Andres Remis

Laura Monge-Bartolomé

Guilhem Boissier

Mounir Waguaf

Jean-Baptiste Rodriguez

Laurent Cerutti

Eric Tournié

Effect of dislocations on the performance of GaSb-based diode lasers grown on Silicon

Silicon photonics is a promising technology for the fabrication of dense photonic chips thanks to the very mature silicon industry. The direct epitaxial growth of III-V lasers on silicon is one of the main challenges for the realization of compact and robust mid-infrared sensors based on photonic integrated circuits. The crystal defects arising from this heteroepitaxial growth affects the laser performance and therefore need to be mitigated, but also studied to better understand their impact on the laser operation. Here, we studied the effect of threading dislocations on laser performance by comparing series of GaSb-based diode lasers grown on native GaSb and Si substrates with different numbers of quantum wells (n QW ) in their active zones. As expected, the laser threshold currents are higher in the case of diode lasers on Si, and they rapidly vary with n QW. Still, the lowest threshold current densities are achieved with n QW = 1 for both substrates. With the help of a theoretical gain model, we attribute these results to the fact that dislocations create non-radiative recombination but do not introduce additional optical losses. This work allows a better understanding of the origin of performance degradation and the decision to be made regarding the heterostructure design.

I. INTRODUCTION

The 2-5 µm mid-infrared (mid-IR) wavelength range presents transparency windows of the atmosphere as well as absorption lines of many biological and chemical species. 1 The demand for compact sensors in this wavelength range has increased significantly over the last decade for medical, industrial, and environmental applications. For now, these sensors are bulky and expensive because they are still composed of discrete devices. Silicon photonics offers the opportunity to reduce cost and footprint by combining the benefits of III-V semiconductors with the very advanced silicon industry. 2,3 The monolithic integration of III-V lasers on silicon appears as the most promising approach for largescale and cost-effective production of such compact and robust sensors. 4,5,6,7 In this context, GaSbbased diode lasers (DLs), which cover the whole 2-3 µm wavelength range [START_REF] Tournié | Mid-Infrared Optoelectronics: Materials, Devices, and Applications[END_REF][START_REF] Belenky | [END_REF] and are perfectly adapted to mid-IR sensing, [START_REF] Bleuel | [END_REF][START_REF] Vizbaras | [END_REF] were recently grown on silicon substrates and demonstrated successful operation. 12 The direct epitaxy of III-Vs on Si, however, leads to a large density of crystal defects such as threading dislocations (TDs) and antiphase boundaries (APBs) caused by the lattice, thermal, and polarity mismatches between III-V materials and silicon. 13 Whereas APBs can be suppressed, 14,15 dislocations are unavoidable, even though their threading density can be reduced through various growth strategies. [START_REF] Calvo | GaSb epitaxy on Si for integrated optoelectronics[END_REF] It is well known that they dramatically affect laser performances by inducing nona eric.tournie@umontpellier.fr This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.

PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5.0135606 radiative recombinations in the active zone, which increases the threshold current density at the expense of the device's lifetime. [START_REF] Liu | [END_REF]18,19,20,21 Still, other aspects remain unclear in the literature where it is often stated that dislocations introduce optical losses as well. 18,20,22,23 In this work, we compared two series of GaSb-based DLs with different numbers of quantum wells (n QW ) in their active zones. One series was grown on native GaSb substrates whereas the other one was grown on Si substrates. Their performances were confronted to a theoretical model which allowed to extract the impact of dislocations on the gain and losses in the laser structures. and photon confinements in the active region. A highly-doped, p-type GaSb contact layer was grown atop all structures. In the case of DLs grown on Si substrates, a 1-µm thick GaSb buffer layer was first grown to suppress APBs. [START_REF] Calvo | GaSb epitaxy on Si for integrated optoelectronics[END_REF] A highly conductive InAs 0.92 Sb 0.08 contact layer was then grown to enable a top-top contact configuration, which avoids driving the current through the highly defective Si/GaSb interface. 22 Finally, an additional 1-µm-thick GaSb buffer layer was inserted between the InAsSb This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.

II. LASER STRUCTURE, GROWTH AND PROCESS

PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5.0135606

surface of the samples grown on Si whereas an upper bound of 9 x10 7 cm -2 is estimated for the threading dislocation density (TDD).

Broad-area and narrow-ridge DLs of different cavity lengths were fabricated. The DLs process starts with the laser ridge definition using standard UV photolithography and inductively coupled plasma etching. The etching was stopped just before reaching the active zone, to minimize current spreading as well as optical losses on the sidewalls. [START_REF] Bartolomé | Toward Mid-Infrared Semiconductor Lasers on Silicon Photonic Integrated Circuits[END_REF] In the case of DLs on Si, a second etching was made to reach the InAs 0.92 Sb 0.08 layer which is serve as the n-contact layer. Si 3 N 4 was then deposited by plasma-enhanced chemical vapor deposition (PECVD) for electrical insulation and sidewall protection. The metallization was then carried out by electron beam evaporation of Ti/Au on the top GaSb p-type layer and the InAs 0.92 Sb 0.08 n-type layer in the case of DLs on Si. In the case of DLs grown on GaSb, Ti/Au was deposited on the top GaSb p-type layer and Pd/AuGeNi on the backside of the GaSb substrate that acts as the n contact. For both series the substrates were thinned down by mechanical polishing to ease the cleavage of laser bars and no special treatment has been done to improve the reflectivity of the cleaved facets. The laser bars were then soldered on Cu heat sinks and measured on a probe station.

III. LASER PROPERTIES

Broad-area DLs (w = 100 µm) were first characterized in pulsed operation at room temperature GaSb substrates exhibit lower threshold current densities than DLs grown on Si, as expected. For both series, the threshold current density increases with the QW number for every cavity length, therefore the best threshold is obtained with the structures having one QW, whatever the substrate. However, the slope of 𝐽 𝑡ℎ =f(n QW ) is much steeper for DLs grown on Si than for those grown on GaSb. As a consequence, we could not reach the threshold current with the DLs fabricated from the 4-QWsstructure grown on Si. Indeed, the extrapolated threshold would be ~2.3 kA/cm 2 , which is beyond the limit of our setup. In contrast, the threshold current density (200 A.cm -2 ) obtained with the 1-QW structure is the best reported so far for GaSb-based DLs grown on Si. 12 Narrow-ridge DLs were then measured in continuous wave (CW) at RT. The L-I-V data for 1-QW DLs grown on Si with an 8-µm wide ridge and different cavity lengths are plotted in fig. 3 a). The turnon voltage is in the 0.7-1 V range and the series resistance is around 4 Ω, comparable to that measured with DLs on GaSb substrates. The threshold currents range between 75 and 120 mA for cavity lengths between 1 and 2.3 mm, which are again the best results ever reported with GaSb-based DLs grown on Si. 12 The evolution of the slope efficiency ( ∆𝑃 ⁄∆𝐼) with the cavity length allows calculating the external quantum efficiency (𝜂 𝑑 ) and, in turn, the evolution with the cavity length of 1/𝜂 𝑑 allows extracting the internal losses 𝛼 𝑖 and the internal quantum efficiency 𝜂 𝑖 using equation ( 1):

