Motion control for laser machining via reinforcement learning.

Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.

Single-frame 3D lensless microscopic imaging via deep learning.

Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to image pollen grains in 3-dimensions using such a technique could be truly disruptive in the palynology, bioaerosol sensing, and ecology sectors. Here, we show the ability to employ deep learning to generate 3-dimensional images of pollen grains using a series of 2-dimensional images created from 2-dimensional scattering patterns. Using a microscope to obtain 3D Z-stack images of a pollen grain and a 520 nm laser to obtain scattering patterns from the pollen, a single scattering pattern per 3D image was obtained for each position of the pollen grain within the laser beam. In order to create a neural network to transform a single scattering pattern into different 2D images from the Z-stack, additional Z-axis information is required to be added to the scattering pattern. Information was therefore encoded into the scattering pattern image channels, such that the scattering pattern occupied the red channel, and a value indicating the position in the Z-axis occupied the green and blue channels. Following neural network training, 3D images were formed from collated generated 2D images. The volumes of the pollen grains were generated with a mean accuracy of ∼84%. The development of airborne-pollen sensors based on this technique could enable the collection of rich data that would be invaluable to scientists for understanding mechanisms of pollen production climate change and effects on the wider public health.

A light-guiding urinary catheter for the inhibition of Proteus mirabilis biofilm formation.

Catheter-associated urinary tract infection (CAUTI) is a leading cause of hospital-acquired infections worldwide causing debilitating illness for patients as well as a significant financial and treatment burden on health services. CAUTI is linked with the build-up of biofilms on catheter surfaces which act as a reservoir for infection. Additionally, urease-producing bacteria such as Gram-negative Proteus mirabilis (PM), can form crystalline biofilms which encrust catheter surfaces ultimately leading to blockages which require immediate removal of the catheter. Currently there are limited treatments available to prevent the formation of biofilms by PM as well as other urinary tract infection causing bacteria. A novel concept for a light-guiding urinary catheter is presented where a silicone elastomer waveguide incorporated along the length of the catheter is used to irradiate the catheter surfaces with antimicrobial blue light (405 nm) to prevent biofilm formation in situ. The prototype device is mass producible while also easy to fabricate in a lab setting for research studies. The inhibitory effect of blue light on PM biofilm formation over a range of irradiances is described for the first time showing an LD90 at 192-345 J/cm2 and total inhibition at 1,700 J/cm2 In vitro studies show that the light-guiding catheter (LGC) prototypes exhibit a 98% inhibition in PM biofilm formation inside the catheter lumen at an average estimated irradiance of 30-50 mW/cm2 (324-540 J/cm2 fluence) showing that the concept is highly effective, promising to be a powerful and economical antimicrobial approach to prevent catheter associated biofilm development and blockage.

Optimization of flow path parameters for enhanced sensitivity lateral flow devices.

Lateral flow devices (LFDs) or lateral flow tests (LFTs) are one of the most widely used biosensor platforms for point-of-care (POC) diagnostics. The basic LFD design has remained largely unchanged since its first appearance, and this has limited LFD use in clinical applications due to a general lack of analytical sensitivity. We report here a comprehensive study of the use of laser-patterned geometric control barriers that influence the flow dynamics within an LFD, with the specific aim of enhancing LFD sensitivity and lowering the limit of detection (LOD). This control of sample flow produces an increase in the time available for optimizing the binding kinetics of the implemented assay. The geometric modification to the flow path is in the form of a constriction that is produced by depositing a photo-sensitive polymer onto the nitrocellulose membrane which when polymerized, creates impermeable barrier walls through the depth of the membrane. Both the position of the constriction within the flow path and the number of constrictions allow for an increase in the sensitivity because of a slower overall flow rate within the test and a larger volume of sample per unit width of the test line. For these high sensitivity LFDs (HS-LFD), through optimization of the constriction position and addition of a second constriction we attained a 62% increase in test line color intensity for the detection of procalcitonin (PCT) and were also able to lower the LOD from 10 ng/mL to 1 ng/mL. In addition, of relevance for future commercial exploitation, this also significantly decreases the antibody consumption per device leading to reduced costs for test production. We have further tested our HS-LFD with contrived human samples, validating its application for future clinical use.

Deep-Learning-Assisted Focused Ion Beam Nanofabrication.

