Determination of monolayer-protected gold nanoparticle ligand–shell morphology using NMR

It is accepted that the ligand shell morphology of nanoparticles coated with a monolayer of molecules can be partly responsible for important properties such as cell membrane penetration and wetting. When binary mixtures of molecules coat a nanoparticle, they can arrange randomly or separate into domains, for example, forming Janus, patchy or striped particles. To date, there is no straightforward method for the determination of such structures. Here we show that a combination of one-dimensional and two-dimensional NMR can be used to determine the ligand shell structure of a series of particles covered with aliphatic and aromatic ligands of varying composition. This approach is a powerful way to determine the ligand shell structure of patchy particles; it has the limitation of needing a whole series of compositions and ligands' combinations with NMR peaks well separated and whose shifts due to the surrounding environment can be large enough

Final results on neutrino oscillation parameters from the OPERA experiment in the CNGS beam

International audienceThe OPERA experiment has conclusively observed the appearance of tau neutrinos in the muon neutrino CNGS beam. Exploiting the OPERA detector capabilities, it was possible to isolate high purity samples of νe, νμ and ντ charged current weak neutrino interactions, as well as neutral current weak interactions. In this paper, the full dataset is used for the first time to test the three-flavor neutrino oscillation model and to derive constraints on the existence of a light sterile neutrino within the framework of the 3+1 neutrino model. For the first time, tau and electron neutrino appearance channels are jointly used to test the sterile neutrino hypothesis. A significant fraction of the sterile neutrino parameter space allowed by LSND and MiniBooNE experiments is excluded at 90% C.L. In particular, the best-fit values obtained by MiniBooNE combining neutrino and antineutrino data are excluded at 3.3σ significance

Study of charged hadron multiplicities in charged-current neutrino-lead interactions in the OPERA detector (vol 78, 62, 2018)

Erratum to: Eur. Phys. J. C (2018) 78:6

Measurement of the cosmic ray muon flux seasonal variation with the OPERA detector

The OPERA experiment discovered muon neutrino into tau neutrino oscillations in appearance mode, detecting tau leptons by means of nuclear emulsion films. The apparatus was also endowed with electronic detectors with tracking capability, such as scintillator strips and resistive plate chambers. Because of its location, in the underground Gran Sasso laboratory, under 3800 m.w.e., the OPERA detector has also been used as an observatory for TeV muons produced by cosmic rays in the atmosphere. In this paper the measurement of the single muon flux modulation and of its correlation with the seasonal variation of the atmospheric temperature are reported

Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device

In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape cultivars (Cotton Candy (TM), Summer Royal, Allison (TM), and Autumncrisp (R)) were collected during the season to obtain spectral measurements and quality data for developing predictive models based on NIR spectroscopy to be practically used in the vineyard. A SCiO (TM) sensor was used in 2019 for predicting the ripening parameters of Cotton Candy (TM); in particular, total soluble solids (TSS) (R-2 = 0.95; RMSE = 0.60, RPD = 13.13), titratable acidity (R-2 = 0.97; RMSE = 0.40, RPD = 7.31), and pH (R-2 = 0.96; RMSE = 0.07, RPD = 26.06). With these promising results, in the year 2020, the above-mentioned table grape cultivars were all tested for TSS prediction with successful outcomes: Cotton Candy (TM) (R-2 = 0.97; RMSE = 0.68, RPD = 7.48), Summer Royal (R-2 = 0.96; RMSE = 0.83, RPD = 7.13), Allison (TM) (R-2 = 0.97; RMSE = 0.72, RPD = 8.70) and Autumncrisp (R) (R-2 = 0.96; RMSE = 0.60, RPD = 9.73). In conclusion, a rapid and economic sensor such as the SCiO (TM) device can enable a practical application in the vineyard to assess ripening (quality) parameters of table grapes. Thus, this device or similar ones can be also used for a fast sorting and screening of quality throughout the supply chain, from vineyard to cold storage

Salinity Properties Retrieval from Sentinel-2 Satellite Data and Machine Learning Algorithms

The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables from salt-affected soils in a south Mediterranean region using Sentinel-2 multispectral imagery. In order to realize this goal, a test was carried out using electrical conductivity (EC) data collected in central Tunisia. Soil electrical conductivity and leaf electrical conductivity were measured in an olive orchard over two growing seasons and under three irrigation treatments. Firstly, selected spectral salinity, chlorophyll, water, and vegetation indices were tested over the experimental area to estimate both soil and leaf EC using Sentinel-2 imagery on the Google Earth Engine platform. Subsequently, estimation models of soil and leaf EC were calibrated by employing machine learning (ML) techniques using 12 spectral bands of Sentinel-2 images. The prediction accuracy of the EC estimation was assessed by using k-fold cross-validation and computing statistical metrics. The results of the study revealed that machine learning algorithms, together with multispectral data, could advance the mapping and monitoring of soil and leaf electrical conductivity

Gestione idrica e nutrizionale del pomodoro: esperienze applicative in pieno campo e in serra per la definizione di strategie e algoritmi sensor-based

Il monitoraggio dell’umidità e della conducibilità elettrica del suolo/substrato di coltivazione, mediante tecnologie di proximal sensing sempre più affidabili e a basso costo, rappresenta un approccio intuitivo e versatile, con ampie prospettive di applicazione a livello aziendale, per la gestione razionale dell'irrigazione e della fertilizzazione delle colture. Nell'ambito di recenti progetti di ricerca e applicazione dimostrativa di approcci digitali innovativi in agricoltura, sono state progettate e realizzate infrastrutture tecnologiche IoT (reti di sensori e dispositivi per l’analisi dei dati e la relativa attuazione di automatismi), ed elaborati e testati algoritmi per il pilotaggio automatico della fertirrigazione del pomodoro, coltura di interesse strategico per l'orticoltura mediterranea. Prove sperimentali, condotte in condizioni colturali diversificate (pieno campo, serra, coltivazione senza suolo e su terreno, impiego di acque salmastre, utilizzo di materiale genetico di particolare interesse ai fini della valorizzazione dell'agro-biodiversità), hanno dimostrato come le strategie sensor-based, in confronto con metodi empirici basati sull’esperienza dell’agricoltore, contribuiscano a migliorare l'efficienza d'uso delle risorse (acqua e fertilizzanti) e il risultato produttivo e qualitativo della coltura. Con la gestione automatica sensor-based della fertirrigazione, è stato possibile conseguire risparmi idrici e incrementi dell’efficienza d’uso dell’acqua variabili a seconda della tecnica colturale adottata, delle condizioni ambientali, delle varietà di pomodoro in coltivazione, fino a punte del 58% e del 73%, rispettivamente. L’impiego di sensori può facilitare l’applicazione di stress controllati per il miglioramento della qualità del pomodoro. Rispetto all’impiego del timer, ad esempio, è stato possibile aumentare il contenuto di solidi solubili (6,8 vs 7,8 °Brix) e aumentare la percentuale più pregiata della pezzatura delle bacche di pomodoro ciliegino allevato in serra con la tecnica del senza suolo (61% vs 93% di bacche appartenenti alla classe di diametro 25-35 mm). In pieno campo, l’individuazione di appropriate soglie di intervento irriguo mediante sensori rispetto ad un approccio empirico ha consentito di incrementare il valore nutrizionale di pomodoro da trasformazione (61,1 vs 36,1 μg/g di peso secco di licopene)