1 𝜂 𝑑 = 1 𝜂 𝑖 + 𝛼 𝑖 𝜂 𝑖 ln(𝑅 -1 ) 𝐿 (1)
The internal losses for DLs on Si and on GaSb are plotted in fig. 3 b). The results and the trends are similar for both series. The internal losses increase with the number of QWs, which we ascribe to increasing free carrier absorption and inhomogeneous carrier injection in the multi-QW structures. [START_REF] Rattunde | [END_REF] In contrast, the internal quantum efficiency (not shown) remains constant at around ~ 50% for all structures.

T 0 =91 K T 1 =147 K T 0 =97 K T 1 =117 K 0 2 4

IV. THEORETICAL GAIN MODEL

To analyze these experimental results and to understand the impact of the growth on Si, we modeled the properties of the structures. The threshold current density 𝐽 𝑡ℎ is expressed as a function of the optical losses: 26,27 𝐽 𝑡ℎ = 𝐽 𝑡𝑟 exp (

𝛼 𝑖 + 𝛼 𝐹𝑃 𝑁 𝑤 Γ 𝑤 𝐺 0 ) (2)
where 𝑁 𝑊 is the QW number, Γ 𝑊 is the overlap of the mode with the QWs (Γ 𝑊 =0.015), 27,28 𝐺 0 is the gain parameter, 𝐽 𝑡𝑟 is the transparency current density, 𝛼 𝑖 are the intrinsic internal losses and 𝛼 𝐹𝑃 are the Fabry-Perot cavity losses:

𝛼 𝐹𝑃 = 1 𝐿 ⁄ ln(𝑅 -1 ) ( 3
)
where 𝑅 is the reflectivity of the facets at each end of the Fabry-Perot cavity (𝑅0.3) 27 of length 𝐿.

Besides the values of 𝛼 𝑖 the internal losses, 𝐽 𝑡𝑟 and 𝐺 0 are needed to calculate the threshold current density from equation 2. They can be deduced from the optical gain which is calculated as a function of the energy of the emitted photon 𝐸 and the carrier density 𝑁 for a single QW structure by: 29,28,30,31

𝐿(𝐸) = 1 𝜋 ℏ 𝜏 𝑖𝑛 ⁄ ( ℏ 𝜏 𝑖𝑛 ) 2 + 𝐸 2 (5)
where 𝜏 𝑖𝑛 is the intersubband relaxation time.

The modal gain is then obtained using the following expression: 28,[START_REF] Coldren | Diode Lasers and Photonic Integrated Circuits[END_REF] 𝐺 𝑚𝑜𝑑𝑎𝑙 (𝐸, 𝑁) = 𝑁 𝑊 Γ 𝑊 𝐺 𝑐𝑜𝑛𝑣 (𝐸, 𝑁)

where 𝐺 𝑐𝑜𝑛𝑣 (𝐸, 𝑁) is the gain convoluted with the Lorentzian function.

Afterwards, we calculated the modal gain at the energy of the peak modal gain. Finally, we calculated the modal gain as a function of the current density 𝐺 𝑚𝑜𝑑𝑎𝑙 (𝐽) by substituting 𝑁 by 𝐽 thanks to the following equation:

𝐽 = 𝑞𝑁 𝑤 𝐿 𝑤 𝜂 𝑖 (𝐴𝑁 + 𝐵𝑁 2 + 𝐶𝑁 3 ) (7)
where 𝐿 𝑤 is the QW width, 𝜂 𝑖 is the internal quantum efficiency taken from the experimental results and 𝐴, 𝐵 and 𝐶 are the SRH (Shockley-Read-Hall), radiative and Auger recombination coefficients, respectively. [START_REF] Tournié | Mid-Infrared Optoelectronics: Materials, Devices, and Applications[END_REF] The modal gain as a function of the current density 𝐺 𝑚𝑜𝑑𝑎𝑙 (𝐽) is now known, and the parameters 𝐽 𝑡𝑟 and 𝐺 0 can finally be extracted using the equation below (equation 8), as well as the threshold current density seen above (equation 2).

𝐺 𝑚𝑜𝑑𝑎𝑙 (𝐽) = 𝑁 𝑊 Γ 𝑊 𝐺 0 ln ( 𝐽 𝐽 𝑡𝑟 ) (8)
We fitted these 𝐽 𝑡ℎ values to the experimental values for both series of DLs on GaSb and DLs on Si, by adjusting the SRH coefficient 𝐴, while coefficients 𝐵 and 𝐶 were taken from the literature. 27,28 We used the same 𝐵 and 𝐶 coefficients for both substrates since they are intrinsic to the materials and heterostructures.

This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. on GaSb is excellent. In contrast, for the DLs grown on Si it was not possible to adjust all experimental curves assuming the same 𝐴 SRH coefficient (Fig. 5), which suggests that for these DLs the 𝐴 coefficient increases with the number of QWs. We tentatively ascribe this behavior to the introduction of two new QW/barrier interfaces for every additional QW in the structure. In turn, at such high dislocation densities, misfit dislocation arms propagate at each interface and create additional non-radiative recombinations [START_REF] Groenert | [END_REF]33,34 which is also consistent with the steep variation of the threshold current density with the number of QWs observed for the DLs grown on Si (Fig. 2 b).

PLEASE CITE THIS ARTICLE AS

The final values used to fit all data are in Table I. For the 𝐴 coefficient, the final values for the DLs grown on GaSb are close to those found in the literature 28,27 whereas much higher values have to be used for the DLs grown on Si to account for non-radiative SRH recombination introduced by the dislocations. 35 Coefficient DLs on GaSb DLs on Si 1-4 QWs 1 QW 2 QWs 3 QWs A (s -1 ) 3.9x10 7 1.1x10 8 1.3x10 8 2.4x10 8 B (cm 3 .s -1 ) 4.4x10 -10 4.4x10 -10 4.4x10 -10 4.4x10 -10 C (cm 6 .s -1 ) 2x10 -28 2x10 -28 2x10 -28 2x10 -28

Table I. Recombination coefficient values

Finally, we show in fig. 6 a) the calculated modal gain versus current density curves for all DLs grown on both GaSb and Si substrates. First, we observed that the transparency current density 𝐽 𝑡𝑟 (𝐽 for which the modal gain equals zero) increases with the QW number, as expected. Above transparency, the differential modal gain increases with the number of QWs. 36 For the DLs grown on This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5.0135606