Focused ion beam (FIB) milling is an important rapid prototyping tool for micro- and nanofabrication and device and materials characterization. It allows for the manufacturing of arbitrary structures in a wide variety of materials, but establishing the process parameters for a given task is a multidimensional optimization challenge, usually addressed through time-consuming, iterative trial-and-error. Here, we show that deep learning from prior experience of manufacturing can predict the postfabrication appearance of structures manufactured by focused ion beam (FIB) milling with >96% accuracy over a range of ion beam parameters, taking account of instrument- and target-specific artifacts. With predictions taking only a few milliseconds, the methodology may be deployed in near real time to expedite optimization and improve reproducibility in FIB processing.

Single step phase optimisation for coherent beam combination using deep learning.

Coherent beam combination of multiple fibres can be used to overcome limitations such as the power handling capability of single fibre configurations. In such a scheme, the focal intensity profile is critically dependent upon the relative phase of each fibre and so precise control over the phase of each fibre channel is essential. Determining the required phase compensations from the focal intensity profile alone (as measured via a camera) is extremely challenging with a large number of fibres as the phase information is obfuscated. Whilst iterative methods exist for phase retrieval, in practice, due to phase noise within a fibre laser amplification system, a single step process with computational time on the scale of milliseconds is needed. Here, we show how a neural network can be used to identify the phases of each fibre from the focal intensity profile, in a single step of ~ 10 ms, for a simulated 3-ring hexagonal close-packed arrangement, containing 19 separate fibres and subsequently how this enables bespoke beam shaping. In addition, we show that deep learning can be used to determine whether a desired intensity profile is physically possible within the simulation. This, coupled with the demonstrated resilience against simulated experimental noise, indicates a strong potential for the application of deep learning for coherent beam combination.

A SARS-CoV-2 nucleocapsid ELISA represents a low-cost alternative to lateral flow testing for community screening in LMI countries.

Background Controlling the spread of SARS-CoV-2 is problematic because of transmission driven by asymptomatic and pre-symptomatic individuals. Community screening can help identify these individuals but is often too expensive for countries with limited health care resources. Low-cost ELISA assays may address this problem, but their use has not yet been widely reported. Methods We developed a SARS-CoV-2 nucleocapsid ELISA and assessed its diagnostic performance on nose and throat swab samples from UK hospitalised patients and sputum samples from patients in Ghana. Results The ELISA had a limit of detection of 8.4 pg/ml antigen and 16 pfu/ml virus. When tested on UK samples (128 positive and 10 negative patients), sensitivity was 58.6% (49.6-67.2) rising to 78.3% (66.7-87.3) if real-time PCR Ct values > 30 were excluded, while specificity was 100% (69.2-100). In a second trial using the Ghanaian samples (121 positive, 96 negative), sensitivity was 52% (42.8-61.2) rising to 72.6% (61.8-81.2) when a > 30 Ct cut-off was applied, while specificity was 100% (96.2-100). Conclusions: Our data show that nucleocapsid ELISAs can test a variety of patient sample types while achieving levels of sensitivity and specificity required for effective community screening. Further investigations into the opportunities that this provides are warranted.

Semi-quantitative detection of inflammatory biomarkers using a laser-patterned multiplexed lateral flow device.

Inflammatory markers including C-reactive protein (CRP) and procalcitonin (PCT) have been shown to be useful biomarkers to improve triage speed and prevent the inappropriate use of antibiotics for infections such as pneumonia. Here, we present a novel and exciting solution to guide the administration of antibiotic treatment via rapid, semi-quantitative and multiplexed detection of CRP and PCT using an advanced lateral flow device (LFD) designed to have multiple parallel flow-paths, produced via the precise laser-based partitioning of the single flow-path of a standard LFD. Each flow-path within this multiplexed LFD has a unique detection capability which permits tailored detection of CRP within a predefined cut-off range (20 µg/mL - 100 µg/mL) and PCT above a pre-defined threshold (0.5 ng/mL). We demonstrate the use of this LFD in the successful detection of CRP and PCT semi-quantitatively within spiked human serum samples. This multiplexed near-patient assay has potential for development into a rapid triage and treatment of patients with suspected pneumonia.

Capillary-based reverse transcriptase loop-mediated isothermal amplification for cost-effective and rapid point-of-care COVID-19 testing.

