Ultrafast laser-induced plasma anisotropy in pristine and surface pre-structured zinc telluride, probed by terahertz pulses.

We use THz probe pulses to detect and analyze the dynamics of charge transport anisotropies generated by ultrafast laser two-photon absorption in Zinc Telluride (ZnTe) semi-insulating crystal showing smooth and laser structured surfaces. The detected anisotropy consists in a modulation of the THz transmission as a function of the orientation of the <001 > axis of ZnTe. The change in THz transmission after pump excitation is attributed to free carrier absorption of the THz field in the laser-induced electron-hole plasma. Pre-structuring the surface sample with laser-induced periodic surface structures (ripples) has strong influence on free carrier THz transmission and its associated anisotropic oscillation. Within the relaxation dynamics of the laser-induced free carriers, two relaxation times have to be considered in order to correctly describe the dynamics, a fast relaxation, of about 50 picoseconds in pristine sample (90 picoseconds in sample pre-structured with ripples), and a slow one, of about 1.5 nanoseconds. A theoretical model based on classical Drude theory and on the dependence of the two-photon absorption coefficient with the crystal orientation and with the laser polarization is used to fit the experimental results.

Accurate unsupervised estimation of aberrations in digital holographic microscopy for improved quantitative reconstruction.

In the context of digital in-line holographic microscopy, we describe an unsupervised methodology to estimate the aberrations of an optical microscopy system from a single hologram. The method is based on the Inverse Problems Approach reconstructions of holograms of spherical objects. The forward model is based on a Lorenz-Mie model distorted by optical aberrations described by Zernike polynomials. This methodology is thus able to characterize most varying aberrations in the field of view in order to take them into account to improve the reconstruction of any sample. We show that this approach increases the repeatability and quantitativity of the reconstructions in both simulations and experimental data. We use the Cramér-Rao lower bounds to study the accuracy of the reconstructions. Finally, we demonstrate the efficiency of this aberration calibration with image reconstructions using a phase retrieval algorithm as well as a regularized inverse problems algorithm.

Automatic numerical focus plane estimation in digital holographic microscopy using calibration beads.

We present a new method to achieve autofocus in digital holographic microscopy. The method is based on inserting calibrated objects into a sample placed on a slide. Reconstructing a hologram using the inverse problems approach makes it possible to precisely locate and measure the inserted objects and thereby derive the slide plane location. Numerical focusing can then be performed in a plane at any chosen distance from the slide plane of the sample in a reproducible manner and independently of the diversity of the objects in the sample.

Lutte contre les microplastiques : un objectif de santé publique pour les médecins généralistes ?

Plastic production is overwhelming, worldspread (around 300 millions tons a year) and liable to triple by 2050. Science is currently trying to assess the environmental impact of microplastics: particles that are smaller than 5 mm and end up in oceans, invading thus the marine ecosystems. By 2025, 250 millions tons of accumulated plastic waste are expected to be found in the oceans, althought these oceans provide food, well-being and therapeutics for human beings. Health actors are thus enticed to study with more depth and attention potentials risks of toxicity (additives, contaminants, etc.), sources of microplastics, and the becoming in human body of the thinnest particles (nanoplastics).General practionners could use their public health skills by staying alert and operating a preventive action in the Community (through communication, coordination and cooperation amongst local institutions, eg. school) to use plastics with more relevance. Versatility and multiple practicing (eg. Multidisciplinary group practice, well-followed recommandations…) as well as the maping of territorial networks bring hope for a diffused and assessable action, under control of health authorities.

[Could general practitioners play a part in the public health concern on fighting microplastics?] / Lutte contre les microplastiques : un objectif de santé publique pour les médecins généralistes ?

Plastic production is overwhelming, worldspread (around 300 millions tons a year) and liable to triple by 2050. Science is currently trying to assess the environmental impact of microplastics: particles that are smaller than 5 mm and end up in oceans, invading thus the marine ecosystems. By 2025, 250 millions tons of accumulated plastic waste are expected to be found in the oceans, althought these oceans provide food, well-being and therapeutics for human beings. Health actors are thus enticed to study with more depth and attention potentials risks of toxicity (additives, contaminants, etc.), sources of microplastics, and the becoming in human body of the thinnest particles (nanoplastics).General practionners could use their public health skills by staying alert and operating a preventive action in the Community (through communication, coordination and cooperation amongst local institutions, eg. school) to use plastics with more relevance. Versatility and multiple practicing (eg. Multidisciplinary group practice, well-followed recommandations…) as well as the maping of territorial networks bring hope for a diffused and assessable action, under control of health authorities.

Regularized reconstruction of absorbing and phase objects from a single in-line hologram, application to fluid mechanics and micro-biology.