Si, the larger SRH recombinations increase the transparency current and all curves are shifted towards larger current densities whereas the modal gain decreases. This results in an increased laser threshold (equation 2), as also observed experimentally (fig. 2 b)). in the 10 to 20 cm -1 range. [START_REF] Belenky | [END_REF]26,28 If we consider total losses of 10 cm -1 for example, the modal gain equals this value at the laser threshold. The lowest threshold current density is then obtained for a structure having one QW (Fig. 6 a)). In contrast, for total losses of 15 cm -1 , the lowest threshold current density is obtained for a structure with two QWs (Fig. 6 a)). Thus, when the optical losses increase, the optimal QW number switches from one to two QWs at 12.2 cm -1 in the case of DLs on GaSb and at 14 cm -1 in the case of DLs on Si, as it is summarized in fig. 6 b). The fact that these values are very similar (12.2 cm -1 and 14 cm -1 ), and that the optimal QW number for both DLs on Si and on GaSb is the same (one QW), indicates that threading dislocations do not introduce significant optical losses to the structures,

FIG. 1 .

 1 FIG. 1. a) Design of the 2-QWs laser heterostructure grown on Si. b) AFM image of the surface of the same laser.

(

  RT) to assess the threshold current density of the structure. As an example, the light output versus current (L-I) curves of the 1.5-mm long DLs with different QW numbers are plotted in fig. 2 a) for the two series. To get an overview of the whole set of results, Fig. 2 b) shows the evolution of the threshold current density as a function of the QW number, for the different cavity lengths. DLs grown on native

FIG. 2 .

 2 FIG. 2. Broad-area laser characteristics in pulsed operation for the two series. a) light output versus current density curves of 1.5 mm-long DLs with different numbers of QW. b) threshold current density versus the number of QWs for different cavity lengths.

FIG. 3 .

 3 FIG. 3. Narrow-ridge laser characteristics in CW operation. a) L-I-V data for 1 QW DLs grown on Si with an 8 µm-wide ridge and different cavity lengths. The inset shows the evolution of the reciprocal differential quantum efficiency. b) Extracted internal losses versus the number of QWs for the two series.

FIG. 4 . L-I data for 2 -

 42 FIG. 4. L-I data for 2-QWs DLs on Si and GaSb with a 10-µm wide ridge and 1-mm long cavity for different measurement temperatures.

Fig. 5 .

 5 Fig. 5. Fits of the threshold current density for the two series for the 1.5-mm long DLs Fig. 5 shows the experimental and fitted 𝐽 𝑡ℎ values for the two series. The agreement for the DLs grown

FIG. 6 .Fig. 6 a

 66 FIG. 6. The gain model calculations for the two series. a) modal gain versus current density curves for different number of QWs. b) the optimal number of QWs versus the total optical losses.

  in contrast to what is often claim. It confirms also the results shown in fig.3 b) where the extracted internal losses for both DLs on Si and on GaSb are very similar.V. CONCLUSIONTo conclude, we fabricated and characterized two series of GaSb-based DLs grown on GaSb and Si substrates and containing different numbers of QWs in their active zone. We have studied and analyzed the effect of threading dislocations on the performance of DLs on Si by comparing with DLs grown on GaSb. DLs on Si exhibit, as expected, higher thresholds than DLs on GaSb even though the optimum number of QWs is the same in both cases (one QW). The theoretical model used in this work allowed us to understand the gain properties of these structures, and to demonstrate that the performance degradation on Si arises from increased SRH recombinations, without additional optical losses. We have also shown that the SRH recombination coefficient of the DLs grown on Si increases with the number of QWs. This work allows a better understanding of performance degradation, which is important for the decision making regarding the heterostructure design. This allowed us to This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE ASDOI: 10.1063/5.0135606 demonstrate record threshold currents for GaSb DLs grown on Si thanks to a single QW heterostructure.This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5mW/facet) This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5.0135606

  Two sets of four samples containing different numbers of QWs (1  n QW  4) were grown by solidsource molecular-beam epitaxy (MBE) on GaSb and (001)-oriented Si substrates exhibiting a residual miscut of ~0.5° toward [110]. Their active zones, designed to emit near 2.3 µm, are based on 9-nmthick Ga 0.67 In 0.33 As 0.08 Sb 0.88 QWs confined by Al 0.25 Ga 0.75 As 0.02 Sb 0.98 barrier layers and are embedded within Al 0.25 Ga 0.75 As 0.02 Sb 0.98 waveguides. 8 Two n-and p-type Al 0.9 Ga 0.1 As 0.08 Sb 0.92 layers ensure carrier

CITE THIS ARTICLE AS DOI: 10.1063/5.0135606 so

  This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset.PLEASE -called characteristic temperatures 𝑇 0 and 𝑇 1 , which represent the sensitivity to the temperature of the laser threshold current and the external quantum efficiency, respectively. 𝑇 0 is similar on both substrates (91 and 97 K) whereas T 1 is lower for DLs on Si (117 K vs. 147 K). Similar trends have been observed for all QW numbers. The lower T 1 for DLs on Si than on GaSb reflects the fact that the external quantum efficiency is sensitive to temperature due to the high TDD.

		20°C 30°C	2 QWs -CW
		40°C	L=1 mm
		50°C 60°C	w=10 µm
		70°C
		Si
		GaSb
	0	50	100	150	200	250	300	350	400
				Current (mA)

  𝑓 𝑐 (𝐸, 𝑁) and 𝑓 𝑣 (𝐸, 𝑁) are the Fermi functions in the conduction band and in the valence band respectively, 𝑀 𝑡 is the dipole matrix element and 𝑛 is the real part of the refractive index. The gain broadening caused by the intraband scattering is taken into account in the model by convoluting the calculated gain by a Lorentzian function (5):28,[START_REF] Coldren | Diode Lasers and Photonic Integrated Circuits[END_REF]

𝑔(𝐸, 𝑁) = 𝜋𝑒 2 ℏ𝑀 𝑡 2 𝜀 0 𝑛𝑐𝑚 0 𝐸 𝜌(𝐸)(𝑓 𝑐 (𝐸, 𝑁) -𝑓 𝑣 (𝐸, 𝑁)) (4) This is the author's peer reviewed, accepted manuscript. However, the online version of record will be different from this version once it has been copyedited and typeset. PLEASE CITE THIS ARTICLE AS DOI: 10.1063/5.0135606

where 𝜌(𝐸) is the density of states,

Acknowledgements

Part of the work was supported by the French ANR (LightUp, ANR-19-CE24-0002), and by the French Program on "Investments for the Future" (Equipex EXTRA, ANR-11-EQPX-0016).

