not only to endocrine treatments, but also to HER-2 blocking agents. Among HER-2 positive tumors, HR status probably defines two distinct subtypes, with dissimilar clinical behavior and different sensitivity to anticancer agents. The triple positive subtype, namely, ER/PgR/Her-2 positive tumors, could be considered the subset which most closely resembles the HER-2 negative/HR positive tumors, with substantial differences in biology and clinical outcome. We argue on whether in this subgroup the "standard" treatment may be considered, in selected cases, i.e., small tumors, low tumor burden, high expression of both hormonal receptors, an overtreatment. This article review the existing literature on biologic and clinical data concerning the HER-2/ER/PgR positive tumors, in an attempt to better define the HER-2 subtypes and to optimize the use of HER-2 targeted agents, chemotherapy and endocrine treatments in the various subsets

Gamma distribution function to understand anaerobic digestion kinetics: Kinetic constants are not constant

The Gamma model is a novel approach to characterise the complex degradation dynamics taking place during anaerobic digestion. This three parameters model results from combining the first-order kinetic model and the Gamma distribution function. In contrast to conventional models, where the kinetic constant is considered invariant, the Gamma model allows analysing the variability of the kinetic constant using a probability density function. The kinetic constant of mono-digestion and co-digestion batch tests of different wastes were modelled using the Gamma model and two common first-order models: one-step one-fraction model and one-step two-fraction model. The Gamma distribution function approximates three distinct probability density functions, i.e. exponential, log-normal, and delta Dirac. Specifically, (i) cattle paunch and pig manure approximated a log-normal distribution; (ii) cattle manure and microalgae approximated an exponential distribution, and (iii) primary sludge and cellulose approximated a delta Dirac distribution. The Gamma model was able to characterise two distinct waste activated sludge, one approximated to a log-normal distribution and the other to an exponential distribution. The same cellulose was tested with two different inocula; in both tests, the Gamma distribution function approximated a delta Dirac function but with a different kinetic value. The potential and consistency of Gamma model were also evident when analysing pig manure and microalgae co-digestion batch tests since (i) the mean k of the co-digestion tests were within the values of the mono-digestion tests, and (ii) the profile of the density function transitioned from log-normal to exponential distribution as the percentage of microalgae in the mixture increased

The role of two families of bacterial enzymes in putrescine synthesis from agmatine via agmatine deiminase

Putrescine, one of the main biogenic amines associated to microbial food spoilage, can be formed by bacteriafrom arginine via ornithine decarboxylase (ODC), or from agmatine via agmatine deiminase (AgDI). This study aims to correlate putrescine production from agmatine to the pathway involving N-carbamoylputrescine formation via AdDI (the aguAproduct) and N-carbamoylputrescine amidohydrolase (the aguB product), or putrescine carbamoyltransferase (the ptcA product) in bacteria. PCR methods were developed to detect the two genes involved in putrescine production from agmatine.Putrescine production from agmatine could be linked to the  aguA and  ptcA genes in  Lactobacillus hilgardii X1B,Enterococcus faecalis ATCC 11700, and Bacillus cereus ATCC 14579. By contrast Lactobacillus sakei 23K was unable toproduce putrescine, and although a fragment of DNA corresponding to the gene aguA was amplified, no amplification wasobserved for the ptcA gene. Pseudomonasaeruginosa PAO1 produces putrescine and is reported to harbour aguA and aguBgenes, responsible for agmatine deiminase and N-carbamoylputrescine amidohydrolase activities. The enzyme from P. aeruginosa PAO1 that converts N-carbamoylputrescine to putrescine (the aguB product) is different from other microorganismsstudied (the ptcA product). Therefore, the aguB gene from P. aeruginosa PAO1 could not be amplified with ptcA specificprimers. The aguB and ptcA genes have frequently been erroneously annotated in the past, as in fact these two enzymes areneither homologous nor analogous. Furthermore, the aguA, aguB and ptcA sequences available from GenBank were subjected to phylogenetic analysis, revealing that gram-positive bacteria harboured ptcA, whereas gram-negative bacteria harbouraguB. This paper also discusses the role of the agmatine deiminase system (AgDS) in acid stress resistance.&nbsp

A physical application of Kerr-Schild groups

The present work deals with the search of useful physical applications of some generalized groups of metric transformations. We put forward different proposals and focus our attention on the implementation of one of them. Particularly, the results show how one can control very efficiently the kind of spacetimes related by a Generalized Kerr-Schild (GKS) Ansatz through Kerr-Schild groups. Finally a preliminar study regarding other generalized groups of metric transformations is undertaken which is aimed at giving some hints in new Ans\"atze to finding useful solutions to Einstein's equations.Comment: 18 page