Reconstruction of phase objects is a central problem in digital holography, whose various applications include microscopy, biomedical imaging, and fluid mechanics. Starting from a single in-line hologram, there is no direct way to recover the phase of the diffracted wave in the hologram plane. The reconstruction of absorbing and phase objects therefore requires the inversion of the non-linear hologram formation model. We propose a regularized reconstruction method that includes several physically-grounded constraints such as bounds on transmittance values, maximum/minimum phase, spatial smoothness or the absence of any object in parts of the field of view. To solve the non-convex and non-smooth optimization problem induced by our modeling, a variable splitting strategy is applied and the closed-form solution of the sub-problem (the so-called proximal operator) is derived. The resulting algorithm is efficient and is shown to lead to quantitative phase estimation on reconstructions of accurate simulations of in-line holograms based on the Mie theory. As our approach is adaptable to several in-line digital holography configurations, we present and discuss the promising results of reconstructions from experimental in-line holograms obtained in two different applications: the tracking of an evaporating droplet (size ∼ 100µm) and the microscopic imaging of bacteria (size ∼ 1µm).

Scattering effects and high-spatial-frequency nanostructures on ultrafast laser irradiated surfaces of zirconium metallic alloys with nano-scaled topographies.

The origin of high-spatial-frequency laser-induced periodic surface structures (HSFL) driven by incident ultrafast laser fields, with their ability to achieve structure resolutions below λ/2, is often obscured by the overlap with regular ripples patterns at quasi-wavelength periodicities. We experimentally demonstrate here employing defined surface topographies that these structures are intrinsically related to surface roughness in the nano-scale domain. Using Zr-based bulk metallic glass (Zr-BMG) and its crystalline alloy (Zr-CA) counterpart formed by thermal annealing from its glassy precursor, we prepared surfaces showing either smooth appearances on thermoplastic BMG or high-density nano-protuberances from randomly distributed embedded nano-crystallites with average sizes below 200 nm on the recrystallized alloy. Upon ultrashort pulse irradiation employing linearly polarized 50 fs, 800 nm laser pulses, the surfaces show a range of nanoscale organized features. The change of topology was then followed under multiple pulse irradiation at fluences around and below the single pulse threshold. While the former material (Zr-BMG) shows a specific high quality arrangement of standard ripples around the laser wavelength, the latter (Zr-CA) demonstrates strong predisposition to form high spatial frequency rippled structures (HSFL). We discuss electromagnetic scenarios assisting their formation based on near-field interaction between particles and field-enhancement leading to structure linear growth. Finite-difference-time-domain simulations outline individual and collective effects of nanoparticles on electromagnetic energy modulation and the feedback processes in the formation of HSFL structures with correlation to regular ripples (LSFL).

Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient's risk factors preoperatively. In practice, a blood count is often performed postoperatively but rarely altering the patient's subsequent management. This study aimed to identify the preoperative variables associated with hemorrhagic risk, enabling the creation of a machine-learning model predictive of transfusion risk after total knee arthroplasty and the need for a complete blood count. HYPOTHESIS: Based on preoperative data, a powerful machine learning predictive model can be constructed to estimate the risk of transfusion after total knee arthroplasty. MATERIAL AND METHODS: This retrospective single-centre study included 774 total knee arthroplasties (TKA) operated between January 2020 and March 2023. Twenty-five preoperative variables were integrated into the machine learning model and filtered by a recursive feature elimination algorithm. The most predictive variables were selected and used to construct a gradient-boosting machine algorithm to define the overall postoperative transfusion risk model. Two groups were formed of patients transfused and not transfused after TKA. Odds ratios were determined, and the area under the curve evaluated the model's performance. RESULTS: Of the 774 TKA surgery patients, 100 were transfused postoperatively (12.9%). The machine learning predictive model included five variables: age, body mass index, tranexamic acid administration, preoperative hemoglobin level, and platelet count. The overall performance was good with an area under the curve of 0.97 [95% CI 0.921 - 1], sensitivity of 94.4% [95% CI 91.2 - 97.6], and specificity of 85.4% [95% CI 80.6 - 90.2]. The tool developed to assess the risk of blood transfusion after TKA is available at https://arthrorisk.com. CONCLUSION: The risk of postoperative transfusion after total knee arthroplasty can be predicted by a model that identifies patients at low, moderate, or high risk based on five preoperative variables. This machine learning tool is available on a web platform that is accessible to all, easy to use, and has a high prediction performance. The model aims to limit the need for routine check-ups, depending on the risk presented by the patient. LEVEL OF EVIDENCE: II; diagnostic study.

