Mid Sweden University

Cellulose has emerged as a promising sustainable material for advanced applications, including energy harvesting and sensing via triboelectric nanogenerators (TENGs). Despite significant progress in cellulosic TENGs, the role of carbonaceous nanomaterials with different morphologies and surface chemistries remains insufficiently understood. In this study, regenerated cellulose was used as a matrix to incorporate different carbon allotropes, including nanodiamonds, multi-walled carbon nanotubes (MWCNTs), and graphene nanoplatelets, to systematically evaluate their effect on triboelectric performance. Cellulose was dissolved in cold alkaline solution, and composites containing 2.5, 5, 7.5, 10, and 20 wt% of nanoparticles were prepared and tested as tribolayers against PTFE in a TENG configuration. The results showed that graphene-, MWCNT-, and nanodiamond-based composites improved power output by up to 76%, 51%, and 40%, respectively, compared to pure cellulose (34.3 W m−2). Physical and structural analyses, including dielectric spectroscopy, electrical conductivity measurements, scanning electron microscopy (SEM), atomic force microscopy (AFM), X-ray diffraction (XRD), FTIR, and Raman spectroscopy, were employed to investigate the structure-property relationships of regenerated cellulose‑carbon allotrope composites. The findings provide insight into the role of carbon nanomaterials in tuning the triboelectric behavior of regenerated cellulose and offer guidance for the design of high-performance, sustainable functional composites. 

Direction-of-arrival (DoA) estimation plays a critical role in applications of acoustic localization. This work proposes a closed-form algorithm for single-source one-dimensional direction of arrival estimation using the uniform circular array. The algorithm estimates the azimuthal direction of arrival using the least-square estimate of the circular harmonics steering vector. The least-square (LS) estimate of the steering vector is estimated as the principal eigenvector of the received data covariance matrix. The proposed algorithm is tested and compare against the time-frequency circular harmonics beamforming (TF-CHB) and eigenbeam MUSIC (EB-MUSIC) algorithms. The comparison shows that the proposed algorithm outperformed the two benchmark methods in accuracy under various test conditions. 

Harvesting energy from distributed mechanical motions has garnered significance in future power sources for small electronics and sensors. Although technologies like triboelectric nanogenerators have shown promising results, their efficacy hinges on the alignment of motion vectors and device architectures. Here, an approach employing stationary diode cells (DiCes) to generate electricity is presented. This approach leverages dynamically changing electrostatic fields to induce potential differences across diode junctions via electrostatic induction, which is verified theoretically and experimentally. DiCes constructed with multiple diodes can directly output DC voltage and current. A 0.02 m2 sized DiCe contains 360 diodes can supply a DC voltage and current of maximum 490 V and 1.08 mA, respectively, which equals a DC power density of 26.5 W<middle dot>m-2. Capable of functioning in both contact and non-contact modes, DiCes offer versatile applications, from wirelessly powering implanted medical devices to harvesting energy from vehicles and roads.

A system for the measurement of methane concentration in water is presented. The system is a stand- alone device using a high-resolution NDIR (Non-Dispersive Infra-Red) gas sensor. The NDIR sensor is configured to measure methane, water vapor, and carbon dioxide in the air. It is mounted in a housing with a stabilized environment and includes cross-sensitivity compensation. An equilibrator is used to transfer the methane concentration from the water into a circulating gas flow that is analyzed by the NDIR gas sensor. The equilibrator consists of a vertical plastic tube filled with 2,000 glass marbles, where the water runs from top to bottom on the surface of the glass marbles, in contact with a circulating air flow, exchanging gas. The system is stand- alone, including power supply and logging features for 72 hours of operation. The system performance was evaluated in a field test, measuring the methane content of seawater at a fiber bank in Sundsvall, Sweden. This fiber bank consists of remaining waste from an old paper industry from before 1970 and is known to produce methane. The detection limit of the tested system is below 1.4 nmol/L in water, corresponding to 1 ppm methane concentration in the air. The settling time of the system in its current configuration, including the equilibrator and gas sensor housing, is 30 minutes. 

Hydrogen sulfide (H2S) is a highly toxic and corrosive gas commonly found in industrial emissions and natural gas processing, posing serious risks to human health and environmental safety even at low concentrations. The early detection of H2S is therefore critical for preventing accidents and ensuring compliance with safety regulations. This study presents the development of porous ZnO/SnO2-nanocomposite gas sensors tailored for the ultrasensitive detection of H2S at sub-ppb levels. Utilizing a screen-printing method, we fabricated five different sensor compositions—ranging from pure SnO2 to pure ZnO—and characterized their structural and morphological properties through X-ray diffraction (XRD) and scanning electron microscopy (SEM). Among these, the SnO2/ZnO sensor with a composition-weight ratio of 3:4 demonstrated the highest response at 325 °C, achieving a low detection limit of 0.14 ppb. The sensor was evaluated for detecting H2S concentrations ranging from 5 ppb to 500 ppb under dry, humid air and N2 conditions. The relative concentration error was carefully calculated based on analytical sensitivity, confirming the sensor’s precision in measuring gas concentrations. Our findings underscore the significant advantages of mixture nanocomposites in enhancing gas sensitivity, offering promising applications in environmental monitoring and industrial safety. This research paves the way for the advancement of highly effective gas sensors capable of operating under diverse conditions with high accuracy. 