REFERENCES:


N Thierry-Jebali
email: nicolas.thierry-jebali@insa-lyon.fr

Mihai Lazar

A Vo-Ha

D Carole

V Soulière

A Henry

Dominique Planson

G Ferro

P Brosselard
email: pierre.brosselard@insa-lyon.fr

G Regula

C Brylinski

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Influence of process parameters on electrical properties of PiN diodes fabricated with a highly p-type doped layer selectively grown by VLS transport

Influence of process parameters on electrical properties of PiN diodes fabricated with a highly p-type doped layer selectively grown by VLS transport.

The quality of 4H-SiC bulk and epilayer material is day after day improving and several unipolar devices such as Schottky and JBS rectifiers, JFETs and MOSFETs are commercially available. Nevertheless, some technological barriers still persist like the local and in depth doping into the SiC top epilayer. Indeed, a defect-free area with high doping level is very difficult to obtain by ion implantation. Furthermore, the depth of the implanted area is limited to several hundred of nanometers using standard ion implanters. SiC Selective Epitaxial Growth (SEG) by Vapor-Liquid-Solid (VLS) transport on a bowl-shaped geometry appears to be a promising solution to perform deep, highly doped and high quality p-type doped area. Such SEG-VLS growth of highly p-doped (> 5x10 19 cm -3 ) SiC layer was successfully demonstrated recently on large and small areas fabricated by Reactive Ion Eching (RIE) [1]. Moreover, a high quality P ++ (VLS)-N junction can be achieved by using this technique that offer new prospects for the achievement of new power electronics devices, including deeply buried peripheral protection zones such as guard-rings or JBS structures [2].

This work presents the influence of some process parameters and crystal orientation on both forward and reverse characteristics of PiN diodes composed of p ++ -layers emitter and guard-rings peripheral protection fabricated by SEG-VLS. Fig. 1 reports a picture of a typical processed PiN diode. Spacing between two adjacent guard-rings has been optimized by Sentaurus TCAD finite elements simulation. Simulated breakdown voltages of a diode without and with optimized 3 guardrings peripheral protection are Vbr ~ 500 V and Vbr ~ 1200 V respectively. Different configurations of guard-ring spacing were tested on the fabricated devices and will be detailed in the final paper.

The processed samples were composed of a 15 µm thick, Nd=5x10 15 cm -3 doped n -layer, grown in a hot-wall CVD reactor on commercial n + 4H-SiC wafers. The Emitter and guard-ring have been defined together by RIE. Then an Al(1.5 µm)/Si(1.1 µm) stacking were deposited and selectively etched in order to fill the patterns only. After performing the VLS growth as described in [1], the 500 nm Si02 passivation layer was deposited by PECVD. Finally cathode and anode ohmic contacts were formed by annealing e-beam deposited Ni based and Ni/Ti/Al based metallic stacking. The final result is showed in fig. 1 and the targeted structure is described in fig. 2. The detail of process variations is reported on Table I.

Fig. 3 reports forward current-voltage characteristics recorded at various temperatures of three diodes fabricated with different processes. One can see that the static forward behaviors of diodes change dramatically as a function of the process. For example, "process A" diodes have a Schottky like behavior, "process B" diodes have a bipolar behavior and "process C" diodes exhibits both Schottky and bipolar characteristics in the same time.

Changes on the reverse characteristics has also been observed and, in particular, on the breakdown voltage (Vbr). For example a maximum of Vbr=900 V is observed for "process A" diodes when a maximum of Vbr=100 V has been observed for "process B" diodes. Since the maximum Vbr value of "process A" diodes is higher than simulated Vbr without guard-rings, we can conclude that p ++ SEG-VLS layers are really promising for the fabrication of deep guard-rings peripheral protection.

In order to explain the large variation of Vbr and forward static electrical properties, we performed active degradation characterizations (AD), Photo-Emission Microscopy (PEM) and Transmission Electron Microscopy Observations (TEM). Fig. 4 reports TEM pictures of the VLS layer and demonstrates that some processes generate a lot of defects and poly-typism ("Process D") while some generate almost defect free layers ("process E"). These differences directly affect the properties of the diode.

In the final paper, we will correlate electrical measurements to TEM and PEM observations in order to model the diode behavior and propose improvements of the VLS growth.

[1] D. Carole et al, Materials Science Forum 717-720, 169 (2012) [2] N. Thierry-Jebali et al, Material Science Forum, 740-742, 911 (2013)

Fig. 2 .

 2 Fig. 2. Cross section of the fabricated diodes..

Fig. 3 .

 3 Fig. 3. Forward J-V behavior at 50 °C, 150 °C and 250 °C of 3 different diodes

Table I .

 I Process variations of fabricated diodes

	Process name	A	B	C	D	E
	Substrate orientation		On-axis		8°-off
	Al/Si deposition technique		e-beam	sputtering	e-beam
	Post VLS annealing	No	1700°C, Ar, 30 min	No 1700°C, Ar, 30 min
	Fig. 1. Optical image of a
	typical processed PiN diode


Frédéric Joly

Philip Roche

Jo Dewulf

Lieselot Boone

Differences in LCA impact between meat types are reduced when ecosystem services related to their production are accounted for


Francois Lestremau

Alexandre Levesque

Abdelmoughit Lahssini

Tanguy Magnan De Bornier

Romain Laurans

Azziz Assoumani

Hugues Biaudet

Development and Implementation of Automated Qualification Processes for the Identification of Pollutants in an Aquatic Environment from High-Resolution Mass Spectrometric Nontarget Screening Data

Keywords: suspect screening, LC/HRMS, machine learning, identification, contaminants, water

Environmental pollution monitoring represents a major challenge due to the growing presence of a large and diverse number of potential contaminants. In complement to target analysis, nontarget analysis, via liquid chromatography (LC) coupled to high-resolution mass spectrometry (HRMS), is increasingly used to provide a more comprehensive characterization of pollution. The challenge associated with this type of analysis is particularly related to the data treatment for substance identification. One of the main limitations is that all data must be manually reviewed, which is tedious and time-consuming. Machine learning algorithms aim to reproduce human behavior, and their capabilities were therefore evaluated to automatically identify substances in suspect screening approaches. After selecting the relevant features produced from LC/HRMS, seven different machine learning models were evaluated for each of the three different databases, which resulted in the selection of logistic regression (LR) and random forest (RF)-based algorithms. An interface was built to rank the identified substances and to assess the performance of the developed models. The LR model provided the best results when retention times were available. The developed LR and RF models were determined complementarily, particularly when no retention times were available. However, limitations were noticed when using a database containing different HRMS technologies.