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can lead to serious complications if not correctly identified. A routine check-up is very often carried out to assess the seric ionogram and kidney function after TKA, that rarely requires clinical intervention in the event of a disturbance. The aim of this study was to identify perioperative variables that would lead to the creation of a machine learning model predicting the risk of kalaemia disorders and/or acute kidney injury after total knee arthroplasty. HYPOTHESIS: A predictive model could be constructed to estimate the risk of kalaemia disorders and/or acute kidney injury after total knee arthroplasty. MATERIAL AND METHODS: This single-centre retrospective study included 774 total knee arthroplasties (TKA) operated on between January 2020 and March 2023. Twenty-five preoperative variables were incorporated into the machine learning model and filtered by a first algorithm. The most predictive variables selected were used to construct a second algorithm to define the overall risk model for postoperative kalaemia and/or acute kidney injury (K+ A). Two groups were formed of K+ A and non-K+ A patients after TKA. A univariate analysis was performed and the performance of the machine learning model was assessed by the area under the curve representing the sensitivity of the model as a function of 1 - specificity. RESULTS: Of the 774 patients included who had undergone TKA surgery, 46 patients (5.9%) had a postoperative kalaemia disorder requiring correction and 13 patients (1.7%) had acute kidney injury, of whom 5 patients (0.6%) received vascular filling. Eight variables were included in the machine learning predictive model, including body mass index, age, presence of diabetes, operative time, lowest mean arterial pressure, Charlson score, smoking and preoperative glomerular filtration rate. Overall performance was good with an area under the curve of 0.979 [CI95% 0.938-1.02], sensitivity was 90.3% [CI95% 86.2-94.4] and specificity 89.7% [CI95% 85.5-93.8]. The tool developed to assess the risk of impaired kalaemia and/or acute kidney injury after TKA is available on https://arthrorisk.com. CONCLUSION: The risk of kalaemia disturbance and postoperative acute kidney injury after total knee arthroplasty could be predicted by a model that identifies low-risk and high-risk patients based on eight pre- and intraoperative variables. This machine learning tool is available on a web platform accessible for everyone, easy to use and has a high predictive performance. The aim of the model was to better identify and anticipate the complications of dyskalaemia and postoperative acute kidney injury in high-risk patients. Further prospective multicentre series are needed to assess the value of a systematic postoperative biochemical work-up in the absence of risk predicted by the model. LEVEL OF EVIDENCE: IV; retrospective study of case series.

Multispectral in-line hologram reconstruction with aberration compensation applied to Gram-stained bacteria microscopy.

In multispectral digital in-line holographic microscopy (DIHM), aberrations of the optical system affect the repeatability of the reconstruction of transmittance, phase and morphology of the objects of interest. Here we address this issue first by model fitting calibration using transparent beads inserted in the sample. This step estimates the aberrations of the optical system as a function of the lateral position in the field of view and at each wavelength. Second, we use a regularized inverse problem approach (IPA) to reconstruct the transmittance and phase of objects of interest. Our method accounts for shift-variant chromatic and geometrical aberrations in the forward model. The multi-wavelength holograms are jointly reconstructed by favouring the colocalization of the object edges. The method is applied to the case of bacteria imaging in Gram-stained blood smears. It shows our methodology evaluates aberrations with good repeatability. This improves the repeatability of the reconstructions and delivers more contrasted spectral signatures in transmittance and phase, which could benefit applications of microscopy, such as the analysis and classification of stained bacteria.

Adsorbed Water Promotes Chemically Active Environments on the Surface of Sodium Chloride.

Gas-particle interfaces are chemically active environments. This study investigates the reactivity of SO2 on NaCl surfaces using advanced experimental and theoretical methods with a NH4Cl substrate also examined for cation effects. Results show that NaCl surfaces rapidly convert to Na2SO4 with a new chlorine component when exposed to SO2 under low humidity. In contrast, NH4Cl surfaces have limited SO2 uptake and do not change significantly. Depth profiles reveal transformed layers and elemental ratios at the crystal surfaces. The chlorine species detected originates from Cl- expelled from the NaCl crystal structure, as determined by atomistic density functional theory calculations. Molecular dynamics simulations highlight the chemically active NaCl surface environment, driven by a strong interfacial electric field and the presence of sub-monolayer water coverage. These findings underscore the chemical activity of salt surfaces and the unexpected chemistry that arises from their interaction with interfacial water, even under very dry conditions.

Super-efficient drilling of metals with ultrafast non diffractive laser beams.

A highly efficient drilling process is found in non-transparent metallic materials enabled by the use of non-diffractive ultrafast Bessel beams. Applied for deep drilling through a 200 µm-thick steel plate, the Bessel beam demonstrates twofold higher drilling efficiency compared to a Gaussian beam of similar fluence and spot size. Notwithstanding that surface ablation occurs with the same efficiency for both beams, the drilling booster results from a self-replication and reconstruction of the beam along the axis, driven by internal reflections within the crater at quasi-grazing incidence, bypassing potential obstacles. The mechanism is the consequence of an oblique wavevectors geometry with low angular dispersion and generates a propagation length beyond the projection range allowed by the geometry of the channel. With only the main lobe being selected by the channel entrance, side-wall reflection determines the refolding of the lobe on the axis, enhancing and replicating the beam multiple times inside the channel. The process is critically assisted by the reduction of particle shielding enabled by the intrinsic self-healing of the Bessel beam. Thus the drilling process is sustained in a way which is uniquely different from that of the conventional Gaussian beam, the latter being damped within its Rayleigh range. These mechanisms are supported and quantified by Finite Difference Time Domain calculations of the beam propagation. The results show key advantages for the quest towards efficient laser drilling and fabrication processes.