The National Institute for Occupational Safety and Health (NIOSH) has established exposure limits for sulfur-based volatile components, particularly hydrogen sulfide (H2S), at 20 ppm for an 8-hour exposure and 50 ppm for durations under 10 minutes. Detecting such toxic gases at low levels necessitates innovative sensor fabrication. This study introduces a unique sensor design, involving the direct thermophoretic deposition of SnO2 aerosol streams on one side and densely compacted SnO2 thick films via ultrasonic spray pyrolysis (UPS) on the other side, acting as a heater. Analyzing flame-made SnO2 particles using BET, XRD, and TEM techniques revealed highly crystalline particles approximately 8 nm in size. Methyl mercaptan (CH3SH) and H2S were employed as analyte gases, ranging from 20 ppb to 25 ppm and 20 ppb to 50 ppm, respectively. The results indicate that the flame-made SnO2 exhibits significant potential for developing gas sensors that are highly sensitive to CH3SH and H2S gases across a broad concentration range. The sensor demonstrates a linear increase in response at lower concentrations, saturating at concentrations exceeding 20 ppm. Consequently, highly sensitive gas sensors capable of detecting very low levels can be manufactured, suitable for machine learning applications in environmental monitoring, healthcare, and industrial safety. 

In modern applications such as robotics, autonomous vehicles, and speaker localization, the computational power for sound source localization applications can be limited when other functionalities get more complex. In such application fields, there is a need to maintain high localization accuracy for several sound sources while reducing computational complexity. The array manifold interpolation (AMI) method applied with the Multiple Signal Classification (MUSIC) algorithm enables sound source localization of multiple sources with high accuracy. However, the computational complexity has so far been relatively high. This paper presents a modified AMI for uniform circular array (UCA) that offers reduced computational complexity compared to the original AMI. The complexity reduction is based on the proposed UCA-specific focusing matrix which eliminates the calculation of the Bessel function. The simulation comparison is done with the existing methods of iMUSIC, the Weighted Squared Test of Orthogonality of Projected Subspaces (WS-TOPS), and the original AMI. The experiment result under different scenarios shows that the proposed algorithm outperforms the original AMI method in terms of estimation accuracy and up to a 30% reduction in computation time. An advantage offered by this proposed method is the ability to implement wideband array processing on low-end microprocessors.

Measurements of atmospheric gas concentrations using of NDIR gas sensors requires compensation of ambient pressure variations to achieve reliable result. The extensively used general correction method is based on collecting data for varying pressures for a single reference concentration. This one-dimensional compensation approach is valid for measurements carried out in gas concentrations close to reference concentration but will introduce significant errors for concentrations further away from the calibration point. For applications, requiring high accuracy, collecting, and storing calibration data at several reference concentrations can reduce the error. However, this method will cause higher demands on memory capacity and computational power, which is problematic for cost sensitive applications. We present here an advanced, but practical, algorithm for compensation of environmental pressure variations for relatively low-cost/high resolution NDIR systems. The algorithm consists of a two-dimensional compensation procedure, which widens the valid pressure and concentrations range but with a minimal need to store calibration data, compared to the general one-dimensional compensation method based on a single reference concentration. The implementation of the presented two-dimensional algorithm was verified at two independent concentrations. The results show a reduction in the compensation error from 5.1% and 7.3%, for the one-dimensional method, to −0.02% and 0.83% for the two-dimensional algorithm. In addition, the presented two-dimensional algorithm only requires calibration in four reference gases and the storing of four sets of polynomial coefficients used for calculations. 

 To quickly identify maritime sites polluted by heavy metal contaminants, reductions in the size of instrumentation have made it possible to bring an X-ray fluorescence (XRF) analyzer into the field and in direct contact with various samples. The choice of source-sample-detector geometry plays an important role in minimizing the Compton scattering noise and achieving a better signal-to-noise ratio (SNR) in XRF measurement conditions, especially for analysis of wet sediments. This paper presents the influence of geometrical factors on a prototype, designed for in situ XRF analysis of mercury (Hg) in wet sediments using a ⁵⁷Co excitation source and an X-ray spectrometer. The unique XRF penetrometer prototype has been constructed and tested for maritime wet sediment. The influence on detection efficiency and SNR of various geometrical arrangements have been investigated using the combination of Monte Carlo simulations and laboratory experiments. Instrument calibration was performed for Hg analysis by means of prepared wet sediments with the XRF prototype. The presented results show that it is possible to detect Hg by K-shell emission, thus enabling XRF analysis for underwater sediments. Consequently, the XRF prototype has the potential to be applied as an environmental screening tool for analysis of polluted sediments with relatively high concentrations (e.g., >2880 ppm for Hg), which would benefit in situ monitoring of maritime pollution caused by heavy metals.

A selective algorithm for nondispersive infrared (NDIR) sensing of gases with overlapping absorption spectra was developed and evaluated in modified multichannel NDIR sensor. Measurements in the optic channel with the spectral band where two gas species (target and secondary gas) have overlapping absorption lines are complemented by additional measurements in second channel where spectral absorption for only one gas (secondary gas) is present. The real concentration for the target gas is retrieved by adjusting the absorption data obtained in the overlapping gas spectra's optic channel, with respect to the absorption data retrieved in the second optic channel that has sensitivity only for the secondary gas. An implementation example is performed by obtaining the true concentration of CH4 (as target gas) in a mixture with H2O vapor. The channel for the target gas is equipped by an optic filter with spectra at 3.375 μm where both CH4 and H2O have absorption lines. The complementary second channel provides sensing in spectra at 2.7 μm where only H2O have absorption. Data from a third channel, at 3.95 μm, is used as reference value for 'zero-level' calibration. A calibration procedure was developed and tested, which involves matching of the absorbed light energy in target and secondary channels in humid reference environments. A selective algorithm for sensing of CH4 with elimination of spectral impact from H2O was validated in environments with variable CH4 and H2O concentrations. By implementing the multispectral approach and the developed algorithm, an uncertainties of 5-10 ppm relative the reference concentrations were achieved. For the environments where selective algorithm was validated this should be compared to an uncertainty of 70-90 ppm for the non-corrected CH4 concentration.