As the SARS-CoV-2 pandemic continues to spread, the necessity for rapid, easy diagnostic capabilities could never have been more crucial. With this aim in mind, we have developed a cost-effective and time-saving testing methodology/strategy that implements a sensitive reverse transcriptase loop-mediated amplification (RT-LAMP) assay within narrow, commercially available and cheap, glass capillaries for detection of the SARS-CoV-2 viral RNA. The methodology is compatible with widely used laboratory-based molecular testing protocols and currently available infrastructure. It employs a simple rapid extraction protocol that lyses the virus, releasing sufficient genetic material for amplification. This extracted viral RNA is then amplified using a SARS-CoV-2 RT-LAMP kit, at a constant temperature and the resulting amplified product produces a colour change which can be visually interpreted. This testing protocol, in conjunction with the RT-LAMP assay, has a sensitivity of ∼100 viral copies per reaction of a sample and provides results in a little over 30 min. As the assay is carried out in a water bath, commonly available within most testing laboratories, it eliminates the need for specialised instruments and associated skills. In addition, our testing pathway requires a significantly reduced quantity of reagents per test while providing comparable sensitivity and specificity to the RT-LAMP kit used in this study. While the conventional technique requires 25 µl of reagent, our test only utilises less than half the quantity (10 µl). Thus, with its minimalistic approach, this capillary-based assay could be a promising alternative to the conventional testing, owing to the fact that it can be performed in resource-limited settings, using readily available apparatus, and has the potential of increasing the overall testing capacity, while also reducing the burden on supply chains for mass testing.

The future of bone regeneration: integrating AI into tissue engineering.

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically significant level, including positioning predictions with a probability P < 0.001, and therefore can be used as a model to determine the minimum line separation required for cell alignment, with implications for tissue structure development and tissue engineering. The application of a deep neural network, as a model, reduces the amount of experimental cell culture required to develop an enhanced understanding of cell behavior to topographical cues and, critically, provides rapid prediction of the effects of novel surface structures on tissue fabrication and cell signaling.

Laser-patterned paper-based sensors for rapid point-of-care detection and antibiotic-resistance testing of bacterial infections.

Antimicrobial resistance (AMR) has been identified by the World Health Organisation as a global threat that currently claims at least 25,000 deaths each year in Europe and 700,000 globally; the number is projected to reach 10 million per year between 2015 and 2050. Therefore, there is an urgent need for low-cost but reliable point-of-care diagnostics for early screening of infections especially in developing countries lacking in basic infrastructure and trained personnel. This work is aimed at developing such a device, a paper-based microfluidic device for infection testing by an unskilled user in a low resource setting. Here, we present our work relating to the use of our laser-patterned paper-based devices for detection and susceptibility testing of Escherichia coli, via a simple visually observable colour change. The results indicate the suitability of our integrated paper devices for timely identification of bacterial infections at the point-of-care and their usefulness in providing a hugely beneficial pathway for accurate antibiotic prescribing and thus a novel route to tackling the global challenge of AMR.

Real-time particle pollution sensing using machine learning.

Particle pollution is a global health challenge that is linked to around three million premature deaths per year. There is therefore great interest in the development of sensors capable of precisely quantifying both the number and type of particles. Here, we demonstrate an approach that leverages machine learning in order to identify particulates directly from their scattering patterns. We show the capability for producing a 2D sample map of spherical particles present on a coverslip, and also demonstrate real-time identification of a range of particles including those from diesel combustion.

Rapid Multiplexed Detection on Lateral-Flow Devices Using a Laser Direct-Write Technique.

Paper-based lateral flow devices (LFDs) are regarded as ideal low-cost diagnostic solutions for point-of-care (POC) scenarios that allow rapid detection of a single analyte within a fluidic sample, and have been in common use for a decade. In recent years, there has been an increasing need for rapid and simultaneous detection of multiple analytes present within a single sample and to facilitate this, we report here a novel solution-detection using a multi-path LFD created via the precise partitioning of the single flow-path of a standard LFD using our previously reported laser direct-write (LDW) technique. The multiple flow-paths allow the simultaneous detection of the different analytes individually within each of the parallel channels without any cross-reactivity. The appearance of coloured test lines in individual channels indicates the presence of the different analytes within a sample. We successfully present the use of a LDW-patterned multi-path LFD for multiplexed detection of a biomarker panel comprising C-reactive protein (CRP) and Serum amyloid A-1 (SAA1), used for the diagnosis of bacterial infections. Overall, we demonstrate the use of our LDW technique in the creation of a novel LFD that enables multiplexed detection of two inflammation markers within a single LFD providing a detection protocol that is comparatively more efficient than the standard sequential multiplexing procedure.