■ INTRODUCTION

With the improvement of capabilities of the high-resolution mass spectrometer, nontarget screening, which aims to provide a more representative view of the presence of organic substances in samples, has been a growing field in the past few decades in the characterization of environmental pollution. [START_REF] Hollender | High Resolution Mass Spectrometry-Based Non-Target Screening Can Support Regulatory Environmental Monitoring and Chemicals Management[END_REF][START_REF] Hollender | Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?[END_REF] Coupled to gas or liquid chromatography, nontarget screening strategies were first originally applied to metabolomics studies. [START_REF] Hollywood | Metabolomics: Current Technologies and Future Trends[END_REF][START_REF] Antignac | Mass Spectrometry-Based Metabolomics Applied to the Chemical Safety of Food[END_REF][START_REF] Sangster | A Pragmatic and Readily Implemented Quality Control Strategy for HPLC-MS and GC-MS-Based Metabonomic Analysis[END_REF][START_REF] Theodoridis | LC-MS-Based Methodology for Global Metabolite Profiling in Metabonomics/ Metabolomics[END_REF] It has been extended to many fields of environmental studies to characterize pollutants, for instance, in atmospheric, soil, or water matrices. [START_REF] Ibánẽz | Rapid Non-Target Screening of Organic Pollutants in Water by Ultraperformance Liquid Chromatography Coupled to Time-of-Light Mass Spectrometry[END_REF][START_REF] Díaz | Target and Non-Target Screening Strategies for Organic Contaminants, Residues and Illicit Substances in Food, Environmental and Human Biological Samples by UHPLC-QTOF-MS[END_REF] Different categories can be distinguished for nontarget screening, being used for fingerprint pattern comparison of data samples from different days/sites or for aiming at the identification of detected substances. [START_REF] Krauss | LC-High Resolution MS in Environmental Analysis: From Target Screening to the Identification of Unknowns[END_REF][START_REF] Bletsou | Targeted and Non-Targeted Liquid Chromatography-Mass Spectrometric Workflows for Identification of Transformation Products of Emerging Pollutants in the Aquatic Environment[END_REF] Identification of substances detected by nontarget screening approaches can therefore be performed using mass spectra databases in a socalled "suspect screening". [START_REF] Gago-Ferrero | Suspect Screening and Regulatory Databases: A Powerful Combination to Identify Emerging Micropollutants[END_REF] The constitution of databases is critical to ensure that a large number of substances can be accurately identified. Laboratories that developed their own databases can generally inject only a limited amount of analytical standards, between a hundred up to a thousand, essentially due to the cost limitation of purchasing them. To overcome this limitation, databases from external sources can be employed. Manufacturers therefore provide spectral databases, which can contain more than thousands of substances. Collaborative databases have also been developed by the scientific community. MassBank, [START_REF] Horai | A Public Repository for Sharing Mass Spectral Data for Life Sciences[END_REF][START_REF] Massbank | MassBank Europe Mass Spectral DataBase[END_REF] for instance, contains overall up to 70,000 spectra from various types of instrumentations and acquisition modes provided by diverse voluntary contributor organisms. However, differences in fragmentation patterns, in the number of fragments, and in their relative intensities can occur between these different types of instruments/vendors according to the technologies set up even if several papers have reported that comparability between QTOF spectra and Orbitrap spectra was providing a good correlation. [START_REF] Oberacher | A European Proposal for Quality Control and Quality Assurance of Tandem Mass Spectral Libraries[END_REF][START_REF] Oberacher | Annotating Nontargeted LC-HRMS/ MS Data with Two Complementary Tandem Mass Spectral Libraries[END_REF] For data treatment, dedicated software are used to automate the substance identification (either from vendors or from independent groups, for instance, EnviMass, 16 MS DIAL, [START_REF] Tsugawa | DIAL: Data-Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis[END_REF] or patRoon [START_REF] Helmus | Open Source Software Platform for Environmental Mass Spectrometry Based Non-Target Screening[END_REF] ). Different strategies have been developed depending on the experimental data (molecular ion, retention time, isotopic profile, fragmentation data) to provide assistance to the user for substance identification. However, as there is a huge amount of data to take into account and most of them can suffer from more or less pronounced interferences, it is common practice that experts manually validate all substance identifications since the risk of false-positives can be large. All proposed identifications have therefore to be reviewed, which is time-consuming and represents a tedious task. This task is also likely to become even more time-consuming as databases are expected to grow and the number of substances to be identified will therefore increase. This will lead to a completely unacceptable amount of time dedicated to data analysis. An additional downside is that the validation depends on the experience and view of the expert who carries on this process. Therefore, the validation procedure must be carefully defined to obtain a reproducible process among different experts.

Artificial intelligence/machine learning approaches are increasingly used to improve mass spectrometry-based data treatment due to major improvements in recent years in computing capabilities. Therefore, for example, metabolomic communities have explored capabilities of using machine learning to highlight biomarkers of diseases. [START_REF] Giordano | Rapid Automated Diagnosis of Primary Hepatic Tumour by Mass Spectrometry and Artificial Intelligence[END_REF][START_REF] Ledesma | Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery[END_REF] Deep learning methods have also been investigated for example to improve peak picking [START_REF] Chetnik | A Machine Learning-Based Classifier for Reduced False Positive Peak Detection in Untargeted LC-MS Metabolomics Data[END_REF] or mass spectra comparison. 22 This study was therefore dedicated to evaluating the use of artificial intelligence to develop a scoring system that can provide improved assistance to users for the verification of substance identification in the suspect screening approach. It ideally aims to provide a reproducible and automated validation process without the need of a manual review. The study focused particularly on water sample contaminants. Various parameters were considered for the development of the scoring system with a focus on processing dataindependent analysis results. Two main categories of databases were considered to develop a dedicated data treatment algorithm: a first one containing data generated with the same type of instruments and a second one constituted from different types of instruments. The obtained algorithms were applied on a test data set to establish the degree of confidence of the scoring system and the possibility of automating substance identification.

■ EXPERIMENTAL SECTION Data Sources. Data Samples. Data were obtained from the analysis of surface and wastewater samples from different sites across France as part of a national French monitoring campaign. 23 All details about the samples and their extraction procedure are described in this report. The samples extracted onto the solid phase (SPE) were analyzed using an LC/QTOF 6550 system (Agilent technologies). Chromatographic analysis used a Zorbax Aq column (150 mm × 2.1 mm, 1.8 μm) (Agilent technologies) with 1 mM ammonium acetate and methanol as mobile phases. Detailed chromatographic conditions are presented in SI 1 in Table SI 1. All samples' data were produced in the Agilent format (.d) and converted in the mzML format using MS convert software (Proteowizard [START_REF] Adusumilli | Data Conversion with ProteoWizard MsConvert[END_REF] ).