Periodic nanoscale structures on polyimide surfaces generated by temporally tailored femtosecond laser pulses.

A strong influence of different pulse durations and double pulse delay times on the formation of periodic surface structures on polyimide were observed employing ultrashort laser pulses tailored on a sub-picosecond and picosecond time scale. Multi-photon, defect-related excitation mechanisms and thermal expansion of the polymer lattice correlated to a loss of long range order and polarisation memory were considered.

Fast Antibiotic Susceptibility Testing via Raman Microspectrometry on Single Bacteria: An MRSA Case Study.

Despite recent advances in molecular diagnostics, ultrafast determination of the antibiotic susceptibility phenotype of pathogenic microorganisms is still a major challenge of in vitro diagnostics (IVD) of infectious diseases. Raman microspectroscopy has been proposed as a means to achieve this goal. Previous studies have shown that susceptibility phenotyping could be done through Raman analysis of microbial cells, either in large clusters or down to the single-cell level in the case of Gram-negative rods. Gram-positive cocci such as Staphylococcus aureus pose several challenges due to their size and their different metabolic and chemical characteristics. Using a tailored automated single-cell Raman spectrometer and a previously proposed sample preparation protocol, we acquired and analyzed 9429 S. aureus single cells belonging to three cefoxitin-resistant strains and two susceptible strains during their incubation in the presence of various concentrations of cefoxitin. We observed an effect on S. aureus spectra that is weaker than what was detected on previous bacteria/drug combinations, with a higher cell-to-cell response variability and an important impact of incubation conditions on the phenotypic resistance of a given strain. Overall, the proposed protocol was able to correlate strains' phenotype with a specific modification of the spectra using majority votes. We, hence, confirm that our previous results on single-cell Raman antibiotic susceptibility testing can be extended to the S. aureus case and further clarify potential limitations and development requirements of this approach in the move toward industrial applications.

Non-Diffractive Bessel Beams for Ultrafast Laser Scanning Platform and Proof-Of-Concept Side-Wall Polishing of Additively Manufactured Parts.

We report the potential use of non-diffractive Bessel beam for ultrafast laser processing in additive manufacturing environments, its integration into a fast scanning platform, and proof-of-concept side-wall polishing of stainless steel-based additively fabricated parts. We demonstrate two key advantages of the zeroth-order Bessel beam: the significantly long non-diffractive length for large tolerance of sample positioning and the unique self-reconstruction property for un-disrupted beam access, despite the obstruction of metallic powders in the additive manufacturing environment. The integration of Bessel beam scanning platform is constructed by finely adapting the Bessel beam into a Galvano scanner. The beam sustained its good profile within the scan field of 35 × 35 mm2. As a proof of concept, the platform showcases its advanced capacity by largely reducing the side-wall surface roughness of an additively as-fabricated workpiece from Ra 10 µm down to 1 µm. Therefore, the demonstrated Bessel-Scanner configuration possesses great potential for integrating in a hybrid additive manufacturing apparatus.

In-situ high-resolution visualization of laser-induced periodic nanostructures driven by optical feedback.

Optical feedback is often evoked in laser-induced periodic nanostructures. Visualizing the coupling between surfaces and light requires highly-resolved imaging methods. We propose in-situ structured-illumination-microscopy to observe ultrafast-laser-induced nanostructures during fabrication on metallic glass surfaces. This resolves the pulse-to-pulse development of periodic structures on a single irradiation site and indicates the optical feedback on surface topographies. Firstly, the quasi-constancy of the ripples pattern and the reinforcement of the surface relief with the same spatial positioning indicates a phase-locking mechanism that stabilizes and amplifies the ordered corrugation. Secondly, on sites with uncorrelated initial corrugation, we observe ripple patterns spatially in-phase. These feedback aspects rely on the electromagnetic interplay between the laser pulse and the surface relief, stabilizing the pattern in period and position. They are critically dependent on the space-time coherence of the exciting pulse. This suggests a modulation of energy according to the topography of the surface with a pattern phase imposed by the driving pulse. A scattering and interference model for ripple formation on surfaces supports the experimental observations. This relies on self-phase-stabilized far-field interaction between surface scattered wavelets and the incoming pulse front.