Predictive capabilities for laser machining via a neural network.

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.

Improved sensitivity and limit-of-detection of lateral flow devices using spatial constrictions of the flow-path.

We report on the use of a laser-direct write (LDW) technique that allows the fabrication of lateral flow devices with enhanced sensitivity and limit of detection. This manufacturing technique comprises the dispensing of a liquid photopolymer at specific regions of a nitrocellulose membrane and its subsequent photopolymerisation to create impermeable walls inside the volume of the membrane. These polymerised structures are intentionally designed to create fluidic channels which are constricted over a specific length that spans the test zone within which the sample interacts with pre-deposited reagents. Experiments were conducted to show how these constrictions alter the fluid flow rate and the test zone area within the constricted channel geometries. The slower flow rate and smaller test zone area result in the increased sensitivity and lowered limit of detection for these devices. We have quantified these via the improved performance of a C-Reactive Protein (CRP) sandwich assay on our lateral flow devices with constricted flow paths which demonstrate an improvement in its sensitivity by 62x and in its limit of detection by 30x when compared to a standard lateral flow CRP device.

Ultrafast multi-layer subtractive patterning.

Subtractive femtosecond laser machining using multiple pulses with different spatial intensity profiles centred on the same position on a sample has been used to fabricate surface relief structuring. A digital micromirror device was used as an intensity spatial light modulator, with a fixed position relative to the sample, to ensure optimal alignment between successive masks. Up to 50 distinct layers, 335 nm lateral spatial resolution and 2.6 µm maximum depth structures were produced. The lateral dimensions of the structures are approximately 40 µm. Surface relief structuring is shown to match intended depth profiles in a nickel substrate, and highly repeatable stitching of identical features in close proximity is also demonstrated.

Single-pulse ablation of multi-depth structures via spatially filtered binary intensity masks.

Digital micromirror devices (DMDs) show great promise for use as intensity spatial light modulators. When used in conjunction with pulsed lasers of a timescale below the DMD pixel switching time, DMDs are generally only used as binary intensity masks (i.e., "on" or "off" intensity for each mask pixel). In this work, we show that by exploiting the numerical aperture of an optical system during the design of binary masks, near-continuous intensity control can be accessed, whilst still maintaining high-precision laser-machining resolution. Complex features with ablation depths up to ∼60 nm, corresponding to grayscale values in bitmap images, are produced in single pulses via ablation with 150 fs laser pulses on nickel substrates, with lateral resolutions of ∼2.5 µm.

Sub-diffraction limit laser ablation via multiple exposures using a digital micromirror device.

We present the use of digital micromirror devices as variable illumination masks for pitch-splitting multiple exposures to laser machine the surfaces of materials. Ultrafast laser pulses of length 150 fs and 800 nm central wavelength were used for the sequential machining of contiguous patterns on the surface of samples in order to build up complex structures with sub-diffraction limit features. Machined patterns of tens to hundreds of micrometers in lateral dimensions with feature separations as low as 270 nm were produced in electroless nickel on an optical setup diffraction limited to 727 nm, showing a reduction factor below the Abbe diffraction limit of ∼2.7×. This was compared to similar patterns in a photoresist optimized for two-photon absorption, which showed a reduction factor of only 2×, demonstrating that multiple exposures via ablation can produce a greater resolution enhancement than via two-photon polymerization.

Pulsed laser deposited diode-pumped 7.4 W Yb:Lu2O3 planar waveguide laser.

Fabrication, characterization, and laser performance of an Yb:Lu2O3 planar waveguide laser are reported. Pulsed laser deposition was employed to grow an 8 µm-thick Yb-doped lutetia waveguide on a YAG substrate. X-ray diffraction was used to determine the crystallinity, and spectroscopic characterization showed the absorption and emission cross-sections were indistinguishable from those reported for bulk material. When end-pumped by a diode-laser bar an output power of 7.4 W was achieved, limited by the available pump power, at a wavelength of 1033 nm and a slope efficiency of 38% with respect to the absorbed pump power.