Data Acquisition Mode. Analysis in the data-independent mode was preferred to provide an exhaustive sample characterization since it has the advantage that all ionized substances that reach the collision cell are fragmented. Two collision energies, 20 and 40 eV, were systematically acquired in positive and negative modes for all data and included in the process of the scoring model.

Databases. Three databases were used in this work.

• A database created by our laboratory from the injection of 207 standards in the positive mode (POS) and 80 in the negative mode (NEG) in the same analytical conditions as used for the sample analyses. Data Treatment. All data were treated using a developed in-house interface used to extract the relevant features and display the results generated by the machine learning approach (classification scores). Details on the data treatment are provided in SI 3. General information about the treatment of nontarget screening data and classification aspects have been provided by Fisher et al. [START_REF] Fisher | Approaches for assessing performance of high-resolution mass spectrometry-based non-targeted analysis methods[END_REF] and can help facilitate the comprehension of the results of this study.

Classification Problem and Machine Learning Approach. Suspect screening relies on looking for a large number of substances from a reference database into an experimental sample. For each individual substance investigated, the challenge is to certify if it is present or not in the sample, which practically is being able to identify the substance or not. This constitutes a binary classification problem, which can be solved using different approaches. One particular method is to use machine learning based on common properties, so-called features, to provide an answer to this challenge with a confidence value associated. Many machine learning algorithms offer binary classification. One of the critical aspects of this study was to select the most relevant features and model to achieve an optimum classification.

Features Selected for Scoring System Evaluation. Feature Overview. To develop the algorithm and rank the probability of substance identification, a scoring system was developed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) data contain different types of information generated from chromatography, the MS TIC scan, or the MS fragmentation pattern. Therefore, the following features, considered the most significant, were selected for developing the scoring system (Table 1).

As more variability was expected when comparing with a database including multiple vendor spectra (MassBank data), additional features were specifically included for this study (Study C).

Details about the choice and the description of each feature are provided in SI 3.

Construction of the Database Used for the Training Phase.

A training phase was carried out to develop the scoring model. Representative experimental samples were manually characterized and labeled. For this set of substances, the selected features were built in comparison with the considered reference databases, and various algorithms were trained. The training data were built so that the two classes, substance is present/is not present in the experimental sample, were equally populated to avoid any bias.

Substances Considered for the Training. Twenty SPE extracts of surface water samples from a national French monitoring campaign were considered as case studies to train the model (results for this study have been previously published in a report 23 ). The data of these SPE extracts were treated with vendor software, and the same two databases (laboratory and vendor) were used in this study. Using the vendor database in both cases allowed one to also consider a larger number of substances and to include situations where no retention times were available. For the classification of the common approach, an adaptation of the Schymanski scale 26 was used. Substances for which retention times were matching and at least or more than one fragment were determined were classified as level 1. Substances without retention times but for which at least three fragments were determined were classified as level 2. To minimize the rate of false-positives, only these two levels of classification, and therefore the corresponding substances, were used to train the model. All results were manually inspected with Agilent vendor software (Masshunter) and validated using the criteria described previously. Although this approach is not fully based on matching with retention times of analytical standards (when they were not used) and can therefore represent a larger risk to include some falsepositives, this choice of using real sample data over using injected analytical standards was preferred since it could provide more representative samples and a different level of intensities, particularly in the case of data-independent analysis where a larger noise is produced from the various sample matrices for instance. This mode of analysis was selected particularly since all ionized substances from the analyzed samples produce fragmentation data and thereby a higher level of interference, leading to a more challenging data interpretation.

Overall and across all 20 samples in positive and negative ionization modes and in the data-independent analysis mode, approximately 1200 substances (600 for each category) were selected for the training data set (including many identical substances found in the different samples with different abundances and impacted with different matrix effects).

Substances Not Present Considered for the Training. To be able to train the algorithm, a category where molecules are not present in the samples had to be defined. It had to represent roughly the same size in number as the category considered for the molecules present in the samples. One approach could have been to consider substances in the databases that had not been determined in the samples. However, this approach can potentially highlight substances that were not identified using the common laboratory approach (with vendor software) but that were in fact present in the sample (false-negative). Therefore, instead of considering all of the molecules of the databases not identified, another approach was preferred. To generate a set of molecules classified as not present in the sample, it was chosen to label the peaks with an identical molecular ion but with retention times different from those of the molecules previously identified in the samples. Development and Test of the Different Algorithms. Machine Learning Models Considered. The Python Package scikit-learn for machine learning models was used to tackle the data classification. [START_REF] Pedregosa | Scikit-Learn: Machine Learning in Python[END_REF] The following eight classification models implemented within this package were tested: decision tree/ random forest (RF), KNN classifier, MLP classifier, logistic regression (LR), Bayes classifier, and SVM classifier (some comparison elements can be found elsewhere [START_REF] Bahel | In A Comparative Study on Various Binary Classification Algorithms and Their Improved Variant for Optimal Performance[END_REF] ).

Evaluation of the Models. For the evaluation phase, the same approach was used for all evaluated algorithms to provide a fair comparison between them. Thus, the labeled data set was divided into 80% dedicated to training (i.e., the training set) and 20% dedicated to testing (i.e., the test set). The different trainings were performed with a cross-validation (k-fold = 10) for which the training data were shuffled and split into 10 random subsets to perform small independent trainings. This method usually results in less biased or less optimistic estimates of the classifier than a simple train/test split. The same standard measures were used to evaluate the models. It was based on the confusion matrices of the models evaluated on the test set. The numbers of correct and incorrect predictions are summarized and broken down into each class with count values.

Evaluation of Performance of the Developed Models with the Test Set. The models were evaluated on a test set, which comprised data from four different surface water samples generated in the same analytical conditions, and that was independent of the training set used for the devolvement of the algorithm. All data were reviewed manually and classified according to the Schymanski scale [START_REF] Schymanski | Identifying Small Molecules via High Resolution Mass Spectrometry: Communicating Confidence[END_REF] adapted in level 1 and level 2 categories. Correlation between manual classification and scoring obtained for each model was then performed.

For the three studies performed and the models selected (based with and without retention times on QTOF data and on MassBank data�see the results part), the scoring obtained for each model whatever the data processing was calculated. For example, even if the data treatment was performed with the laboratory database, the "confidence" scores were also calculated with the model developed without considering the retention times (based also on logistic regression) and with the model developed from the MassBank Database (based on RF and using more features). That provided additional information that could be used to compare the different models. That was particularly relevant for the model without using the retention times, which has been selected based on logistic regression although during the development, RF was providing better results.

■ RESULTS AND DISCUSSION

Development of the Scoring Model with the Same HRMS Technology. Mass spectrometry data and particularly fragmentation data can be influenced by the technology (and experimental conditions) used. Therefore, to minimize the number of factors having an impact on the variability of the data produced, only data produced by an Agilent QTOF system were initially considered. The first part of this work was therefore dedicated to evaluating an automated scoring system using a unique instrumentation type. Two cases were considered: when the retention times in the database were available (Study A), usually using a laboratory database; and when a vendor database is used therefore without using the retention times feature (Study B). Eight machine learning models were evaluated in each case.

Study Using Retention Times (Study A). The best performance could be observed with the "Random forest" (RF) algorithm with an accuracy of 96% (Table 2). Even if the performances obtained were slightly lower (95% accuracy), it was preferred to select the "logistic regression" (LR) algorithm. Indeed, when a visual inspection of the results with high scores determined with the RF model was performed, some inconsistencies were observed on certain substances, and moreover, the logistic regression model was more understandable to rank the substance scores based on their confidence level. The distribution of the different parameters according to their classification is displayed in Figure SI 1, and the coefficient obtained per considered parameter is shown in Table SI 4. To visualize the separation of the two classes with the chosen algorithm, Figure SI 3 displays a PCA separating the zones of the two classes 0 (in red) and 1 (in blue) created by the algorithm. It can be noticed that both sets of data are clearly separated. The confusion matrix performed on the test set (Figure 1A) also demonstrated the good performances of the developed model. No false-negatives were noticed on the training set with an acceptable limited number of falsepositives. This result was determined acceptable since no suspected substances would be missed when processing the data and a manual double-check of the remaining doubtful cases can be manageable. A robust probability score, obtained with the selected LR model, enabled a ranking of the substances based on the confidence of their presence (1 being the highest).

Study Not Using Retention Times (Study B).

As for the study using retention times, the best performance could be observed with the RF model but with a lower accuracy at 92% (Table SI 5). To be in accordance and for the same reason as for the choice of study A, the LR model, for which a lower accuracy of 0.81 was determined, was finally preferred.

To visualize the separation of the two classes with the LR model, Figure SI 6 displays a PCA separating the zones of the two classes 0 (in red) and 1 (in blue) defined by the algorithm. While both sets of data are correctly separated, it is not as clear as to what was obtained for the study using the retention times. This is also reflected by the confusion matrix performed on the test set (Figure 1B), which also displayed lower performances than for the other developed model. The retention times are indeed strong indicators in the identification process of a substance and bring a superior level of confidence than when not used or available in the database. There is therefore more uncertainty in the identification process when a model without considering this parameter is used.

Evaluation with a Database Using Multiple HRMS Technologies (Study C). The second part of the work was dedicated to evaluating if the same approach could be potentially used with a database including data produced from a large panel of technologies/vendors. The MassBank database was therefore used. Data were curated and homogenized, and then, a ranking was performed to select only one entry per substance considered. Additional features were also included to reflect the expected increase in variabilities in the reference spectra from multiple vendors (see Experimental Section, Table 1).

Development, Training, and Comparison of Different Algorithms. The eight classification models were tested on MassBank data. The same set of sample data as the one used for the first part was also selected. Therefore, only the substances that were common between MassBank and the substances determined in the samples using the standard laboratory procedure were considered, which represented overall 1220 substances. Since more variability was expected, the pool of samples was extended with seven wastewater samples also previously characterized by the laboratory. Standard solutions of pesticides and pharmaceuticals injected at three different concentrations, low, middle, and high, were also used to have known substances at different levels of intensities. Overall, 2155 different substances were used instead of 1200 in the first phase of the project.

The same approach as for the first part was set up, with two categories defined, which corresponded to the presence of the substance in the sample (label 1) and when the substance was not present (label 0).

Model Developed (Model C). Average performances for each of the eight models are presented in Table SI 6, with the best performance observed for the RF algorithm reaching an accuracy of 94%. This model was therefore selected. The confusion matrix performed on the test set (Figure 1C) indicated a robust classification with a very low rate of falsepositives and false-negatives.

An overall summary of the three investigated studies (Studies A, B, and C) is presented in Table SI 7.

Evaluation of the Performances of the Developed Models. Four data sets from river sample analyses were processed with the three different models to reproduce the approach of assessing a confidence level for suspect analysis (LR with retention times�Study A; LR without retention times�Study B; and RF�Study C)/database (laboratory, vendor, and MassBank). This process provided a score for every substance matching with a database entry. All data were also manually reviewed and classified according to the Schymanski scale 32 . The scoring obtained with the machine learning process was then compared with the manual review.

It can also be pointed out that while for each database used, a specific model was selected (for instance, an LR data treatment performed for Study A with the laboratory database, for instance), the scores based on LR and RF models were anyway systematically calculated. That feature was particularly useful considering Study C based on RF and including more parameters than for Studies A and B when data treatments were performed on the LR model.

Comparison of Performances Depending on Databases. With the threshold of scoring set at LR ≥ 0.8 (out of a maximum scoring of 1), comparable results could be obtained with Studies A and B regarding the percentage of substances that could be assessed with the highest level of confidence (Figure SI 9). For the positive ionization mode (POS), 86% of the substances were classified as level 1 using the laboratory database, while 84% were set at level 2 with the vendor database. For the negative mode (NEG), 74% of the highest confidence level was determined in both cases. The negative mode produces generally less fragments than the positive mode, therefore leading to less confidence for substances with a low number of fragments.

Through the evaluation of the test results, it was determined that the RF model could be complementary to the use of the LR model. When using the LR model with retention times (Study A), RF did not bring any additional value since retention time provides a sufficiently strong feature. For the LR model developed without retention times (Study B), it was noticed that some relevant substances (level 2) had a score <0.8. In this case (Study B), the RF model (which includes additional features) could provide 20 more substances in addition to the LR model (note that using only the RF model was not as efficient as using only the LR model).

The RF model (with the score set at ≥0.85) presented however some limitations when used with the MassBank database (Study C) with the percentage of substances set at level 2 at 57% in POS and 44% in NEG. The MassBank database used presented many substances in POS and particularly in NEG with fragments of low mass (and therefore poorly selective), particularly at 20 eV.

Evaluation of an Automated Validation of Substance Identification. An automated validation process for substance identification was evaluated by defining a scoring threshold depending on the ionization mode and models/databases used (Figure 2). As highlighted previously, the RF model seemed complementary to the LR model. In the positive mode, for the match obtained for both the laboratory and vendor databases, setting the LR thresholds at, respectively, ≥0.88 and 0.89 while ruling out substances with RF < 0.5 allowed one to automatically classify at their highest level of confidence (level 1 for the laboratory database, example of acetaminophen in Figure S10; and level 2 for the vendor database, example of clarithromycin and diltiazem in Figures S11 andS12) all of the substances falling in these settings (representing about 88% of the substances above the set RF threshold). The remaining substances would then have to be evaluated by an expert review.

For the negative mode, the same features (with thresholds LR ≥ 0.90 and RF < 0.5) could be used with the laboratory database with the validation of all substances with these settings (representing 88% of the substances with LR ≥ 0.90).

The same NEG threshold of the LR score at 0.9 was determined for the vendor database. However, using the RF score <0.5 would not work out. It was however noticed that if the feature "number of fragments at 20 eV" was used and the number of fragments >2 was selected, all substances falling in this category were level 2, representing 62% of the 94 substances with the LR score ≥ 0.90. This parameter was not as selective as the RF factor and led therefore to a lower percentage of substances that could be automatically qualified. Substances analyzed in the negative mode do not fragment as much as those analyzed in the positive mode, particularly at 20 eV, which can be considered the most informative fragmentation level. However, even if fragmentation at 40 eV particularly in data-independent analysis produced more interferences, using a secondary collision proved to be complementary to only using one level of fragmentation.

For the RF model developed with the MassBank database, no rules could be determined for automated identification with either POS or NEG modes.

■ CONCLUSIONS

A machine-learning-based approach has been developed to provide better guidance and aiming at an automated validation process for the identification of substances in the suspect LC HRMS mode. For the first part of the study, using the same HRMS technology, eight classification models were evaluated, and the logistic regression model was selected, which provided robust scoring ranking of identified substances with or without using the retention time feature. In a second part, the study was then continued with the use of an open-source reference database including data from different HRMS technologies. Additional features were included to consider for the larger MS spectra variability, and the random forest model was considered the most efficient. However, this approach was determined to present limitations for a database that comprises data from different HRMS technologies, particularly for substances presenting a low fragmentation MS pattern, which prevented a possible automated validation process.

Overall, this study demonstrates the potential of using machine learning approaches to facilitate the data treatment process and constitutes a first step for an automated process for the identification of environmental substances in LC/ HRMS data.

The best approach was determined using both logistic regression and random forest, so further work could include a model combining both algorithms. Other improvements could also be considered with additional features such as intensity, a better integration of substances with low MS fragmentation, or using a predictive model for retention times or MS fragmentation data. Moreover, application on larger data sets, including spiked samples with analytical standards, will provide better refinement on the boundary and robustness of the developed approaches. Machine learning approaches can also be used to implement a simplified scoring model.

  the experimental fragmentation spectrum and the theoretical one at 20 eV A,B,C MassBank_sim_20 MassBank similarity between the experimental fragmentation spectrum and the theoretical one at 20 eV A,B,C nb_matched_peaks_20 number of matched peaks between the experimental fragmentation spectrum and the theoretical one at 20 eV only C ratio_peaks_found_20 ratio of peaks matched at 20 eV (= nb_matched_peaks_20/total_nb_peaks_20) A,B,C nb_high_peaks_20 number of experimental fragments at 20 eV with a mass >100 only C error_sum_20 cumulated sum of errors in mass peak by peak between the experimental fragmentation spectrum and the theoretical one at 20 eV A,B,C fragmentation at 40 eV cos_sim_40 cosine similarity between the experimental fragmentation spectrum and the theoretical one at 40 eV A,B,C MassBank_sim_40 MassBank similarity between the experimental fragmentation spectrum and the theoretical one at 40 eV A,B,C nb_matched_peaks_40 number of matched peaks between the experimental fragmentation spectrum and the theoretical one at 40 eV only C ratio_peaks_found_20 ratio of peaks matched at 40 eV (= nb_matched_peaks_40/total_nb_peaks_40) A,B,C nb_high_peaks_20 number of experimental fragments at 40 eV with a mass >100 only C error_sum_40 cumulated sum of errors in mass peak by peak between the experimental fragmentation spectrum and the theoretical one at 40 eV A,B,C isotopic profile cos_sim_isotopic cosine similarity between the experimental isotopic spectrum and the theoretical one A,B,C nb_matched_peaks_isotopic number of matched peaks between the experimental isotopic spectrum and the theoretical one at 20 eV A,B,C other considered features tic_flag 1 if apex intensity is higher than an intensity defined by expert; else 0 A,B,C

Figure 1 .

 1 Figure 1. Confusion matrix on the selected model using (A) laboratory database (Study A), (B) vendor database (Study B), and (C) MassBank database (Study C).

Figure 2 .

 2 Figure 2. Performances with the highest possible confidence levels obtained depending on the model/databases used and the ionization modes (LR, logistic regression; RF, random forest; rt, retention time; L1/L2, level 1/2; in bracket, number of substances for each category).

Table 1 .

 1 Summary of All Features Considered for the Development of the Scoring System

Table 2 .

 2 Performances Obtained with Different Classifiers for Study A

		model	fitting time	scoring time	accuracy	precision	recall	F1_score
	5	random forests	0.324726	0.030378	0.965347	0.966728	0.965054	0.965296
	4	quadratic discriminant analyses	0.008564	0.008156	0.961425	0.963795	0.960994	0.961312
	0	logistic regression	0.018897	0.011027	0.953808	0.959158	0.953066	0.953484
	2	support vector machines	0.019733	0.009340	0.953808	0.960903	0.952846	0.953399
	1	decision tree	0.010815	0.008656	0.953771	0.955632	0.953510	0.953699
	6	K-nearest neighbors	0.005651	0.013044	0.946116	0.954928	0.945000	0.945545
	7	Bayes	0.007140	0.007359	0.934351	0.937782	0.935157	0.934185
	3	linear discriminant analyses	0.010990	0.009642	0.924811	0.928917	0.924048	0.924512

29 ■ AUTHOR INFORMATION Corresponding Author Francois

  Lestremau -Hydrosciences Montpellier, Univ Montpellier, IMT Mines Ales, IRD, CNRS, Ales 30100, France; orcid.org/0000-0002-0959-3477; Email: Francois.lestremau@mines-ales.fr SIA Partners, Paris 75000, France Abdelmoughit Lahssini -SIA Partners, Paris 75000, France Tanguy Magnan de Bornier -SIA Partners, Paris 75000, France Romain Laurans -SIA Partners, Paris 75000, France Azziz Assoumani -Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte 60550, France; orcid.org/0000-0002-3774-659X Hugues Biaudet -Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte 60550, France

Authors

Alexandre Levesque -

Author Contributions

F.L.: writing original draft preparation, formal analysis, data contributor, method validation, review and editing.

Notes

The authors declare no competing financial interest.

■ ACKNOWLEDGMENTS

This work was funded as part of "AMI Intelligence Artificielle" by DITP and DINSIC and from the "France Relance" project by DINUM. The authors acknowledge Agilent for proving the vendor database in the sdf format and authorizing its use for the scope of this project.